Furin-Catalyzed Enhanced Magnetic Resonance Imaging Probe for Differential Diagnosis of Malignant Breast Cancers.

Molecular magnetic resonance imaging (mMRI) of biomarkers is essential for accurate cancer detection in precision medicine. However, the current clinically used contrast agents provide structural magnetic resonance imaging (sMRI) information only and rarely provide mMRI information. Here, a tumor-specific furin-catalyzed nanoprobe (NP) was reported for differential diagnosis of malignant breast cancers (BCs) in vivo. This NP with a compact structure of Fe3O4@Gd-DOTA NPs (FFG NPs) contains an "always-on" T2-weighted MR signal provided by the magnetic Fe3O4 core and a furin-catalyzed enhanced T1-weighted MR signal provided by the Gd-DOTA moiety. The FFG NPs were found to produce an activated T1 signal in the presence of furin catalysis and an "always-on" T2 signal, providing mMRI and sMRI information simultaneously. Ratiometric mMRI:sMRI intensity can be used for differential diagnosis of malignant BCs MDA-MB-231 and MCF-7, where the furin levels relatively differ. The proposed probe not only provides structural imaging but also enables real-time molecular differential visualization of BC through enzymatic activities of cancer tissues.

Combustion Characteristics of Vat-Photopolymerized Fuels with a Spiral Star Port for Hybrid Propulsion.

Despite hybrid rocket motors offering distinct advantages over solid or liquid rocket motors, their low regression rate and insufficient combustion efficiency remain significantly unimproved. This study focuses on the effects of the helix lead on the regression rate distribution and combustion efficiency of vat-polymerized fuel grains with a spiral star port for a hybrid rocket. Both experimental and numerical investigations were conducted to study the combustion characteristics and regression rate distribution of three-dimensional (3D) print grains. Spiral star grains with varying helix leads of 60, 90, and 120 mm were fabricated using light-curing 3D printing technology. A 3D simulation model was developed to obtain the temperature distribution, species mass distribution, and combustion efficiency. Furthermore, firing tests were performed on a two-dimensional radial hybrid combustion test stand to measure the regression rate. Digital image processing of computed tomography images was used to determine the regression rate. Simulation results indicated that the spiral star grain port helps to improve the combustion efficiency compared with those seen with round tube and straight star port grains. With an increase in the axial distance, the flame zone gradually shrinks, and the smaller the helix lead, the faster the shrinkage. At a mass flow rate of 1.50 g/s for oxygen, the regression rate of the spiral star grains is significantly higher than that of the straight star grain and the conventional round tubular grains, and the regression rate gradually increases with a decrease in the helix lead. This finding is expected to solve the problem of the low regression rate of solid fuels with spiral star pore-shaped grains prepared by the light-curing 3D printing method.

Chemical Potentials and the One-Electron Hamiltonian of the Second-Order Perturbation Theory from the Functional Derivative Approach.

We develop a functional derivative approach to calculate the chemical potentials of second-order perturbation theory (MP2). In the functional derivative approach, the correlation part of the MP2 chemical potential, which is the derivative of the MP2 correlation energy with respect to the occupation number of frontier orbitals, is obtained from the chain rule via the noninteracting Green's function. First, the MP2 correlation energy is expressed in terms of the noninteracting Green's function, and its functional derivative to the noninteracting Green's function is the second-order self-energy. Then, the derivative of the noninteracting Green's function to the occupation number is obtained by including the orbital relaxation effect. We show that the MP2 chemical potentials obtained from the functional derivative approach agree with that obtained from the finite difference approach. The one-electron Hamiltonian, defined as the derivative of the MP2 energy with respect to the one particle density matrix, is also derived using the functional derivative approach, which can be used in the self-consistent calculations of MP2 and double-hybrid density functionals. The developed functional derivative approach is promising for calculating the chemical potentials and the one-electron Hamiltonian of approximate functionals and many-body perturbation approaches dependent explicitly on the noninteracting Green's function.

Development of a machine learning finite-range nonlocal density functional.

