Tetrahymena thermophila, a classic ciliate model organism, has been shown to possess tubular mitochondrial cristae and highly divergent electron transport chain involving four transmembrane protein complexes (I–IV). Here we report cryo-EM structures of its ~8 MDa megacomplex IV2 + (I + III2 + II)2, as well as a ~ 10.6 MDa megacomplex (IV2 + I + III2 + II)2 at lower resolution. In megacomplex IV2 + (I + III2 + II)2, each CIV2 protomer associates one copy of supercomplex I + III2 and one copy of CII, forming a half ring-shaped architecture that adapts to the membrane curvature of mitochondrial cristae. Megacomplex (IV2 + I + III2 + II)2 defines the relative position between neighbouring half rings and maintains the proximity between CIV2 and CIII2 cytochrome c binding sites. Our findings expand the current understanding of divergence in eukaryotic electron transport chain organization and how it is related to mitochondrial morphology. Tetrahymena thermophila possesses tubular mitochondrial cristae and a highly divergent electron transport chain. Here the authors report cryo-EM structures of its half ring-shaped ~8 MDa megacomplex IV2 + (I + III2 + II)2 and ~10.6 MDa megacomplex (IV2 + I + III2 + II)2 adapted to the cristae membrane curvature. Apart from working individually, ETC complexes can assemble into supercomplexes (SCs), including SC I + III23, SC III2 + IV1/27, and the respirasome SC I + III2 + IV10, as well as megacomplexes (MCs) such as MC I2 + III2 + IV213. Among these, SC I + III2 has been shown to be widely conserved across most eukaryotic clades, including the Opisthokonta (fungi and metazoan)2, Archaeplastida 13, Alveolata (ciliates and Apicomplexans)3, and Euglenozoan (kinetoplastids and euglenids)14, while further association of CIV differs in positions15. The term ETC megacomplexes describe the connection of two SC I + III2 by oligomerization of CIII or CIV, which is in line with the formation of even higher-order assemblies such as respiratory strings or patches in a range of species19. However, structural description is limited to the mammalian MC I2 + III2 + IV2, where two copies of CI are coupled by dimerization of CIII12, while possible organization of the respiratory patch remains hypotheses. In addition to CI passing electrons from NADH to ubiquinone (Q), CII participates in the tricarboxylic acid (TCA) cycle by oxidizing succinate into fumarate and depositing electrons also to Q, thus serving as an alternative ETC entry point20. Although proposed as a megacomplex component11 and/or respirasome assembly regulator23, CII has never been structurally confirmed as part of any ETC supercomplex. Currently, reported eukaryotic CII structures are mostly limited to metazoans26. CII structural diversity in other eukaryotic clades has been investigated biochemically and genetically28, but without reported structures.Oxidative phosphorylation is the energy-generating step of cellular respiration and takes place on the prokaryotic cytoplasmic membrane or the eukaryotic inner mitochondrial membrane (IMM). The energetically favorable oxidation of NADH or succinate by the electron transport chain (ETC) complexes I-IV (CI-CIV) first establishes a cross-membrane electrochemical gradient of protons, or the proton motive force (pmf), which is then utilized by the complex V (CV) to drive the synthesis of ATP2, CIV23, and CV oligomers29 of the model organism Tetrahymena thermophila (Tt) demonstrated their compositional and mechanistic divergences compared to the classic mammalian models, which likely resulted from structural adaptation to the tubular cristae of ciliate IMM30. Solubilization of the T. thermophila mitochondria membrane by mild detergents such as lauryl maltose neopentyl glycol (LMNG) and digitonin results in three separable bands with CI activity on blue native PAGE (BN-PAGE) 2 (Tt-MC IV2 + (I + III2 + II)2), we reveal that the obligatory Tt-CIV2 serves as the central coupler linking two Tt-SC I + III2 and two Tt-CII into a half ring-shaped megacomplex adapted to the cristae IMM curvature. Our Tt-MC IV2 + (I + III2 + II)2 structure agrees with a recent study of the ~5.8 MDa Tt-SC IV2 + I + III2 + II on multiple aspects, including overall organization, membrane curvature adaptation, Tt-CII structure, and inter-complex interaction sites31. We also demonstrate, via a low-resolution reconstruction of Tt-MC (IV2 + I + III2 + II)2, how two megacomplex half rings associate into a respiratory patch to maintain proximity between cytochrome c (cyt c) binding sites of Tt-CIII and Tt-CIV.In this study, by solving the structures of T. thermophila mitochondria membrane was purified into a single sample using sucrose gradient ultracentrifugation and size-exclusion chromatography  is difficult to capture accurately with a high enzyme concentration of 10 nM 2 2, respectively 2 particles 2  revealed the identities of the two upper bands as Tt-SC IV2 + (I + III2 + II)2 is a ~8 MDa megacomplex encompassing the entire ETC of T. thermophila. A total of 364 subunits are identified, mostly already assigned by blasting the density-derived query sequence against the T. thermophila proteome from UniProt in the previous study  helices (TMHs) from Tt-CI to Tt-CIV form a continuous patch of membrane domain flanked by two copies of Tt-CIII2, exhibiting a ~160° curvature when the tubular cristae is viewed axially 2 in a separate cryo-EM dataset using digitonin-solubilization, focused refinement of which resulted in 4.18 and 6.77 Å for its two Tt-SC IV2 + I + III2 + II parts 2 and Tt-CV4 2 corresponds to two axially packed Tt-CV2 in their strip of dimer row along the cristae tube30.We were also able to isolate a minor particle set corresponding to Tt-MC 8, in Tt-MC IV2 + (I + III2 + II)2 the constitutively dimeric Tt-CIV2 associates the Tt-CI MA heel from the side opposing its Q tunnel  or buried deeply inside by other Tt-specific subunits (COX7A and COX7)3. In addition, the Tt-specific CI toe bridge structure embraces Tt-CIII2 in Tt-SC I + III2 and precludes canonical respirasome assembly via the interaction between CI MA toe and Tt-CIV 2, subunit NDUTT4 at Tt-CI’s MA toe does contact Tt-CIV2 from the neighboring megacomplex via subunit COXMC2, as revealed by Tt-MC (IV2 + I + III2 + II)2 structure 2, but across stacks of rings with defined register on the IMM cristae. Conservation of such interaction across eukaryotic clades, albeit structural rearrangements, emphasizes the physiological importance of spatial proximity between cyt c redox sites of CIII2 and CIV. Decreasing such distance via supercomplex formation could decrease cyt c’s diffusion volume needed to achieve electron transfer and therefore provide a kinetic advantage over individual complexes, as demonstrated by theoretical39 and mutational40 approaches. Restricting cyt c’s 3D diffusion with the bulk matrix phase to electrostatically guided 2D diffusion on a supercomplex surface can further increase its turnover, as shown structurally in yeast SC III2 + IV6. In Tt-MC (IV2 + I + III2 + II)2, a direct distance of 97 Å is measured between the two cyt c redox sites across neighboring megacomplexes, while within a single Tt-MC IV2 + (I + III2 + II)2 the distance increases to 126 and 170 Å , previously invisible in individual Tt-CIV2 structure3, is now resolved as an all α-helical globular domain, fitting into a concave surface formed by the Tt-specific NT loop extension of NDUA3 and the C-terminal (CT) helix of NDUCA2 belonging to the γ carbonic anhydrase (γCA) domain 2 from the digitonin dataset and fits into the concave surface formed by COXTT21 and COXTT8 2 structure. The Y-shaped Tt-CII is a 288 KDa TM complex with eight hydrophilic and seven TM subunits, therefore structurally more complicated than the canonical four-subunit, ~130 KDa succinate dehydrogenases (SDHs) of E.coli42 and opisthokonts  have been previously classified into type A-F based on the numbers of TM subunit(s) and heme group(s)46. Tt-CII contains two core TM subunits, SDHC and SDHD 38. The ciliate-specific NT helix-loop-helix extensions of both NDUA4 copies are spatially adjacent to each other on the dimer interface and together line the lipid-filled central cavity of Tt-CIV2 from the matrix side parallelly to the IMM 2, as well as to Tt-CI MA toe of the neighboring megacomplex. For example, three mitochondrial carriers COXMC1-3 are identified on the peripheral of Tt-CIV2, of which COXMC1 and 2 form a heterodimer and are both annotated as 2-oxoglutarate carriers (SLC25A11) in UniProt database3. COXMC1 and 2 are kept in different states, with their central substrate cavities opening to the matrix and IMS sides, respectively. In this way, COXMC1 TMH2 comes in close contact with Tt-CII SDHTT1 on the matrix side and provides an additional Tt-CII attachment site to the megacomplex 2, thereby creating an additional interaction site across the two megacomplexes in Tt-MC (IV2 + I + III2 + II)2 apart from dimerization of Tt-CII’s IMS domain  is sterically locked by the extensive CI-CIII2 interactions and incapable of such swinging motion, as demonstrated by its much clearer electron density compared to the Rieske head near Tt-CI Q tunnel 3. Moreover, an edge-to-edge distance between the two bL hemes, measured between C2B carbon atoms (using PDB atomic nomenclature of ligand HEM) of the two porphyrin rings60, is 17.5 Å in the current Tt-MC IV2 + (I + III2 + II)2 structure, contrasted by that of 13.3 Å in the recent high-resolution structure of Arabidopsis thaliana SC I + III2 (PDB 8BPX)61. The generally accepted distance limit for direct electron transfer is 14 Å62, therefore, electrons cannot transfer across the two Tt-CIII2 protomers in Tt-MC IV2 + (I + III2 + II)2. The increased inter-bL heme distance and symmetry breaking regarding Rieske head mobility suggest that in Tt-SC I + III2, functional electron pathway exists only in the proximal Tt-CIII2 protomer and transports electron from the QP site in the proximal Q cavity to the QN site in the distal Q cavity 2 however, both Rieske heads lack clear density, thereby likely capable of the swinging motion required for electron transfer towards cyt c 2 2, suggesting the functional restoration of the electron pathway from the distal QP site to the proximal QN site.In Tt-SC IVt c Fig. 59. Moreo2 protomer in Tt-SC I + III2 and its restoration in Tt-SC IV2 + I + III2 + II and Tt-MC IV2 + (I + III2 + II)2 lead to a tempting possibility that the distal QP site is specialized in oxidizing QH2 generated by Tt-CII. Structurally, a notable cleft between ND5a’s bulk portion and its Tt-specific CT TMH constitutes a hydrophobic channel connecting two lipid-filled cavities separated by Tt-CI MA toe, reaching respectively to Tt-CII’s Q reduction cavity and to the distal Q cavity of Tt-CIII2 2, equally clear Q densities occupy both en route Q sites of Tt-CIII2, implying QH2 supply to not only the proximal but also the distal QP sites 2, it contacts Tt-CI MA toe in the neighboring megacomplex so that spatial proximity to Tt-CIII2 is