Kohn-Sham density functional theory has been the most popular method in electronic structure calculations. To fulfill the increasing accuracy requirements, new approximate functionals are needed to address key issues in existing approximations. It is well known that nonlocal components are crucial. Current nonlocal functionals mostly require orbital dependence such as in Hartree-Fock exchange and many-body perturbation correlation energy, which, however, leads to higher computational costs. Deviating from this pathway, we describe functional nonlocality in a new approach. By partitioning the total density to atom-centered local densities, a many-body expansion is proposed. This many-body expansion can be truncated at one-body contributions, if a base functional is used and an energy correction is approximated. The contribution from each atom-centered local density is a single finite-range nonlocal functional that is universal for all atoms. We then use machine learning to develop this universal atom-centered functional. Parameters in this functional are determined by fitting to data that are produced by high-level theories. Extensive tests on several different test sets, which include reaction energies, reaction barrier heights, and non-covalent interaction energies, show that the new functional, with only the density as the basic variable, can produce results comparable to the best-performing double-hybrid functionals, (for example, for the thermochemistry test set selected from the GMTKN55 database, BLYP based machine learning functional gives a weighted total mean absolute deviations of 3.33 kcal/mol, while DSD-BLYP-D3(BJ) gives 3.28 kcal/mol) with a lower computational cost. This opens a new pathway to nonlocal functional development and applications.

Perdew Festschrift editorial.

This Special Issue of the Journal of Chemical Physics is dedicated to the work and life of John P. Perdew. A short bio is available within the issue [J. P. Perdew, J. Chem. Phys. 160, 010402 (2024)]. Here, we briefly summarize key publications in density functional theory by Perdew and his collaborators, followed by a structured guide to the papers contributed to this Special Issue.

Potential Association of Gut Microbial Metabolism and Circulating mRNA Based on Multiomics Sequencing Analysis in Fetal Growth Restriction.

Objective: Fetal growth restriction (FGR) is a significant contributor to negative pregnancy and postnatal developmental outcomes. Currently, the exact pathological mechanism of FGR remains unknown. This study aims to utilize multiomics sequencing technology to investigate potential relationships among mRNA, gut microbiota, and metabolism in order to establish a theoretical foundation for diagnosing and understanding the molecular mechanisms underlying FGR. Methods: In this study, 11 healthy pregnant women and nine pregnant women with FGR were divided into Control group and FGR group based on the health status. Umbilical cord blood, maternal serum, feces, and placental tissue samples were collected during delivery. RNA sequencing, 16S rRNA sequencing, and metabolomics methods were applied to analyze changes in umbilical cord blood circulating mRNA, fecal microbiota, and metabolites. RT-qPCR, ELISA, or western blot were used to detect the expression of top 5 differential circulating mRNA in neonatal cord blood, maternal serum, or placental tissue samples. Correlation between differential circulating mRNA, microbiota, and metabolites was analyzed by the Spearman coefficient. Results: The top 5 mRNA genes in FGR were altered with the downregulation of TRIM34, DEFA3, DEFA1B, DEFA1, and QPC, and the upregulation of CHPT1, SMOX, FAM83A, GDF15, and NAPG in newborn umbilical cord blood, maternal serum, and placental tissue. The abundance of Bacteroides, Akkermansia, Eubacterium_coprostanoligenes_group, Phascolarctobacterium, Parasutterella, Odoribacter, Lachnospiraceae_UCG_010, and Dielma were significantly enriched in the FGR group. Metabolites such as aspartic acid, methionine, alanine, L-tryptophan, 3-methyl-2-oxovalerate, and ketoleucine showed notable functional alterations. Spearman correlation analysis indicated that metabolites like methionine and alanine, microbiota (Tyzzerella), and circulating mRNA (TRIM34, SMOX, FAM83A, NAPG) might play a role as mediators in the communication between the gut and circulatory system interaction in FGR. Conclusion: Metabolites (METHIONINE, alanine) as well as microbiota (Tyzzerella) and circulating mRNA (TRIM34, SMOX, FAM83A, NAPG) were possible mediators that communicated the interaction between the gut and circulatory systems in FGR.

Biomimetic Molybdenum Sulfide-Catalyzed Tumor Ferroptosis and Bioimaging.

The chemical generation of singlet oxygen (1 O2 ) by the MoO4 2- -catalyzed disproportionation of hydrogen peroxide (H2 O2 ) has been widely applied in numerous catalytic processes; however, such molybdate ions cannot be administered for redox-based cancer therapeutics. This work reports the albumin-mediated biomimetic synthesis of highly active molybdenum sulfide (denoted MoB) nanocatalysts that mediate the simultaneous generation of 1 O2 and superoxide anion (O2 •- ) from H2 O2 , which is relatively abundant in malignant tumors. The MoB-catalyzed reactive oxygen species (ROS) are able to activate the ferroptosis pathway and cause lipid peroxidation for efficient cancer therapy. Furthermore, for the first time, the catalytic activity of MoB is visualized in situ. Moreover, a catalytic imaging modality based on MoB is developed for specific imaging of inflammation diseases without background interference. Therefore, this study presents a biomimetic strategy toward Mo-based nanocatalysts for ROS-facilitated tumor ferroptosis and catalytic imaging.

Linear Scaling Calculations of Excitation Energies with Active-Space Particle-Particle Random-Phase Approximation.

We developed an efficient active-space particle-particle random-phase approximation (ppRPA) approach to calculate accurate charge-neutral excitation energies of molecular systems. The active-space ppRPA approach constrains both indexes in particle and hole pairs in the ppRPA matrix, which only selects frontier orbitals with dominant contributions to low-lying excitation energies. It employs the truncation in both orbital indexes in the particle-particle and the hole-hole spaces. The resulting matrix, whose eigenvalues are excitation energies, has a dimension that is independent of the size of the systems. The computational effort for the excitation energy calculation, therefore, scales linearly with system size and is negligible compared with the ground-state calculation of the (N - 2)-electron system, where N is the electron number of the molecule. With the active space consisting of 30 occupied and 30 virtual orbitals, the active-space ppRPA approach predicts the excitation energies of valence, charge-transfer, Rydberg, double, and diradical excitations with the mean absolute errors (MAEs) smaller than 0.03 eV compared with the full-space ppRPA results. As a side product, we also applied the active-space ppRPA approach in the renormalized singles (RS) T-matrix approach. Combining the non-interacting pair approximation that approximates the contribution to the self-energy outside the active space, the active-space GRSTRS@PBE approach predicts accurate absolute and relative core-level binding energies with the MAEs around 1.58 and 0.3 eV, respectively. The developed linear scaling calculation of excitation energies is promising for applications to large and complex systems.

Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein.

Molecular dynamics (MD) is an extremely powerful, highly effective, and widely used approach to understanding the nature of chemical processes in atomic details for proteins. The accuracy of results from MD simulations is highly dependent on force fields. Currently, molecular mechanical (MM) force fields are mainly utilized in MD simulations because of their low computational cost. Quantum mechanical (QM) calculation has high accuracy, but it is exceedingly time consuming for protein simulations. Machine learning (ML) provides the capability for generating accurate potential at the QM level without increasing much computational effort for specific systems that can be studied at the QM level. However, the construction of general machine learned force fields, needed for broad applications and large and complex systems, is still challenging. Here, general and transferable neural network (NN) force fields based on CHARMM force fields, named CHARMM-NN, are constructed for proteins by training NN models on 27 fragments partitioned from the residue-based systematic molecular fragmentation (rSMF) method. The NN for each fragment is based on atom types and uses new input features that are similar to MM inputs, including bonds, angles, dihedrals, and non-bonded terms, which enhance the compatibility of CHARMM-NN to MM MD and enable the implementation of CHARMM-NN force fields in different MD programs. While the main part of the energy of the protein is based on rSMF and NN, the nonbonded interactions between the fragments and with water are taken from the CHARMM force field through mechanical embedding. The validations of the method for dipeptides on geometric data, relative potential energies, and structural reorganization energies demonstrate that the CHARMM-NN local minima on the potential energy surface are very accurate approximations to QM, showing the success of CHARMM-NN for bonded interactions. However, the MD simulations on peptides and proteins indicate that more accurate methods to represent protein-water interactions in fragments and non-bonded interactions between fragments should be considered in the future improvement of CHARMM-NN, which can increase the accuracy of approximation beyond the current mechanical embedding QM/MM level.

SENP5 deteriorates traumatic brain injury via SUMO2-dependent suppression of E2F1 SUMOylation.

Traumatic brain injury (TBI) represents a main public health concern during the past decade, attracting considerable interest because of its rising prevalence, wide-ranging risk factors and lifelong familial and societal influence. SUMO2 can conjugate to substrates upon various cellular stresses. Nevertheless, whether and how SUMO2-specific proteases partake in TBI is less understood. The aim of this study is to dissect the effects of SUMO-specific peptidase 5 (SENP5) on accentuating TBI in rats in an effort to unveil its underlying mechanism. SENP5 is overexpressed in hippocampal tissues of TBI rats, and inhibition of SENP5 reduces neurological function scores, decreases brain water content, inhibits apoptosis in hippocampal tissues, and attenuates brain injury caused in rats. Moreover, SENP5 inhibits the SUMOylation level of E2F transcription factor 1 (E2F1) and increases the protein expression of E2F1. Silencing of E2F1 blocks the p53 signaling pathway. Overexpression of E2F1 partially reverses the protective effect of sh-SENP5 on TBI in rats. These findings reveal an essential role of SENP5 and the SUMOylation status of E2F1 in the TBI development.