maintained, likely to facilitate cyt c diffusion. Nonetheless, Tt-MC (IV2 + I + III2 + II)2 only represents a minor assembly species of digitonin-extracted T. thermophila IMM as demonstrated by its particle number during cryo-EM image processing  studies30, as different degrees of tubular curvatures can only be accommodated by flexible interfaces among stacks of ring-like ETC assemblies. Therefore, stable assembly with direct CIII2-CIV contact must be lacking in T. thermophila IMM, which is in stark contrast to opisthokonts and archaeplastidans with isolatable SC III2 + IV1/27. Physiological significances of SC III2 + IV1/2 formation include reducing the cyt c diffusion distance and kinetically optimize electron transfer between the two complexes24. Moreover, the concept of 2D diffusion has been raised for yeast SC III2 + IV1/2 as a negatively charged patch between its CIII2 and CIV cyt c sites serves as a sliding track for the positively charged cyt c channeling in between without equilibration with the bulk IMS phase6. Although no such charged surface patch has been identified for Tt-MC IV2 + (I + III2 + II)2 and Tt-MC (IV2 + I + III2 + II)2, the nearest distance (~97 Å) between cyt c reduction and oxidation sites in Tt-MC (IV2 + I + III2 + II)2 is comparable to that in a mammal (85 Å)4, yeast (61 Å)7, and plant (70 Å)5. The current organization of Tt-SC IV2 + I + III2 + II separating Tt-CIII2 from Tt-CIV is likely due to other structural and functional requirements. The large, obligatory dimeric Tt-CIV2 obviously requires a more extensive association interface than the narrow Tt-CI MA toe, while the incorporation of Tt-CII into the megacomplex necessitates spatial adjacencies of both Tt-CI and Tt-CII to Tt-CIII2 to reduce the diffusion distances of QH2. The increased distance between Tt-CIII2 and Tt-CIV sites within Tt-MC IV2 + (I + III2 + II)2 is compensated by the reversible contact between Tt-MC IV2 + (I + III2 + II)2 assemblies, providing an overall evolutionary advantage to T. thermophila ETC.Our findings demonstrate the structural divergence of 71, hypoxia-inducible gene domain (HIGD)73, and NDUA475 have been proposed as assembly factors in the ETC complex and supercomplex maturation of Opisthokonta. Among these, NDUA4 has not been detected in mammalian CI, CIV, or CIV2 except for the human36 and, to a lesser extent, ovine respirasomes76. It has neither been proposed as a CI-CIV linker due to its non-interfacial position in the respirasome76, nor as a CIV dimerization factor considering the potential steric clashes it would introduce at the dimer interface38. In the ciliate respiratory chain, however, its proposed role inhibiting CIV dimerization can be ruled out due to its presence on the dimer interface of Tt-CIV2 2 + IV)2 and Tt-MC IV2 + (I + III2 + II)2, apart from the incorporation of Tt-CII, is that the central coupler linking two CI copies together are mammalian CIII2 and Tt-CIV2, respectively. The half ring-shaped architecture of Tt-MC IV2 + (I + III2 + II)2 explicitly indicates that ETC complexes are packed into stacks of rings on T. thermophila tubular cristae. Questions are raised as to whether T. thermophila ETC is further organized into a full ring and what possible complex arrangement the full ring could have. A simple concatenation of two Tt-MC IV2 + (I + III2 + II)2 by the obligatory dimeric Tt-CIII2, as in mammalian and plant megacomplexes, would extend the ETC assembly curvature to 320°. However, it is unlikely to happen since superposing two Tt-MC IV2 + (I + III2 + II)2 by Tt-CIII2 introduces an elliptical instead of circular assembly 2. Whether such protein complexes adjacent to Tt-CV4 is one of the four ETC complexes or involve, unidentified structural elements is currently unknown. Therefore, the full architecture of the respiratory ring, the way Tt-CV4 is incorporated into such ring and possible variabilities in spatial register between neighboring rings await future cryo-electron microscopic and cryo-tomographic investigations.A major difference between mammalian MC  at 35 °C and 125 rpm shaking until 1 × 106 cells/ml density. Harvested cell pellets were lysed manually in TMIBS buffer ) with a pre-cooled KIMBLE Dounce tissue grinder. To get rid of the exocytosed mucocyst, a crude mitochondrial fraction of the lysate was washed twice in TMIBS buffer with 10% percoll (Yaseen Biotechnology) before pelleting down by centrifugation at 7000 × g for 10 min. This crude mitochondria was further purified on a discontinuous sucrose gradient with 30, 45, and 60% sucrose (w/v) in TMIB buffer  by centrifugation at 140,000 × g for 2 h in a SW32 Ti rotor (Beckman Coulter). Fine mitochondria fraction was harvested from the 45–60% sucrose interface and pelleted by final centrifugation at 16,000 × g for 45 min. The mitochondria pellet was weighed and stored at −80 °C until use.Wild-type T. thermophila mitochondria, thawed mitochondria pellets were homogenized in 10 ml/g  pure water manually in a pre-cooled KIMBLE Dounce tissue grinder. About 3 M KCl was then added to the homogenate to a final KCl concentration of 150 mM, followed by brief manual homogenization. The homogenate was then centrifuged at 32,000×g for 45 min to pellet down the membrane fraction. The pellet was resuspended in 18 ml/g (v/w of membrane pellet) M10 buffer ), followed by centrifugation at 32,000×g for 45 min. The pellet was again resuspended in 3 ml/g (v/w of membrane pellet) M10 buffer. The total protein concentration of the resuspended membrane fraction was determined by Bradford colorimetric assay (BCA), then added by M90 buffer ) to final glycerol concentration of 30%. The mitochondrial membrane fraction was stored at −80 °C until use.The following steps were performed at 4 °C unless otherwise indicated. To isolate the mitochondrial membrane fraction from 2 + (I + III2 + II)2 purification, thawed mitochondrial membrane  was solubilized by slow rotating for 1 h in buffer M10 , 0.002% PMSF (w/v), 10% glycerol (v/v)) with 1% lauryl maltose neopentyl glycol (LMNG) (w/v) at a detergent-to-protein ratio of 2:1 (w/w), before centrifugation at 16,000×g for 45 min. The supernatant was concentrated to ~3 ml in a centrifugal concentrator with 100 KDa molecular weight cut off (MWCO) (Millipore), which was used for all the following concentration steps in this section. The concentrated sample was loaded onto a continuous 20–50% (w/v) sucrose gradient in SGB buffer  with 0.005% LMNG (w/v) by 140,000×g centrifugation for 20 h using a SW32 Ti rotor (Beckman Coulter). Gradients were fractionated manually into 1 ml fractions, before running them on 2–8% BN-PAGE and in-gel CI activity nitrotetrazoleum blue assay. Fractions containing the two upper bands corresponding to Tt-MC IV2 + (I + III2 + II)2 and IV2 + I + III2 + II were pooled, concentrated, and loaded onto a Superose 6 Increase 10/300 GL column  for size-exclusion chromatography in SEC buffer ). Tt-MC IV2 + (I + III2 + II)2-enriched fraction eluting at ~9 ml was concentrated to 4 mg/ml and used immediately for cryo-EM grid preparation.For Tt-MC IV2 + I + III2 + II)2 was purified likewise. Briefly, the thawed mitochondrial membrane  was solubilized by slow rotating for 1 h in buffer MX  glycerol) with 2% digitonin at a detergent-to-protein ratio of 5:1 (w/w). After centrifugation at 140,000 × g for 20 h in a continuous 20–50% (w/v) sucrose gradient in SGB buffer with 0.1% digitonin (w/v), fractions at 33–38% sucrose concentration were pooled, concentrated, and loaded onto the Superose 6 Increase 10/300 GL column equilibrated in SEC buffer with 0.1% digitonin (w/v). The fraction eluted at 8–8.5 ml was diluted by SEC buffer with 0.1% digitonin (w/v) to ~1.5 mg/ml and used immediately for cryo-EM grid preparation.Tt-SC (IVc gene (UniProt: I7MFH3) immediately between the pelB periplasmic signal sequence and the 6×His tag was transformed into E. coli BL21 strain by Tsingke Biotechnology. Plasmid pEC86 containing the E. coli ccmABCDEFGH operon essential for maturation of cyt c was purchased from the Culture Collection of Switzerland78 and transformed into the E. coli BL21 strain carrying the pET22b(+) plasmid. A single double-transformed colony was cultured in 15 ml of LB containing 100 μg/ml ampicillin and 34 μg/ml chloramphenicol at 37 °C with 250 rpm shaking for about 12 h. Then 100 ml LB with the same antibiotics was inoculated with 10 ml of this start culture and cultured similarly overnight. The next morning, eight 1 L conical flasks, each containing 500 ml LB with the same antibiotics, were each inoculated with 10 ml of the previous culture and cultured at 30 °C with 250 rpm shaking for about 4 h until the optical density at 600 nm reached between 0.9 to 1. The cultures were then induced with 30 μM IPTG and cultured at 30 °C with 190 rpm shaking overnight.pET22b(+) plasmid containing synthesized Tt-cyt g at 4 °C, then resuspended in ice-cold TES buffer  containing 0.002% PMSF (30 ml TES buffer per pellet from 1 L of culture). The resuspensions were transferred into 300 ml bottles, added with 0.5 mg/ml lysozyme, and incubated for 15 min at room temperature. The bottles were then placed on an ice tray, added with equal volumes of ice-cold ultra-pure water containing 0.002% PMSF and bovine DNase A, then rocked at 90 rpm for 30 min. The suspension was then centrifuged at 12,000×g for 30 min at 4 °C, the supernatant of which was transferred to fresh tubes and filtered through a 0.45 µm filter. The cleared supernatant was loaded onto a 5 ml SP Sepharose FF column and eluted with a 0−500 mM NaCl gradient over 10 column volumes at a flow rate of 1 ml/min. Peak fractions monitored by absorbance at 415 and 550 nm were pooled and concentrated with 3 KDa MWCO centrifugal concentrators (Millipore) to a final concentration of ~9.7 mg/ml (~0.8 mM), estimated by BCA assay.The next morning, the bacterial cells were harvested by centrifugation for 15 min at 3000×c were determined by SDS-PAGE with a 12% Tris-SDS gel at 120 V before staining with Coomassie brilliant blue for 20 min  or 4 mM DDT were used to fully oxidize or reduce the Tt-cyt c. The redox state of the purified Tt-cyt c were measured by a spectral scan from 200 to 800 nm in 1 nm increments using a Thermo Scientific Spectrophotometer, from which an A550 nm/A565 nm ratio >9.0 was considered fully reduced , giving a reduced–oxidized Tt-cyt c extinction coefficient of 6.36 mM−1 cm−1 2 activity was determined by spectroscopic observation of individual and simultaneous NADH and succinate oxidation kinetics in the absence and presence of CI (rotenone), CII , CIII2 (antimycin A), and CIV (sodium azide) inhibitors at 340 and 550 nm. NADH (Aladdin), succinate (Sigma-Aldrich), decylubiquinone , rotenone (Sigma-Aldrich), malonate (Aladdin), antimycin A (MKbio), and sodium azide (Sigma-Aldrich) were used for following activity measurements as needed. All activities were measured in 384-well plates at room temperature using a Thermo Scientific Spectrophotometer with a total reaction volume of 20 μl per well. Extinction