Biosensors based on potent miniprotein binder for sensitive testing of SARS-CoV­2 variants of concern.

The miniprotein binder TRI2-2 was employed as an antibody alternative to build a single antibody-coupled TRI2-2 based gold nanoparticle-based lateral flow immunoassay (AT-GLFIA) biosensor. The biosensor provides high specificity and affinity binding between TRI2-2 and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) spike antigen receptor binding domain (S-RBD). It also enables rapid testing of wild-type (WT), B.1.1.7 (Alpha), B.1.351 (Beta), B.1.617.2 (Delta), P.1 (Gamma), and B.1.1.529 (Omicron) SARS-CoV-2 S-RBD and is at least ~ 16-fold more sensitive than conventional antibody pair-based GLFIA (AP-GLFIA). Besides, we developed a wireless micro-electrochemical assay (WMECA) biosensor based on the TRI2-2, which demonstrates an excellent VOCs testing capability at the pg mL-1 level. Overall, our results demonstrate that integrating miniprotein binders into conventional immunoassay systems is a promising design for improving the testing capabilities of such systems without hard-to-obtain antibody pair, complex reporter design, laborious signal amplification strategies, or specific instrumentation.

ROS-Targeted Depression Therapy via BSA-Incubated Ceria Nanoclusters.

Depression is one of the most fatal mental diseases, and there is currently a lack of efficient drugs for the treatment of depression. Emerging evidence has indicated oxidative stress as a key pathological feature of depression. We targeted reactive oxygen species (ROS) and synthesized CeO2@BSA nanoclusters as a novel antidepression nanodrug via a convenient, green, and highly effective bovine serum albumin (BSA) incubation strategy. CeO2@BSA has ultrasmall size (2 nm) with outstanding ROS scavenging and blood-brain barrier crossing capacity, rapid metabolism, and negligible adverse effects in vitro and in vivo. CeO2@BSA administration alleviates depressive behaviors and depression-related pathological changes of the chronic restraint stress-induced depressive model, suggesting promising therapeutic effects of CeO2@BSA for the treatment of depression. Our study proved the validity by directly using nanodrugs as antidepression drugs instead of using them as a nanocarrier, which greatly expands the application of nanomaterials in depression treatment.

Specific-Tuning Band Structure in Hetero-Semiconductor Nanorods to Match with Reduction of Oxygen Molecules for Low-Intensity Yet Highly Effective Sonodynamic/Hole Therapy of Tumors.

Sonosensitizers-assisted sonodynamic therapy (SDT) has been emerging as a promising treatment for cancers, and yet few specific regulations of band structure of sonosensitizers have been reported in relation to oxygen in tissues. Herein, by a gradient doping technique to modulate the band structure of hetero-semiconductor nanorods, it is found that the reduction potential of band-edge is very critical to reactive oxygen species (ROS) production under low-intensity ultrasound (US) irradiation and particularly, when aligned with the reduction of oxygen, ROS generation is found to be most significantly enhanced. Withal, US-generated oxidation holes are found to be effective in consuming overexpressed glutathione in tumor lesions, which amplifies cellular oxidative stress and finally induces tumor cell death. Moreover, the intrinsic fluorescence property of semiconductors provides imaging capability to illumine tumor area and guide the SDT process. This study demonstrates that the reduction potential state of sonosensitizers is of crucial importance in ROS generation and the proposed reduction potential-tailored hetero-semiconductor nanorods materialize low-intensity US irradiation yet highly effective SDT and synergetic hole therapy of tumors with imaging guidance and reduced radiation injury.

Oxygen-Deficient BiOCl Combined with L-Buthionine-Sulfoximine Synergistically Suppresses Tumor Growth through Enhanced Singlet Oxygen Generation under Ultrasound Irradiation.

Excess generation of reactive oxygen species (ROS) based on sensitizers under ultrasound (US) excitation can cause the death of tumor cells via oxidative damage, but sonosensitizers are largely unexplored. Herein, oxygen-deficient black BiOCl (B-BiOCl) nanoplates (NPs) are reported, with post-treatment on conventional BiOCl by simple UV excitation, showing stronger singlet oxygen (1 O2 ) generation than commercial TiO2 nanoparticles and their derivatives under US irradiation. Moreover, L-buthionine-sulfoximine (BSO), a GSH biosynthesis inhibitor, is incorporated into B-BiOCl NPs. The authors find that BSO can be released owing to the degradation of B-BiOCl NPs in the presence of acid and GSH, which are overexpressed in tumors. The results show that BSO/B-BiOCl-PEG NPs have a multifunctional synergistic effect on improving ROS production. In particular, BiOCl has remarkable near-infrared light absorption after UV treatment and is good for photoacoustic imaging that can guide subsequent sonodynamic therapy. This work shows that just with a simple oxygen deficiency treatment, strong 1 O2 generation can be provided to a conventional material under US irradiation and, interestingly, this effect can be amplified by using a small inhibitor BSO, and this is clearly demonstrated in cell and mice experiments.

Mechanical Unfolding and Refolding of NanoLuc via Single-Molecule Force Spectroscopy and Computer Simulations.

A highly bioluminescent protein, NanoLuc (Nluc), has seen numerous applications in biological assays since its creation. We recently engineered a NanoLuc polyprotein that showed high bioluminescence but displayed a strong misfolding propensity after mechanical unfolding. Here, we present our single-molecule force spectroscopy (SMFS) studies by atomic force microscopy (AFM) and steered molecular dynamics (SMD) simulations on two new hybrid protein constructs comprised of Nluc and I91 titin domains, I91-I91-Nluc-I91-I91-I91-I91 (I912-Nluc-I914) and I91-Nluc-I91-Nluc-I91-Nluc-I91, to characterize the unfolding behavior of Nluc in detail and to further investigate its misfolding properties that we observed earlier for the I912-Nluc3-I912 construct. Our SMFS results confirm that Nluc's unfolding proceeds similarly in all constructs; however, Nluc's refolding differs in these constructs, and its misfolding is minimized when Nluc is monomeric or separated by I91 domains. Our simulations on monomeric Nluc, Nluc dyads, and Nluc triads pinpointed the origin of its mechanical stability and captured interesting unfolding intermediates, which we also observed experimentally.

Reformulation of thermally assisted-occupation density functional theory in the Kohn-Sham framework.

We reformulate the thermally assisted-occupation density functional theory (TAO-DFT) into the Kohn-Sham single-determinant framework and construct two new post-self-consistent field (post-SCF) static correlation correction schemes, named rTAO and rTAO-1. In contrast to the original TAO-DFT with the density in an ensemble form, in which each orbital density is weighted with a fractional occupation number, the ground-state density is given by a single-determinant wavefunction, a regular Kohn-Sham (KS) density, and total ground state energy is expressed in the normal KS form with a static correlation energy formulated in terms of the KS orbitals. In post-SCF calculations with rTAO functionals, an efficient energy scanning to quantitatively determine θ is also proposed. The rTAOs provide a promising method to simulate systems with strong static correlation as original TAO, but simpler and more efficient. We show that both rTAO and rTAO-1 is capable of reproducing most results from TAO-DFT without the additional functional Eθ used in TAO-DFT. Furthermore, our numerical results support that, without the functional Eθ, both rTAO and rTAO-1 can capture correct static correlation profiles in various systems.

Combining localized orbital scaling correction and Bethe-Salpeter equation for accurate excitation energies.

We applied localized orbital scaling correction (LOSC) in Bethe-Salpeter equation (BSE) to predict accurate excitation energies for molecules. LOSC systematically eliminates the delocalization error in the density functional approximation and is capable of approximating quasiparticle (QP) energies with accuracy similar to or better than GW Green's function approach and with much less computational cost. The QP energies from LOSC, instead of commonly used G0W0 and evGW, are directly used in BSE. We show that the BSE/LOSC approach greatly outperforms the commonly used BSE/G0W0 approach for predicting excitations with different characters. For the calculations of Truhlar-Gagliardi test set containing valence, charge transfer, and Rydberg excitations, BSE/LOSC with the Tamm-Dancoff approximation provides a comparable accuracy to time-dependent density functional theory (TDDFT) and BSE/evGW. For the calculations of Stein CT test set and Rydberg excitations of atoms, BSE/LOSC considerably outperforms both BSE/G0W0 and TDDFT approaches with a reduced starting point dependence. BSE/LOSC is, thus, a promising and efficient approach to calculate excitation energies for molecular systems.

Insulin-incubated palladium clusters promote recovery after brain injury.