coefficients of 6.22 and 4.52 mM−1 cm−1 was used for NADH and Tt-cyt c in the activity calculations.Tt-MC IVc. For NADH:O2 and succinate:O2 oxidoreductase activities of Tt-MC IV2 + (I + III2 + II)2, 10 nM Tt-MC IV2 + (I + III2 + II)2 was used and where indicated, the relevant respiratory inhibitors  were added. The reaction was initiated by the addition of 500 μM NADH or 5 mM succinate, mixed in the spectrophotometer for 5 s, then recorded every 6 s at 340 and 550 nm simultaneously. The NADH:O2 and succinate:O2 oxidoreductase activities in Fig. c reduction, based on the slopes of the initial linear phases between 50 and 150 s in the 340 and 550 nm kinetic curves, respectively , 0.002% PMSF, 50 μM decylubiquinone (DQ), and 100 μM Tt-cyt 2 + (I + III2 + II)2, 2 nM Tt-MC IV2 + (I + III2 + II)2 were used for the activity assay and where indicated, 50, 100, 500 μM, or 5 mM malonate was added to the reaction mixture 2 (Dataset 1), a 3 μl sample was applied to Quantifoil 0.6/1 copper grids freshly glow discharged at 25 mA for 120 s, blotted by Vitrobot Mark IV (Thermo Fisher) for 8 s under 100% humidity at 4 °C and plunged-frozen by liquid ethane. For Tt-SC (IV2 + I + III2 + II)2 (Dataset 2), a 3 μl sample was applied to a Quantifoil R1.2/1.3 300 mesh copper grid with 2 nm thin layer of carbon freshly pre-glow discharged at 15 mA for 15 s. The blotting procedure is the same as for Dataset 1. Grids were transferred to liquid nitrogen before screening under a 200 kV Talos Arctica microscope (Thermo Fisher).The cryo-EM grid preparation and data collection were carried out at the Center of Cryo-Electron Microscopy at Zhejiang University. For Tt-MC IV−/Å2 with 7 s exposure time was fractionated into 1687 frames and a total of 16,772 movies were collected. For Dataset 2, the grid was imaged at a calibrated magnification of ×105,000 with a physical pixel size of 1.2 Å on images. A total dose of 51.4 e−/Å2 with 8.46 s exposure time was fractionated into 2039 frames and a total of 11,182 movies were collected.Grids with correct particle shape, density, and dispersion were used for the data collection on a Titan Krios microscope (Thermo Fisher) operated at 300 kV equipped with a Falcon4 detector operating at 241 frames/s and a Selectris energy filter. Automated data collection was performed using EPU software following standard procedures with a defocus range from −0.8 to −2.0 μm. For Dataset 1, the grids were imaged at a calibrated magnification of ×140,000 with a physical pixel size of 0.93 Å. A total dose of 61.5 e79, before contrast transfer function (ctf) estimation using CTFFIND4.180. Particles were picked in crYOLO81 and extracted in Relion-4.0 using box sizes 840 for Dataset 1 and 700 for Dataset 2. The following steps were performed in cryoSPARC v3.3.282. For Dataset 1, a total of 434,449 particles were generated after particle curation by 2D classification and 3D ab-initio reconstruction. Overall homogeneous refinement with global and local ctf corrections and C1 symmetry gave a resolution of 2.89 Å. Focused 3D classification on the Tt-SC I + III2 + II region that was absent in Tt-SC IV2 + I + III2 + II separated 97,688 Tt-MC IV2 + (I + III2 + II)2 particles from 336,761 Tt-SC IV2 + I + III2 + II particles. Overall non-uniform refinement83 with global and local ctf corrections and C2 symmetry of the Tt-MC IV2 + (I + III2 + II)2 particles gave a resolution of 2.96 Å. After re-extraction centered on each Tt-SC IV + I + III2 + II protomer, thereby doubling the particle number, local refinements were performed on Tt-CIV, Tt-CI PA, Tt-MA proximal region, Tt-MA distal region, Tt-CIII2 and Tt-CII, giving resolutions of 2.89, 2.96, 2.80, 2.83, 2.86, and 3.26 Å. These regional maps were combined into a composite map using the Combine Focused Maps program in Phenix-1.20.184. For Dataset 2, a total of 325,256 particles were generated after particle curation by 2D classification and 3D ab-initio reconstruction. Overall homogeneous refinement with C1 symmetry gave a resolution of 2.89 Å. Focused 3D classification on the Tt-SC IV2 + I + III2 + II region that was absent in Tt-SC IV2 + I + III2 + II and Tt-MC IV2 + (I + III2 + II)2 gave 19,023 Tt-SC (IV2 + I + III2 + II)2 particles, overall homogeneous refinement of which gave a resolution of 4.18 Å, but only one copy of Tt-SC IV2 + I + III2 + II had sensible density features. Local refinement of the second copy of Tt-SC IV2 + I + III2 + II gave a resolution of 6.77 Å, which was combined with the former map into a composite map in Phenix-1.20.1. The composite maps from both datasets were used for the following automatic and rigid-body model refinements.For both datasets, motion-correction was performed using Relion-4.0’s own implementation2 (PDB: 7W5Z) and Tt-SC I + III2 (PDB: 7TGH)3 were rigid-body fit into the composite map of Tt-MC IV2 + (I + III2 + II)2 in ChimeraX85. For Tt-CII, core subunits, including SDHA, SDHB, SDHC, and SDHD were homology modeled using the Phyre2 server86 and rigid-body fit into the Tt-CII region of the composite map. All manual model building was performed in Coot-0.9.687. Ciliate-specific subunits were first built as a poly-alanine model, side chains were then added de novo according to density. The generated query sequences were used in BLAST searches against the T. thermophila UP000009168 database from UniProt. Secondary structure information predicted by the PredictProtein server88 was also used to assist modeling in Coot-0.9.6. The manually built Tt-SC IV + I + III2 + II model was rigid-body fit into both Tt-SC IV + I + III2 + II protomers of the composite Tt-MC IV2 + (I + III2 + II)2 map, before being combined into a Tt-MC IV2 + (I + III2 + II)2 model with chain ID adjustments. Automatic model refinement was performed using the phenix.refine and phenix.real_space_refine programs against the composite Tt-MC IV2 + (I + III2 + II)2 map. We automatically generated secondary structure restraints, custom bond linkage, and custom ligand description files by Phenix-1.20.1 before manually editing according to the outcome of the automatic refinement. The refined model was manually edited in Coot-0.9.6 before the next round of automatic refinement until the refined model achieved high model-map correlation and good geometric statistics. A total of 326 subunits were built for Tt-MC IV2 + (I + III2 + II)2, among which 11 ciliate-specific subunits were uniquely identified for Tt-CII. For Dataset 2, individual Tt-CI, Tt-CIII2, and Tt-CIV2 models from the refined Tt-SC IV2 + (I + III2 + CII)2 model was rigid-body fit into Tt-SC (IV2 + I + III2 + CII)2 map and combined without further refinement.For Dataset 1, initial models for Tt-CIVFurther information on research design is available in the Supplementary InformationPeer review fileDescription of Additional Supplementary FilesDataset 1Supplementary Movie 1Supplementary Movie 2Reporting Summary


P < 0.001] of progression to Alzheimer’s disease dementia during follow-up. These findings indicate that CSF 14-3-3β may be a potential biomarker for Alzheimer’s disease and could provide a more comprehensive understanding of the underlying pathological changes of Alzheimer’s disease, as well as aid in the diagnosis and monitoring of disease progression.Alzheimer’s disease is a neurodegenerative disorder characterized pathologically by amyloid-beta plaques, tau tangles and neuronal loss. In clinical practice, the 14-3-3 isoform beta (β) is a biomarker that aids in the diagnosis of sporadic Creutzfeldt–Jakob disease. Recently, a proteomics study found increased CSF 14-3-3β levels in Alzheimer’s disease patients, suggesting a potential link between CSF 14-3-3β and Alzheimer’s disease. Our present study aimed to further investigate the role of CSF 14-3-3β in Alzheimer’s disease by analysing the data of 719 participants with available CSF 14-3-3β measurements from the Alzheimer’s Disease Neuroimaging Initiative. Higher CSF 14-3-3β levels were observed in the mild cognitive impairment group compared to the cognitively normal group, with the highest CSF 14-3-3β levels in the Alzheimer’s disease dementia group. This study also found significant associations between CSF 14-3-3β levels and CSF biomarkers of p-tau, t-tau, pTau/Aβ42 ratios and GAP-43, as well as other Alzheimer’s disease biomarkers such as Aβ-PET. An early increase in CSF 14-3-3β levels was observed prior to Aβ-PET–positive status, and CSF 14-3-3β levels continued to rise after crossing the Aβ-PET positivity threshold before reaching a plateau. The diagnostic accuracy of CSF 14-3-3β (area under the receiver operating characteristic curve = 0.819) was moderate compared to other established Alzheimer’s disease biomarkers in distinguishing cognitively normal Aβ pathology–negative individuals from Alzheimer’s disease Aβ pathology–positive individuals. Higher baseline CSF 14-3-3β levels were associated with accelerated cognitive decline, reduced hippocampus volumes and declining fluorodeoxyglucose-PET values over a 4-year follow-up period. Patients with mild cognitive impairment and high CSF 14-3-3β levels at baseline had a significantly increased risk [hazard ratio = 2.894 (1.599–5.238),  et al. report that increased CSF 14-3-3β levels in patients with Alzheimer’s disease correlate with cognitive decline and biomarkers of the disease, suggesting CSF 14-3-3β as a potential biomarker and tool for monitoring disease progression.Qiang   These biomarkers can be detected in biological fluids, such as CSF, or through neuroimaging techniques, such as MRI or PET scans, which can be utilized to classify patients according to the amyloid/tau/neurodegeneration [AT(N)] biomarker system.3 While CSF and PET biomarkers for β-amyloid plaques and tau pathology have high accuracy in detecting Alzheimer’s disease pathology, their feasibility in clinical diagnostic practice is limited due to the high cost and low availability of required tools and equipment.4 One potential approach to overcome the above issues is to explore whether some established biomarkers, which are already used in routine clinical practice, could aid in the diagnosis and prediction of disease progression of Alzheimer’s disease patients.Alzheimer’s disease is a chronic neurodegenerative disorder that affects millions of people worldwide, especially those over the age of 65.5 The 14-3-3 proteins are abundantly expressed in brain tissue and constitute ∼1% of total soluble brain protein.6 There are seven isoforms of the 14-3-3 protein family, including beta (β), gamma (γ), epsilon (ε), zeta (ζ), eta (η), theta (θ) and sigma (σ). In humans, the 14-3-3 isoform β is encoded by the YWHAB gene, which is located on the 20th chromosome.7 Testing for 14-3-3β in CSF has already been established as an effective method aiding the diagnosis of prion diseases, such as Creutzfeldt–Jakob disease, and multiple studies have confirmed the utility of this biomarker clinically for the diagnosis of sporadic Creutzfeldt–Jakob disease.8–12 A recent study using integrative proteomics to investigate new CSF biomarkers in Alzheimer’s disease identified hundreds of proteins with significantly altered abundance in CSF. Among these proteins, CSF 14-3-3β levels were significantly increased in Alzheimer’s disease patients.13The 14-3-3 proteins are a family of highly conserved regulatory proteins that play important roles in various cellular processes, including signal transduction, cell cycle control and apoptosis.In this study, we further explore the characteristics of CSF 14-3-3β in Alzheimer’s disease with participants from the Alzheimer’s Disease Neuroimaging Initiative (ADNI), and 719 participants with available CSF 14-3-3β measurements at baseline were involved in this study. We aimed to evaluate whether CSF 14-3-3β levels were elevated in Alzheimer’s disease, the associations of CSF 14-3-3β with other biomarkers of Alzheimer’s disease and the diagnostic accuracy of CSF 14-3-3β and its association with Alzheimer’s disease progression and cognitive decline.http://adni.loni.usc.edu/) in November 2022. This study included 719 participants with available CSF 14-3-3β measurements at baseline from the ADNI, ranging from clinically diagnosed cognitively normal (CN) to mild cognitive impairment (MCI) and Alzheimer’s disease dementia. For gender demographics, males constituted 43.61% of the CN group (99 of 227 participants), 54.88% of the MCI group (208 of 379 participants) and 60.18% of the Alzheimer’s disease dementia group (68 of 113 participants). The criteria for inclusion and exclusion as well as the specific ADNI diagnostic criteria for differentiating CN, MCI and Alzheimer’s disease participants have been previously outlined.14All participants in this study were sourced from the ADNI. Launched in 2003 as a public–private project under the leadership of principal investigator Michael W. Weiner, MD, the ADNI aims to facilitate research on Alzheimer’s disease by developing clinical, genetic, MRI/PET imaging and CSF/blood biomarkers for early Alzheimer’s disease diagnoses and monitoring of disease progression. The ADNI study as a multi-centred investigation was approved by ethics committees of all involved institutions and was conducted according to relevant guidelines and regulations. Authorized written informed consent was obtained from all study participants. The data analysed in this report were downloaded from the ADNI database (http://adni.loni.usc.edu/).CSF 14-3-3β levels were analysed using targeted proteomics assay by mass spectrometry (MS) at the Department of Neurology, Emory University School of Medicine. Crude CSF samples were reduced and alkylated, then denatured. CSF proteins were digested by Lys-C and trypsin. After digestion, peptides were analysed by the Agilent 1290 Infinity II liquid chromatography system coupled with the TSQ Altis Triple Quadrupole Mass Spectrometer (Thermo Fisher Scientific), and the mass spectrometer was set to acquire data using the single reaction monitoring approach. Total area ratios were calculated for relative quantification of CSF 14-3-3β peptides 44-51 (NLLSVAYK) and 63-70 (VISSIEQK). Detailed methods of the CSF 14-3-3β quantification can be downloaded from the ADNI database  were measured at the Clinical Neurochemistry Lab, University of Gothenburg, Sweden, through an in-house enzyme-linked immunosorbent assay (ELISA) method as previously described.18The levels of Aβ42, total tau (t-tau) and phosphorylated tau 181 (p-tau) in the CSF samples were analysed at the ADNI Biomarker Core laboratory at the University of Pennsylvania Medical Center. CSF Aβ42, CSF t-tau and CSF p-tau were quantified using electrochemiluminescence immunoassays on a fully automated Elecsys cobas e 601 instrument as previously described.19 The Clinical Neurochemistry Lab at the University of Gothenburg, Sweden, analysed both plasma p-tau 181 and neurofilament light (NfL) levels using the single molecule array (Simoa) technique.21 Plasma p-tau181 levels were measured by combining two monoclonal antibodies (Tau12 and AT270) targeting N-terminal to mid-domain forms of p-tau 181. Plasma NfL levels were measured using a combination of monoclonal antibodies.Measurements of plasma Aβ42 and Aβ40 levels were quantified at Bateman lab, Washington University School of Medicine. An anti-Aβ mid-domain antibody (HJ5.1) was used to isolate the specific Aβ isoforms in the KingFisher (Thermo) automated immunoprecipitation platform. The sample was then digested with Lys-N protease and analysed through liquid chromatography tandem MS, as previously described.22 The input for FreeSurfer was T1-weighted MRI images (MPR or IR-SPGR) in the NiFTI format. During analyses, we used data of hippocampus volumes and adjusted for intracranial volume.This study utilized 3.0 T MRI scanners for brain imaging. FreeSurfer version 5.1 was used to quantify regional volumes based on the 2010 Desikan–Killany atlas.24 Fluorodeoxyglucose (FDG)-PET image data were processed by the Helen Wills Neuroscience Institute, University of California Berkeley, and Lawrence Berkeley National Laboratory. A set of pre-defined regions of interest (MetaROIs) were developed based on literature review that was indicative of MCI and Alzheimer’s disease pathological metabolic changes. These regions included the left angular gyrus, right angular gyrus, bilateral posterior cingulate gyrus, left inferior temporal gyrus and right inferior temporal gyrus and were normalized by the pons and cerebellar vermis reference regions.25Aβ burden was estimated by amyloid PET using florbetapir (AV45) tracer, and amyloid PET image data were processed, and summary data were updated regularly by the Helen Wills Neuroscience Institute, University of California Berkeley, and Lawrence Berkeley National Laboratory. The image data were processed with FreeSurfer to define a cortical summary that included frontal, anterior/posterior cingulate, lateral parietal and lateral temporal regions. For cross-sectional analyses, the whole cerebellum was defined as the reference region. Summary global florbetapir standard uptake value ratios (SUVRs) were calculated, and amyloid PET results were defined as positive if global SUVRs were higher than 1.11.The participants’ general cognition level in this cohort was assessed using the Mini-Mental State Examination (MMSE) and the Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB).χ2 tests for categorical variables. The CSF 14-3-3β data were found to have a skewed distribution based on visual inspection of the histogram and were log transformed to obtain a data set with a normal distribution. Multiple group comparisons of log-transformed CSF 14-3-3β data were conducted using one-way ANOVA and the Tukey post hoc test. Pearson correlations were calculated between baseline log-transformed CSF p-tau and log-transformed CSF 14-3-3β. Linear regression models were used to test associations between fluid biomarkers with CSF 14-3-3β, and analyses were adjusted for age, sex, years of education and APOE ε4 status. We applied a cubic spline model to investigate the trajectories of CSF 14-3-3β as a function of Aβ-PET and used Aβ-PET as a proxy of disease progression along the Alzheimer’s disease continuum. The cubic spline model was estimated by using the postrcspline command in Stata software. Receiver operating characteristic (ROC) analyses were conducted to calculate the area under the curve (AUC) for discrimination of the CN control Aβ pathology–negative group from the Aβ pathology–positive MCI (MCI Aβ+) or Alzheimer’s disease group, and DeLong’s test was used to compare the AUCs of different fluid biomarkers. Cohen’s d was calculated to estimate the effect size of group differences. Associations between CSF 14-3-3β and longitudinal changes in cognition performance and neuroimaging were tested using linear mixed effects (LME) models. The LME models included an interaction between CSF 14-3-3β and time as the independent variable, and random intercept and random slope with the covariance of the random effects were unstructured. To directly compare effects, all outcome variables in LME models were standardized to Z-scores. All LME models included adjustments for age, sex, education years and APOE ε4 status, and adjustments for intracranial volume were also included in models involving imaging of the brain hippocampus. We utilized Cox proportional hazard regression to assess associations between CSF 14-3-3β and risk of progression to Alzheimer’s disease dementia. Initially, univariate analyses were used for the independent variables CSF 14-3-3β, age, sex, education years and APOE ε4 status, and then independent variables with P < 0.1 were further entered into a multivariate Cox proportional hazard model to analyse risk of progression to Alzheimer’s disease dementia. Schoenfeld residuals were used to evaluate the proportional hazards assumption. All figures except boxplots were created using Stata version 16  statistical software, and boxplots were created using R (version 4.2.0). All statistical analyses were conducted using Stata version 16  statistical software, and two-tailed P < 0.05 was deemed statistically significant.Differences in baseline characteristics between the Aβ-negative and Aβ-positive groups were assessed using the Wilcoxon rank-sum tests for continuous variables and Pearson’s A total of 719 participants with available CSF 14-3-3β measurements from targeted proteomics assays were involved in this study. Participants’ demographic characteristics are summarized in P = 0.001), with the highest concentration in the Alzheimer’s disease dementia group , tau pathology  or MMSE scores , as illustrated in Our study analysed the associations of CSF 14-3-3β peptides (peptides 44-51 and 63-70) with tau pathology, as measured by CSF p-tau. CSF 14-3-3β peptide 44-51 and peptide 63-70 levels were significantly associated with CSF p-tau . AssociaTo assess the changes of CSF 14-3-3β concentrations over the disease course of Alzheimer’s disease, cubic spline models were applied to model the trajectories of CSF 14-3-3β concentrations across the Alzheimer’s disease continuum, and Aβ-PET SUVRs were used as proxies for disease progression. In order to model the disease course over the Alzheimer’s disease continuum, our analyses excluded cognitively impaired participants who were Aβ-PET negative. The trajectories demonstrated that there was an early increase of CSF 14-3-3β peptide 44-51 and peptide 63-70 concentrations prior to development of Aβ-PET–positive status, and that the concentrations continued to rise after reaching the threshold of Aβ-PET positivity, then gradually came to a plateau .d = 1.96) and CSF t-tau (Cohen’s d = 1.662) showed the highest degree of change, followed by plasma p-tau (Cohen’s d = 1.228), CSF 14-3-3β peptide 44-51 (Cohen’s d = 1.14), CSF 14-3-3β peptide 63-70 (Cohen’s d = 0.973) and plasma NfL  and CSF t-tau (Cohen’s d = 1.107), followed by plasma p-tau (Cohen’s d = 0.896), CSF 14-3-3β peptide 44-51 (Cohen’s d = 0.794) and CSF 14-3-3β peptide 63-70 (Cohen’s d = 0.715), while plasma NfL (Cohen’s d = 