Traumatic brain injury (TBI) is a cause of disability and death worldwide, but there are currently no specific treatments for this condition. Release of excess reactive oxygen species (ROS) in the injured brain leads to a series of pathological changes; thus, eliminating ROS could be a potential therapeutic strategy. Herein, we synthesized insulin-incubated ultrasmall palladium (Pd@insulin) clusters via green biomimetic chemistry. The Pd@insulin clusters, which were 3.2 nm in diameter, exhibited marked multiple ROS-scavenging ability testified by the theoretical calculation. Pd@insulin could be rapidly excreted via kidney-urine metabolism and induce negligible adverse effects after a long-time treatment in vivo. In a TBI mouse model, intravenously injected Pd@insulin clusters aggregated in the injured cortex, effectively suppressed excessive ROS production, and significantly rescued motor function, cognition and spatial memory. We found that the positive therapeutic effects of the Pd@insulin clusters were mainly attributed to their ROS-scavenging ability, as they inhibited excessive neuroinflammation, reduced cell apoptosis, and prevented neuronal loss. Therefore, the ability of Pd@insulin clusters to effectively eliminate ROS, as well as their simple structure, easy synthesis, low toxicity, and rapid metabolism may facilitate their clinical translation for TBI treatment.

Mechanism of Reduction of an Aminyl Radical Intermediate in the Radical SAM GTP 3',8-Cyclase MoaA.

The diversity of the reactions catalyzed by radical S-adenosyl-l-methionine (SAM) enzymes is achieved at least in part through the variety of mechanisms to quench their radical intermediates. In the SPASM-twitch family, the largest family of radical SAM enzymes, the radical quenching step is thought to involve an electron transfer to or from an auxiliary 4Fe-4S cluster in or adjacent to the active site. However, experimental demonstration of such functions remains limited. As a representative member of this family, MoaA has one radical SAM cluster ([4Fe-4S]RS) and one auxiliary cluster ([4Fe-4S]AUX), and catalyzes a unique 3',8-cyclization of GTP into 3',8-cyclo-7,8-dihydro-GTP (3',8-cH2GTP) in the molybdenum cofactor (Moco) biosynthesis. Here, we report a mechanistic investigation of the radical quenching step in MoaA, a chemically challenging reduction of 3',8-cyclo-GTP-N7 aminyl radical. We first determined the reduction potentials of [4Fe-4S]RS and [4Fe-4S]AUX as -510 mV and -455 mV, respectively, using a combination of protein film voltammogram (PFV) and electron paramagnetic resonance (EPR) spectroscopy. Subsequent Q-band EPR characterization of 5'-deoxyadenosine C4' radical (5'-dA-C4'•) trapped in the active site revealed isotropic exchange interaction (∼260 MHz) between 5'-dA-C4'• and [4Fe-4S]AUX1+, suggesting that [4Fe-4S]AUX is in the reduced (1+) state during the catalysis. Together with density functional theory (DFT) calculation, we propose that the aminyl radical reduction proceeds through a proton-coupled electron transfer (PCET), where [4Fe-4S]AUX serves as an electron donor and R17 residue acts as a proton donor. These results provide detailed mechanistic insights into the radical quenching step of radical SAM enzyme catalysis.

Gut microbiota changes in preeclampsia, abnormal placental growth and healthy pregnant women.

BACKGROUND: Preeclampsia (PE) is a condition of high blood pressure that is usually concurrent with proteinuria in pregnancy. PE complicates the management of both maternal and fetal health and contributes to most adverse pregnancy outcomes, but the mechanism underlying the development of PE remains unclear. In this study, we performed a case-control study to compare the gut microbiota of PE (n = 26), abnormal placental growth (APG, n = 25) and healthy pregnant women (n = 28) and analyzed the potential pathogenic role of gut microbiota in PE progression. RESULTS: The clinical pathophysiological state did not affect the bacterial diversity, while the compositions of the gut microbiota were significantly altered in both the PE and APG groups compared with healthy pregnant women. At the phylum level, TM7 was significantly increased in women with APG. Heterogeneity was observed at the genus level, especially in genera with positive LDA scores, suggesting the stage-dependent effect of gut microbiota on the development of PE. The beneficial bacterium Lactobacillus was markedly depleted in the PE and APG groups but was only correlated with blood pressure (BP) and proteinuria levels in the PE group. Two different bacterial taxa belonged to Lactobacillus showed different correlations (OTU255 and OTU784 were significantly related to PE and APG, respectively). CONCLUSIONS: Our results indicated that shifts in the gut microbiota might occur from the early stages of the development of PE, which is of possible etiological and therapeutic importance.