0.55) showed the lowest degree of change (We next compared the diagnostic accuracy of CSF 14-3-3β peptides (peptides 44-51 and 63-70) and other fluid biomarkers to differentiate the CN control Aβ pathology–negative group (CN Aβ−) from the Aβ pathology–positive Alzheimer’s disease dementia group (Alzheimer’s disease Aβ+), using Aβ pathology measured by amyloid PET. ROC analyses were conducted, and the AUCs for each fluid biomarker were compared using DeLong’s test. The accuracy of CSF 14-3-3β peptide 44-51 was significantly lower than that of CSF p-tau and CSF t-tau but not significantly different from plasma p-tau and plasma NfL . As for f change .Next, we investigated whether baseline CSF 14-3-3β peptide levels were associated with cognitive changes, in terms of MMSE scores and CDR-SB scores, over 4 years of follow-up. According to the baseline distribution of CSF 14-3-3β peptide 44-51 levels, participants were subdivided into low-, intermediate- and high-level groups. Intermediate and high levels of CSF 14-3-3β peptide were associated with accelerated reductions in MMSE scores and increases in CDR-SB scores over time . We alsoP < 0.0001; P < 0.001; Kaplan–Meier curves showed the probability of progression to Alzheimer’s disease dementia in three MCI patient groups subdivided into tertiles of the CSF 14-3-3β peptide 44-51 level distribution, and the probability of development of Alzheimer’s disease dementia in MCI patients was statistically different among the three groups . No significant differences were found between the AT− and A + T− groups, and the highest levels of CSF 14-3-3β were observed in the A + T + group, which had significantly higher CSF 14-3-3β levels compared to the other two groups. This result suggests an association between elevated CSF 14-3-3β levels and the presence of both amyloid-beta and tau pathologies, which are indicative of an advanced pathological stage of Alzheimer’s disease.27 In brain extracts, tau interacts with 14-3-3 isoforms beta and zeta.31 Since CSF p-tau is a biomarker of tau pathology, the correlation between CSF 14-3-3β and CSF p-tau indicates that CSF 14-3-3β is also associated with tau pathology. Previous studies have found that the synaptic biomarker CSF GAP-43 is associated with tau pathology in Alzheimer’s disease,32 and in this study, we also observed that CSF 14-3-3β is associated with CSF GAP-43, highlighting the combination of tau pathology, synaptic dysfunction and 14-3-3β pathological changes of Alzheimer’s disease. This study found that CSF 14-3-3β levels were significantly higher in the Aβ-positive group compared to the Aβ-negative group. Moreover, CSF 14-3-3β levels showed a positive correlation with Aβ-PET SUVR. Our study also demonstrated an association between CSF 14-3-3β and CSF pTau/Aβ42 ratios, and a previous study highlighted the superiority of CSF pTau/Aβ42 ratios over CSF Aβ42 alone in aligning with Aβ-PET results.16 Collectively, these findings indicate that CSF 14-3-3β levels are also related to brain amyloidosis.In this study, we demonstrated that CSF 14-3-3β levels are associated with CSF p-tau levels. In Alzheimer’s disease, neurofibrillary tangles are formed by the abnormal aggregation of hyperphosphorylated tau protein, and previous studies have shown that 14-3-3 proteins are present in the neurofibrillary tangles of Alzheimer’s disease patients.33 and to ensure an accurate representation of the Alzheimer’s disease continuum, Aβ-PET–negative cognitively impaired participants were excluded from the analyses. The trajectories of CSF 14-3-3β peptide 44-51 and peptide 63-70 concentrations showed an early increase prior to reaching Aβ-PET–positive status. This phenomenon suggests that the levels of CSF 14-3-3β may start to rise even before the brain has significant Aβ plaque accumulation. After crossing the threshold of Aβ-PET positivity, CSF 14-3-3β levels continued to rise, indicating that their levels increase as the disease progresses. However, CSF 14-3-3β levels eventually reached a plateau, meaning that the increase in CSF 14-3-3β slows down or stabilizes at a certain time point in the disease course. These findings are important for understanding the role of 14-3-3β in the pathophysiology of Alzheimer’s disease. The fact that its concentrations increase before Aβ-PET positivity is reached and continue to rise as the disease progresses might suggest that CSF 14-3-3β could potentially serve as an early biomarker for Alzheimer’s disease or that 14-3-3β may be involved in the underlying disease mechanisms, and further study is needed to confirm and understand these observations.This study assessed the changes in CSF 14-3-3β concentrations over the disease course of Alzheimer’s disease by examining their trajectories across the Alzheimer’s disease continuum. We used Aβ-PET SUVR as a proxy for disease progression,The present study compared the diagnostic accuracy of several fluid biomarkers in distinguishing CN Aβ− individuals from Alzheimer’s disease Aβ+ or MCI Aβ+ patients, using amyloid PET to define Aβ status. The biomarkers compared included CSF 14-3-3β peptides (peptides 44-51 and 63-70), CSF p-tau and CSF t-tau, plasma p-tau and plasma NfL. Our results suggest that while CSF 14-3-3β is less accurate than CSF p-tau and CSF t-tau in discriminating CN Aβ− from Alzheimer’s disease Aβ+ or MCI Aβ+ individuals, CSF 14-3-3β performs similarly to plasma p-tau and plasma NfL. These findings suggest that while CSF 14-3-3β may not be as accurate as CSF p-tau and CSF t-tau in distinguishing CN Aβ− from Alzheimer’s disease Aβ+ or MCI Aβ+ groups, it may still be a promising biomarker for Alzheimer’s disease diagnosis, particularly when compared to plasma biomarkers such as plasma p-tau and plasma NfL. In this study, the comparison of diagnostic accuracy of CSF 14-3-3β with multiple fluid biomarkers also provides a comprehensive illustration of CSF 14-3-3β’s diagnostic performance in Alzheimer’s disease.Our study also investigated whether baseline levels of CSF 14-3-3β could predict changes in cognitive function, as well as changes on neuroimaging over time. The results showed that, compared to those with low levels of CSF 14-3-3β, individuals with intermediate and high levels of CSF 14-3-3β had accelerated reductions in MMSE scores and increases in CDR-SB scores, indicating that higher baseline levels of CSF 14-3-3β are associated with faster cognitive decline over time. Additionally, these individuals also had faster reductions in hippocampus volumes and greater declines in FDG-PET values, suggesting that higher baseline levels of CSF 14-3-3β are associated with greater neurodegeneration and neuronal dysfunction over time. These findings thus indicate that higher baseline levels of CSF 14-3-3β may predict accelerated cognitive decline and more pronounced neurodegeneration in individuals at risk for progression to dementia. However, further studies are needed to explore underlying mechanisms for the observed associations between baseline CSF 14-3-3β levels and longitudinal changes in cognitive function and on neuroimaging.34 Identifying individuals with MCI who are likely to progress to Alzheimer’s disease dementia could help guide more targeted interventions and follow-up strategies. Our study showed that the probability of conversion to Alzheimer’s disease dementia was significantly different among the three MCI patient groups based on their CSF 14-3-3β distributions, with high levels of CSF 14-3-3β associated with increased risk of progression to Alzheimer’s disease dementia. With a previous study demonstrating that CSF Aβ42, t-tau and p-tau levels are associated with the risk of progression to Alzheimer’s disease dementia in MCI patients,35 our findings suggest that measuring CSF 14-3-3β levels in MCI patients might also be a useful tool for identifying those at the highest risk of developing Alzheimer’s disease dementia.MCI is a condition characterized by cognitive impairment that is greater than expected for an individual’s age and education level but not severe enough to meet the criteria for dementia. However, many individuals with MCI will eventually progress to Alzheimer’s disease dementia.9 Our study provides evidence that CSF 14-3-3β levels are increased in Alzheimer’s disease and are associated with other biomarkers of the disease, such as biomarkers of tau pathology and the synaptic biomarker CSF GAP-43.36 The association between CSF 14-3-3β levels and tau pathology is particularly noteworthy, as it highlights the potential of 14-3-3β as a biomarker of tau-related neurodegeneration. Additionally, our study showed an early increase in CSF 14-3-3β concentrations before Aβ-PET–positive status was reached, suggesting that CSF 14-3-3β may be a useful biomarker for preclinical Alzheimer’s disease patients.The findings of this study are consistent with previous research that demonstrated the potential of CSF 14-3-3β as a biomarker for neurodegenerative diseases, such as Creutzfeldt–Jakob disease.37The present study has several limitations. First, due to lack of data from patients with other neurodegenerative disorders, such as dementia with Lewy bodies, frontotemporal dementia, progressive supranuclear palsy and corticobasal syndrome, the current study is limited in its ability to assess the specificity of CSF 14-3-3β for Alzheimer’s disease. Second, the CSF biomarkers were employed as proxies of Alzheimer’s disease pathology, and it should be noted that autopsy remains the gold standard for assessing Alzheimer’s disease pathology. Third, in this study, CSF 14-3-3β levels were measured by targeted proteomics assay using mass spectrometers. However, MS is not commonly used in clinical practice for analysis of CSF biomarker levels, and other traditional protein quantification methods such as ELISA assay of CSF 14-3-3β should be explored to delineate the characteristics of CSF 14-3-3β in Alzheimer’s disease patients. Fourth, we utilized CSF pTau181 and plasma pTau181 as markers for tau pathology. However, other phosphorylation sites, such as pTau217 (data not available in ADNI), could be more effective biomarkers.In conclusion, alongside how CSF 14-3-3β is routinely used clinically as a biomarker to aid in the diagnosis of sporadic Creutzfeldt–Jakob disease, our study provides additional evidence that CSF 14-3-3β levels are increased in Alzheimer’s disease and that they are associated with other biomarkers of the disease, such as biomarkers of tau pathology and synaptic biomarkers. Though the diagnostic accuracy of CSF 14-3-3β was moderate compared to CSF p-tau and CSF t-tau in distinguishing CN Aβ− from Alzheimer’s disease Aβ+ individuals, baseline CSF 14-3-3β levels were associated with progressive decline of cognitive function over time. These findings suggest that CSF 14-3-3β may be a useful clinical biomarker for Alzheimer’s disease and could potentially aid in early diagnosis and prediction of disease progression.Brain Communications online.fcad312_Supplementary_DataClick here for additional data file.


Fallopian canal meningocele is an extremely rare cause of cerebrospinal fluid rhinorrhoea. Also, due to complex anatomical relations and a lack of experience, its management remains a challenge. Here we report a case focusing on its clinical course, radiological features, and management. Fallopian canal meningocele (FCM) is a rare cause of cerebrospinal fluid (CSF) leak. Only 17 cases have been reported to date, so its incidence is unknown . PathophA 6-year-old female visited our centre, she had history of idiopathic intracranial hypertension and recurrent meningitis. Moreover, she had history of a left medial temporal lobe encephalocele managed surgically by transsphenoidal approach in another centre.She presented to our centre with suspicion of intermittent CSF rhinorrhoea. A CSF leak was detected at the base of both pterygoid processes. Neurosurgery tried to seal these leaks surgically. However, rhinorrhoea persisted and a right- sided intermittent facial palsy debuted. Then, a ventriculoperitoneal shunt was performed ceasing rhinorrhoea complaints. Nonetheless, two months later the leak was evidenced again, and we decided to explore the nasopharynx surgically using intrathecal fluorescein. The substance was observed in the nasopharyngeal mucosa, especially around the left eustachian tube opening. Cone beam computed tomography (CT) and magnetic resonance were performed to explore the left temporal bone and middle cranial fossa and findings were compatible with FCM Fig. . An attiFCM has been described as the underlying cause of a CSF leak in 17 cases, the first of them was reported in 1967 . There iGeniculate fossa is the dehiscence location in our patient and in more than 70% of the cases reported . TheoretThe management is challenging compared to usual CSF leak locations (tegmen tympani and mastoid region). First, in case of FCM, the CSF leak has its origin on the posterior fossa and these leaks have higher CSF flow than middle fossa leaks. Moreover, the surgical management of FCM requires the occlusion of the fallopian canal avoiding damage to the facial nerve . ConcretTo summarize, FCM is a rare cause of CSF leaks and only 17 cases have been reported to date. Surgical management is complex and there is a high risk of postoperative facial nerve palsy and/ or persistence of the CSF leak. Then, to report experiences is essential to precisely assess risks and optimize the management of future cases.


The current clinical practice of assessing the quality and suitability of a donor liver for human transplantation does not exclude cases of primary graft dysfunction of the transplanted organ and, at the same time, leads to an unreasonable refusal to transplant a significant number of functionally suitable organs. In this regard, searching for new methods for additional objective assessment and monitoring of the state of donor organs in the peritransplant period is relevant.The aim of the study was to determine the clinical utility of monitoring interstitial concentrations of glucose and its metabolites to assess the viability and functional state of a donor liver before and after human transplantation.A retrospective observational single-center study included 32 cases of liver transplantation. Along with standard methods for assessing the initial function of grafts during the first week after surgery, interstitial (in the transplanted liver) concentrations of glucose and its metabolites were monitored. In 18 cases, the interstitial glucose metabolism was also studied during static cold storage (SCS).With the development of early allograft dysfunction (EAD), compared with the uneventful post-transplant period, statistically significantly higher interstitial lactate concentrations were observed as early as 3 h after reperfusion: 12.3  mmol/L versus 7.2  mmol/L (p=0.003). A value above 8.8 mmol/L may be considered as a criterion for the immediate diagnosis of EAD .Interstitial lactate concentration at the end of SCS and the area under the “lactate concentration–SCS duration” curve were associated with the initial graft function. Values of these parameters greater than 15.4 mmol/L and 76.1 mmol/L·h, respectively, with a sensitivity of 100% in both cases and a specificity of 77 and 85%, may be used to assess the risk of primary EAD.Monitoring of interstitial concentrations of glucose and its metabolites, primarily, lactate, is an objective additional method for the assessment of the donor liver viability both during SCS and in the early postoperative period. Over the past two decades, the number of liver transplants performed annually in Russia has increased almost fiftyfold: from 12 operations in 2000 to 584 in 2019  and EuroObviously, the extension of the criteria for a deceased donor liver suitability for transplantation, on the one hand, increases the number of procedures performed and reduces mortality among transplant candidates, and, on the other hand, it increases the risk of primary non-function or early allograft dysfunction (EAD) after transplantation, which affects the immediate and long-term outcomes of the operations and increases the need for repeated transplantations.Current clinical practice involves the assessment of the quality of a donor organ and the feasibility of its subsequent transplantation based on the results of a number of standard laboratory and instrumental tests of a potential donor (a person with an established diagnosis of brain death) before the procurement, as well as during the surgery based on the subjective visual assessment. In some cases, an urgent histological study is acceptable, which, however, is not common due to the possible increase in the time of graft preservation and only an indirect assessment of the functional suitability of the organ. The existing approaches allow for the risks of primary non-function (PNF) graft and at the same time lead to unreasonable refusal to transplant a significant number of organs, which have been found unsuitable because of an erroneous subjective assessmentTaking into consideration the limited diagnostic and prognostic potential of routine methods, we decided to initiate a scientific clinical study aimed at searching and determining the effectiveness of additional objective methods for assessing the viability and functional state of the donor liver during preservation and in the early post-transplant period. Based on the analysis of the literature and the available technological potential of the clinic, interstitial microdialysis with the assessment of glucose metabolism parameters has been chosen as the study method.The work was carried out in the Laboratory of New Surgical Technologies and subdivisions of the Surgery and Transplantation Center of the Burnazyan Federal Medical Biophysical Center of FMBA of Russia. It was a two-stage observational study ; its resAt stage I , interstitial metabolism was studied only in the early postoperative period: from the completion of transplantation to the end of the 7th postoperative day. This part of the work included 14 observations, in 5 cases of which the right lobe of the liver from a living donor was transplanted.At stage II , the period of sample collection and analysis of the composition of interstitial fluid was extended and lasted since the beginning of graft explantation, during the period of static cold storage (SCS) and transportation of the donor organ, as well as during the first week after surgery. There were 18 observations at stage II, all the organs were obtained from deceased donors.In this way, 32 recipients were monitored for the parameters of interstitial metabolism after transplantation . The analyses of the observations were put together in order to study the dynamics of the interstitial concentrations of glucose, lactate, pyruvate, and glycerol in the early postoperative period, to establish their relationship with the initial function of the grafts (assessed on the 7th day), as well as with the immediate outcomes of operations (assessed on the 7th and 30th days).To study the impact of the parameters of interstitial metabolism during SCS on the severity of ischemic and reperfusion injury (IRI) and the initial function of the transplanted liver, 18 cases were analyzed separately, in which the study was also carried out during SCS and donor organ transportation.Early allograft dysfunction was defined according to criteria by Olthoff et al. : the levTo deal with each of the issues see , all theOut of 18 cases included in the second part of the analysis, 13 patients had an uneventful postoperative course (group C), EAD was diagnosed in 5 recipients (group D), and no cases of PNF were noted.32 patients (21 men and 11 women) aged 38 to 68 years (the median was 53 years) were transplanted. All the procedures were primary. The indications for surgery were liver cirrhosis (LC) caused by viral hepatitis , hepatocellular carcinoma associated with LC of viral etiology (n=8), cholestatic liver diseases (n=3), unresectable liver alveococcosis (n=3), LC as a result of non-alcoholic steatohepatitis (n=2), alcoholic LC (n=2), LC of unknown etiology (n=2). Severity assessment by the MELD score varied from 7 to 40 points (the median was 15).In 5 cases, the graft of the right lobe of the liver was obtained from living-related donors (3 men and 2 women) aged 22 to 49 years (the median was 32 years). Graft weight varied from 732 to 1130 g (median 885 g), the graft-recipient weight ratio ranged from 1.15 to 1.41% (the median was 1.25%).In 27 cases, an organ for transplantation was obtained from deceased donors (18 men and 9 women) with established brain death, resulted from cerebrovascular accident (n=21) and severe traumatic brain injury (n=6). Donor age ranged from 20 to 67 years (the median was 48 years). The mean time spent on mechanical ventilation before retrieval was 3 days (from 1 to 6). In all the cases, hemodynamics was stable, without hypotensive and/or asystolic episodes. The results of the laboratory tests immediately before procurement were as follows: sodium — 147 (129–163) mmol/L, AST — 48 (14–200) U/L, ALT — 38 (7–110) U/L, total bilirubin — 10.2 (2.0–16.6) μmol/L. The median cold ischemia time (CIT) of the organs obtained from deceased donors was 9.0 (from 3.6 to 13.5) h. In 21 cases, aircraft transport was used for transportation. The CIT in living-donor liver transplants ranged from 30 min to 1 h 20 min (the median was 45 min).The principle of microdialysis and its application in liver transplantation are known and described earlier -8, as weWhen an organ was procured from a deceased donor, a microdialysis catheter was placed in segment IV of the liver to obtain interstitial fluid samples immediately after sternolaparotomy and the initial stage of liver mobilization  on the preserved blood flow. The catheter was fixed with a ligature to the falciform ligament, filled with perfusion fluid, and connected to a microdialysis pump. The pump was placed inside a sterile surgical glove, which was hermetically tied with a ligature and attached to the surgical drapes. 20 min after perfusion started, a sterile microtube was placed in the catheter port to collect interstitial fluid. Changing microtubes  was performed once an hour until the organ was delivered to the operating room of the transplantation center. The feed rate of perfusate into the catheter was 0.3 μl/min.After explantation, the organ was packed in the standard manner in three sterile plastic bags, with a microdialysis catheter sequentially passed through the node of each of them. When packing was completed, the sections of the catheter with a port for microtubes and connecting a microdialysis pump became non-sterile. The packed organ was placed in a transport isothermal container. Before unpacking the donor organ, the outer non-sterile part of the catheter was cut off; then unpacking was performed in the traditional way. Before starting the graft processing, the microdialysis catheter placed at the time of excision was removed.A catheter for collecting samples of interstitial fluid in the early postoperative period was placed in the graft parenchyma immediately before suturing the surgical wound, brought out through the counteropening to the anterior abdominal wall and fixed to the skin.In case of transplantation from a living donor at the stage of donor right-sided hemihepatectomy, after liver mobilization, before parenchyma splitting, a microdialysis catheter under ultrasound control was placed at the border of segments VII and VIII and fixed with a ligature to a fragment of the right triangular ligament.After transplantation, the microtubes were changed every 3 h until the 7th postoperative day. During the study, the microdialysis pump was placed and fixed on the anterior abdominal wall of the patient. After sampling was completed, the microdialysis pump was turned off, and the catheter ports were closed. The catheter was removed on the 10–14th postoperative day, then a control abdomen ultrasound was performed.Microdialysis catheters for hepatic tissue , Perfusion Fluid T1, 106 Microdialysis Pump, syringes for microdialysis pump (106 Syringe), and special microtubes with a volume of 200 μl were used to obtain interstitial fluid samples. The composition of interstitial fluid samples was determined 2 times a day on the ISCUS Clinical Microdialysis Analyzer using Reagent Set B. All the equipment and consumables were manufactured by CMA Microdialysis AB (Sweden) and have a registration certificate for a medical device No.FSZ 2007/00697 dated November 28, 2007, valid in the Russian Federation. The results of the study were digitized.Quantitative variables were presented as the median values, with additionally indicating either the minimum and maximum values, when it was them that represented clinical significance, or giving the interquartile range absolute frequencies indicated qualitative features. The significance of differences in quantitative and qualitative variables in two independent samples was determined using the nonparametric two-tailed Mann–Whitney test and two-tailed Fisher’s exact test, respectively. Differences were considered statistically significant at p<0.050. When calculating the odds ratio of a binary outcome, a 95% confidence interval (CI) was also indicated. To find the boundary values of quantitative parameters, ROC analysis was performed, sensitivity and specificity were calculated. Calculations were performed using the Statistica 12 software package .The main characteristics of the recipients, donors, and transplants were matched by comparing groups A and B . By the st, 5th, and 7th days after surgery due to PNF, hepatic artery thrombosis, and progressive multiple organ dysfunction syndrome (MODS), as well as severe EAD after the development of malignant hyperthermia in the intraoperative period. All the early graft losses occurred in group B. By the 30th postoperative day (i.e. over the next three weeks), three more grafts were lost: two in group B (on the 22nd and 26th days as a result of the death of the recipients because of progressive MODS due to sepsis) and one case in group A .Assessment of the renal function of the recipients by the end of the first postoperative week demonstrated a statistically significantly higher frequency of episodes of acute renal injury (“Injury” and higher severity according to the RIFLE classification ) and a nth day because of the patient’s death from progressive MODS due to sepsis.Due to the fact that the observations of groups C and D , PNF was 3% (1/32); by the end of the first month after transplantation, 81% of recipients (26/32) were alive. In 5 of 6 cases of the loss of a transplanted liver, it occurred against the background of EAD/PNF. The odds ratio of graft loss in the development of early dysfunction, including PNF, was 27.5 (95% CI: 2.5–302.2).The dynamics of interstitial concentrations of glucose, lactate, pyruvate, and glycerol is shown in nd–3rd postoperative day;moderately increased, compared with normal values for capillary blood, interstitial glucose concentration during the first day tending to decrease to 5–10 mmol/L during the 2the maximum levels of lactate registered during the first hours after the operation and its subsequent decrease;an increase in the concentration of pyruvate in the first hours after the operation, reaching peak values after 18–24 h and then decreasing to 100–200 μmol/L.Higher concentrations of lactate (compared to the smooth course of the postoperative period) in the first measurement 3 h after reperfusion — 12.3  mmol/L versus 7.2  mmol/L (p=0.003), and after 24 h — 6.1  mmol/L versus 2.4  mmol/L (p<0.001), with subsequent retention of statistically significant differences throughout the entire observation period, should be considered the most important feature of the dynamics of interstitial concentrations of the studied parameters in group B, i.e. with EAD development .The ROC analysis determined the threshold value of interstitial lactate concentration to be 8.8 mmol/L. The use of the threshold value for the diagnosis of EAD provides 89% sensitivity, 65% specificity (the area under the ROC curve is 0.85).th–5th postoperative day, chronologically coincided with the termination of parenteral nutrition. None of the patients in group A received parenteral nutrition during the first week after transplantation.Statistically significant differences in interstitial glycerol concentrations , which pIn all the cases, the conditioning of deceased donors, procurement, preservation and transportation of donor organs were carried out by one surgical team in compliance with a single technology, the same criteria for assessing the quality and suitability of a donor liver for transplantation were used. Despite these facts, interstitial concentrations of the studied substances throughout SCS were ranged widely: glucose varied from 3.3 to 38.5 mmol/L, lactate — from 0.3 to 19.9 mmol/L, pyruvate — from 1 to 239 μmol/L, glycerol — from 6 to 1606 μmol/L. The universal trends were an increase in the concentrations of glucose , lactatein situ cold perfusion and at the end of SCS, the areas under the “concentration–SCS duration” curves ;primary EAD, the causes of which are the specific characteristics of the donor , the donor organ , as well as preservation , rejection, infections, cardiovascular and respiratory complications, should be considered as a separate condition.The methods for assessing the quality of the deceased-donor liver used in real clinical practice are, to a certain degree, subjective, and this is what complicates the differential diagnosis of primary and secondary EAD. Normothermic perfusion with oxygenated erythrocyte-based media becomes a solution to this problem and, in the vast majority of cases, makes it appropriate to use grafts of subjectively non-ideal  quality, excluding the risk of severe primary EAD and PNF . It shouIt is these circumstances, as well as a unique feature of the Coordination Center for Organ Donation of the Burnazyan Federal Medical Biophysical Center  that have become decisive for initiating and conducting stage II of this study, i.e. studying the features of interstitial glucose metabolism in the deceased-donor liver during SCS, assessing of their relationship with the IRI severity and the initial graft function. In the analysis of previously published works, we have found only one similar clinical study performed by Silva et al. , consistDespite the described advantages of the method of interstitial microdialysis, the practicability of its routine use in clinical practice is debatable. The retrospective nature of the results of the study of the transported donor organ is supposed to be the main disadvantage since the analysis of the composition of microsamples of the interstitial fluid is performed on the stationary equipment at the transplantation center. This either excludes the use of the results at the time of making a decision on the suitability of the organ for transplantation and the start of the operation in the recipient, or requires additional time to conduct the study after the delivery of the organ to the clinic, which causes a delay in the start of the operation until the results are obtained and, as a result, the SCS duration is extended by up to 2–3 h. Moreover, studying interstitial metabolism requires specialized equipment  and disposable consumables. Compared to the technology of isolated normothermic perfusion, their cost is significantly lower (by a factor of 10 or more), however, it demands leveraging additional financial resources: the study only during the SCS period accounts for about 70 thousand rubles, monitoring extension in the early postoperative period requires about 100 thousand rubles .Obviously, addressing these challenges rests exclusively in the area of technical implementation of the method for obtaining and analyzing the composition of interstitial fluid samples. We consider it to be promising to shift from the photocolorimetric method of measuring metabolite concentrations in the microsamples of interstitial fluid to the use of flow-through electrochemical sensors and obtaining the results of the analysis in real time. The development of a specialized monitoring module coupled with such sensors will provide sending data during transportation, and, if necessary, continuous measurement of other parameters .In our opinion, the creation of a portable medical device for monitoring the state of interstitial metabolism both during preservation and in the postoperative period for early diagnosis of functional disorders and vascular complications would provide adequate clinical studies in order to establish accurate diagnostic and prognostic criteria and be a decisive factor for the subsequent rapid and large-scale introduction of the interstitial microdialysis technology into the clinical practice of organ donation and transplantation. The opportunity of using the same device at all stages and for any options for static and perfusion preservation of organs provides formulation and proposal of a new concept for assessing their quality, predicting the risk of severe primary EAD and PNF . At the It should be underlined that the assessment of the parameters of interstitial metabolism during the preservation period and obtaining additional objective data on the state of the donor organ can be carried out not only for the liver. This is confirmed by the findings in published works on monitoring the state of the kidney in the experiment , 17 and The extension of the criteria for the suitability of donor organs for human transplantation is an inevitable process that has accompanied the entire history of clinical transplantation. The most efficient use of organs from deceased donors implies not only an increase in the number of transplants performed but also the achievement of satisfactory immediate and long-term results. This principle determines the extremely high relevance and need for search work and clinical studies of new technologies for the preservation and assessment of the viability of donor organs. One of the already ongoing activities is the development of methods and devices for isolated normothermic perfusion. Although, static cold storage has been and remains a reliable approach for organ preservation, the main disadvantage of which is the inability to monitor the organs’ condition. The findings of foreign studies, as well as the results of our own practice, allow us to rightly claim that the study of the parameters of interstitial metabolism of glucose (mainly lactate) will solve this problem.Moreover, the use of interstitial microdialysis in the early post-transplant period gives an immediate (within a few hours after reperfusion) assessment of the initial graft function, allows to differentiate primary and secondary EAD, and, accordingly, have a chance to adjust the intensive care plan in a preventive mode. Continued monitoring during the first days after surgery can be considered as a method for early diagnosis of vascular complications.Technical improvement of the process of collecting and analyzing samples of interstitial fluid and the creation of a medical device will be crucial for the further development and widespread implementation of the technology into clinical practice.


Some microbiota compositions are associated with negative outcomes, including among others, obesity, allergies, and the failure to respond to treatment. Microbiota manipulation or supplementation can restore a community associated with a healthy condition. Such interventions are typically probiotics or fecal microbiota transplantation (FMT). FMT donor selection is currently based on donor phenotype, rather than the anticipated microbiota composition in the recipient and associated health benefits. However, the donor and post-transplant recipient conditions differ drastically. We here propose an algorithm to identify ideal donors and predict the expected outcome of FMT based on donor microbiome alone. We also demonstrate how to optimize FMT for different required outcomes.We show, using multiple microbiome properties, that donor and post-transplant recipient microbiota differ widely and propose a tool to predict the recipient post-transplant condition , using only the donors’ microbiome and, when available, demographics for transplantations from humans to either mice or other humans (with or without antibiotic pre-treatment). We validated the predictor using a de novo FMT experiment highlighting the possibility of choosing transplants that optimize an array of required goals.We then extend the method to characterize a best-planned transplant  by combining the predictor and a generative genetic algorithm (GA). We further show that a limited number of taxa is enough for an FMT to produce a desired microbiome or phenotype.Off-the-shelf FMT requires recipient-independent optimized FMT selection. Such a transplant can be from an optimal donor or from a cultured set of microbes. We have here shown the feasibility of both types of manipulations in mouse and human recipients.Video AbstractThe online version contains supplementary material available at 10.1186/s40168-023-01623-w. In recent decades, the relationship between the human gut microbiota and its host health has been shown in a wide range of conditions, ranging from metabolic disorders through autoimmune diseases to mental health disorders –6. The rOften, finding specific bacterial strains or metabolites to prescribe is challenging, as the microbiota is a complex community. Furthermore, clinical studies include real-life noise, unlike experimental setups with GF animals or under extremely controlled environments. Ensuring subsequent colonization of a single strain in the milieu of other microbes is challenging, and predicting the specific microbe’s functionality within the complex microbiota is nearly impossible. Full personalization of microbe-based treatments is still cost prohibitive and may even be unnecessary.Clostridioides difficile infection) and is studied in a large variety of other conditions , where the minimum value represents the performance of a random classifier and the maximum value would correspond to a perfect classifier . It measures the area under the ROC curve we define above.Genetic algorithm (GA). GA is a method for solving constrained and unconstrained optimization problems that uses iterations of selection and modifications on a family of solutions. Here, we optimize the best-planned transplant  by combining a simple GA model with the iMic predictor.In order to facilitate the understanding of the more machine learning (ML)-oriented terms in the text, we here provide a short description of the main ML terms used in the manuscript.We trained our models (described in full in the Same donor - same recipient (SDSR). All distances between samples of a single recipient, collected at different time points post-FMT, to measure the temporal variation in a recipient.Same donor - different recipients (SDDR). All distances between all the different recipients that received FMT from the same donor and were sampled at the same time point post-FMT. This measures the effect of the recipient’s background on its post-FMT microbiome.Different donors - different recipients (DDDR). All distances between recipients that received a transplant from different donors, and were sampled at the same time point. This adds the effect of the donor to the effect of the background.In several cohorts, the same donor stool was given to multiple recipients, resulting in multiple post-FMT recipient properties  for the same donor. To compare the effect of the donor microbiome vs the recipient, we defined the similarity between samples as the Euclidean distance between the MIPMLP  preproceThe validation experiment consisted of 4 steps:D was assembled from gut microbiome data previously characterized. Each sample in D was passed through the MIPMLP-preprocessing, as above, with the “mean” merge method applied to the species level and a log-normalization. The 7 days post-FMT expected outcome of the transplant with each donor sample was computed using the pre-trained iMic model.A large cohort of candidate donors The donor cohort was run through the ideal donor identification model described above, and the donors were divided according to their expected post-FMT microbiome richness: 8 optimal adult donors and 16 sub-optimal donors, 8 of which were children  and 8 of which were adults  were selected. Then, a human-to-mouse FMT experiment was performed.Briefly, 48 Swiss Webster mice were raised under conventional conditions in the animal facility at the Azrieli Faculty of Medicine, Bar-Ilan University, with controlled temperature  by dividing the transplants only from adults based on the expected order frequencies. We only analyzed orders that were predicted with high accuracy Fig. A, step BFollowing microbiota characterization, recipients’ feces were collected at 8 weeks (8W), 10 weeks (10W), and 15 weeks (15W). DNA was extracted from all mice fecal samples, using the MagMAX Microbiome Ultra-Kit  according to the manufacturer’s instructions and following a 2-min bead beating step . The V4 region of the bacterial 16S rRNA gene was amplified by polymerase chain reaction (PCR) using the 515F (AATGATACGGCGACCACCGAGATCTACACGCT) barcoded and 806R (TATGGTAATTGTGTGYCAGCMGCCGCGGTAA) primers  with a fTo identify an ideal synthetic community for microbiota manipulation that would increase the richness and have a high probability of engraftment, we used a GA  to firstT-tests for pairwise comparisons.To evaluate the performance of our predictors, we calculated the R2 metric on external test sets using 10 cross-validations (CVs). We reported the average R2 score of the 10 runs. Then, we applied an ANOVA test to check whether the performances of the models were significantly different. If ANOVAs were significant, we used one-sided T-test.To evaluate the performance of our predictors, we calculated the SCC metric on the external test set using 10 CVs. We reported the average SCC of the 10 runs. Then, we applied an ANOVA test to check whether the performances of the models were significantly different. If the ANOVA were significant, we kept comparing by a one-sided T-test between the RF (random forest) model and the iMic model.To evaluate the performance of our predictors on the absence-presence species task, we calculated the AUC metric on the external test set. Then we applied a one-sided T-test.To assess the biological relevance of our donor-picking algorithm (according to the predicted Shannon diversity), we compared the predicted recipient richness of the clinically defined FMT “successes” and “failures” by applying a two-sided In a second scenario, we examined the actual distribution of conditions based on donor strain colonization in recipients undergoing FMT. To analyze this distribution, we utilized data from the 5 aforementioned conditions as well as additional data from the study conducted by Schmidt et al. . For eacTo investigate the relationship between the donor microbiome and FMT outcomes, we conducted an analysis of transplants involving the transfer of stool samples from human donors to either GF mice or humans across 12 different cohorts , the same transplant (stool of the same donor) was applied to multiple recipients Fig. B, C. TheDifferent time points within the same recipient (SDSR). Distances between the samples of a certain recipient at different time points post-FMT to measure the temporal variation in a recipient.Same donor - different recipients (SDDR). Distances between all the different recipients that received the FMT from the same donor at the same time point. This measures the effect of the recipient’s background on its microbiome.Different donors - different recipients (DDDR). Distances between recipients that received a transplant from different donors at the same time point. This adds the effect of the donor to the effect of the background.In the human-to-GF cohorts, there was no initial recipient microbiome. However, the recipient may have microbiome-independent factors affecting the outcome. In the human-to-human cohorts, to ensure that we really study the effect of recipient background vs donor microbiome and not basal differences in human recipients, we again compared the distances between the SDDR group and the DDDR group of the recipient after antibiotic treatment and before the FMT and there were no significant differences  than in all other groups  can be predicted using the donor sample composition, we tested 7 different multivariate predictors using the donor preprocessed ASVs , support vector machine regression (SVR), and Ridge regression that gave quite poor results similar to the low SCCs in the univariate analysis entclass1pt{minimaentclass1pt{minimaentclass1pt{minimaentclass1pt{minimap-value Similar results were obtained when predicting species’ relative abundances instead of orders. We predicted for the 50 most frequent species both the presence or absence of the species from the recipient samples 1-week post-FMT  and their relative abundances (in such a case the SCC was similarly reported). The average AUC of all the species was 0.8 +/− 0.11. For the species relative abundance predictions; the average SCC over 10 CVs of all the species was 0.52 +/− 0.16  to ensure that the results are not an artifact of a single experimental setup. To test the prediction accuracy between datasets, we ran iMic on a leave-one-dataset-out (LODO), where the whole Chemotherapy dataset (which contains the least number of samples) was not used during the training of the model. The model was trained on the merged 3 cohorts and was tested on the Chemotherapy dataset. The prediction accuracy was slightly lower than the mixed learning prediction, but much better than the univariate natural correlations , but defined the “high” and “low” groups according to the expected relative abundances of different orders as computed by iMic for all the orders properly predicted in the initial analysis . Again, the recipients’ order frequencies were measured in the mice stool samples at the age of 10 weeks. For all orders except for the Enterobacterials, the groups predicted to have a higher frequency for the appropriate order indeed had a higher frequency (To ensure that the difference is not the result of the recipient microbiome (after the antibiotics treatment), we repeated the analysis on the samples after antibiotic treatment and before FMT Fig. D–G 8W, wFollowing the prediction of the post-FMT microbiome properties, we checked whether the FMT clinical outcome can be predicted using only the donor microbiome. In this context, one must separate transplants in recipients with CDI and transplants in non-CDI recipients. In CDI FMT, there is a very high success fraction, so there is almost no need for an outcome prediction model , 64. We We first tested if the outcome was mainly determined by the donor properties. If that would be the case, all recipients receiving transplants from a particular donor would either consistently succeed or fail. We calculated the fraction of recipients for each donor group where the treatment succeeded. The results varied among conditions. For instance, colonization success , melanoma response to PD-1 therapy, and antibiotic resistance exhibited strong donor consistency, whereas other conditions showed very limited consistency Fig. A.Fig. 4CWe further employed the iMic model to predict the clinical conditions of recipients across various datasets, including WGS cohorts  accurately predicts improvement in clinical symptoms, we compared the predicted recipient post-FMT richness and the improvement in clinical outcome (“Success”) in different cohorts. The model consistently predicted higher Shannon diversity in all conditions for the group where the clinical outcome improved ―most of the recipient’s initial microbiome is destroyed and all mice grow in similar conditions.Antibiotic treated human (ABX)―most of the recipient’s initial microbiome is destroyed, but the recipients live in different conditions.Humans, with no antibiotic treatment―the recipient’s initial microbiome is intact, and the recipients live in different conditions.iMic managed to predict the recipient post-FMT outcome in the 4 groups with decreasing accuracy as the recipient microbiome becomes more important  and validated the prediction accuracy in a de-novo FMT experiment. The outcome predicted was either properties of the recipient’s post-FMT microbiome or improvement in clinical symptoms (for example response to IBD treatment as measured by the Mayo score). We further built a planned transplant of specific taxa while balancing between the number of non-zero taxa (cost and feasibility) and the quality of the optimization by using a GA.https://github.com/oshritshtossel/iMic_FMT and the trained algorithm (iMic) is available in the Drive at https://drive.google.com/file/d/1FIDy8uUBdv9Alj-xTe9Brkl5_QGBwamc/view?usp=sharing as well as in the To our knowledge, these are the first tools to propose a generic fully donor-based prediction for FMT success. Such algorithms can change the way FMT donor selection is performed―from randomly matching the donor or using a rational donor selection  to optimThe proposed models focused on the short-term post-FMT outcome. Longitudinal analyses in patients who have received FMT for recurrent CDI have shown an effect of FMT-induced microbiota alterations lasting anywhere from a few days to a few years after transfer , 82, 83.While off-the-shelf treatments require a fully donor-based transplant selection, the recipient microbiome and health have been shown to also affect the outcome. Here, we observed a clear hierarchy in the outcome prediction accuracy, where GF mice are more precisely predicted than ABX-treated mice and ABX-treated humans are more precisely predicted than non-treated humans.The danger of microbial toxicity may be solved via bacterial cocktails and personalized probiotics, in which we generate the transplant from scratch , 86, 87.https://drive.google.com/file/d/1FIDy8uUBdv9Alj-xTe9Brkl5_QGBwamc/view?usp=sharing and as a To summarize, we have here proposed 2 alternative clinical scenarios. The first case is the choice among a set of existing donors for the specific donors optimizing the transplant outcome. The second is the possibility of generating a microbial “Soup” with a limited number of microbes. While the second solution may be safer and more efficient, the first one is probably more amenable to clinical use with current practices. The trained algorithm (iMic) is available in the Drive at The donor’s phenotype differs from the recipient’s phenotype. However, the recipient’s future properties a week post-FMT in GF mice and for a period of up to 24 weeks post-FMT in humans can be predicted from the donor’s microbiome solely by using our prediction tool. We further proposed another tool to optimize the optimal transplant . By using our predictor and a GA, one can control the balance between the number of taxa to transplant and the targeted outcome. We validated our predictor using a de-novo FMT experiment highlighting the possibility to choose transplants that optimize the required goals. Our tools may change the current FMT protocols both on transplants from existing donors as well as planned transplants (from scratch).Additional file 1.Additional file 2.