Genomic insights into the cellular specialization of predation in raptorial protists.

BACKGROUND: Predation is a fundamental mechanism for organisms to acquire energy, and various species have evolved diverse tools to enhance their hunting abilities. Among protozoan predators, raptorial Haptorian ciliates are particularly fascinating as they possess offensive extrusomes known as toxicysts, which are rapidly discharged upon prey contact. However, our understanding of the genetic processes and specific toxins involved in toxicyst formation and discharge is still limited. RESULTS: In this study, we investigated the predation strategies and subcellular structures of seven Haptoria ciliate species and obtained their genome sequences using single-cell sequencing technology. Comparative genomic analysis revealed distinct gene duplications related to membrane transport proteins and hydrolytic enzymes in Haptoria, which play a crucial role in the production and discharge of toxicysts. Transcriptomic analysis further confirmed the abundant expression of genes related to membrane transporters and cellular toxins in Haptoria compared to Trichostomatia. Notably, polyketide synthases (PKS) and L-amino acid oxidases (LAAO) were identified as potentially toxin genes that underwent extensive duplication events in Haptoria. CONCLUSIONS: Our results shed light on the evolutionary and genomic adaptations of Haptorian ciliates for their predation strategies in evolution and provide insights into their toxic mechanisms.

Distinct molecular profiles drive multifaceted characteristics of colorectal cancer metastatic seeds.

Metastasis of primary tumors remains a challenge for early diagnosis and prevention. The cellular properties and molecular drivers of metastatically competent clones within primary tumors remain unclear. Here, we generated 10-16 single cell-derived lines from each of three colorectal cancer (CRC) tumors to identify and characterize metastatic seeds. We found that intrinsic factors conferred clones with distinct metastatic potential and cellular communication capabilities, determining organ-specific metastasis. Poorly differentiated or highly metastatic clones, rather than drug-resistant clones, exhibited poor clinical prognostic impact. Personalized genetic alterations, instead of mutation burden, determined the occurrence of metastatic potential during clonal evolution. Additionally, we developed a gene signature for capturing metastatic potential of primary CRC tumors and demonstrated a strategy for identifying metastatic drivers using isogenic clones with distinct metastatic potential in primary tumors. This study provides insight into the origin and mechanisms of metastasis and will help develop potential anti-metastatic therapeutic targets for CRC patients.

CIRI-Deep Enables Single-Cell and Spatial Transcriptomic Analysis of Circular RNAs with Deep Learning.

Circular RNAs (circRNAs) are a crucial yet relatively unexplored class of transcripts known for their tissue- and cell-type-specific expression patterns. Despite the advances in single-cell and spatial transcriptomics, these technologies face difficulties in effectively profiling circRNAs due to inherent limitations in circRNA sequencing efficiency. To address this gap, a deep learning model, CIRI-deep, is presented for comprehensive prediction of circRNA regulation on diverse types of RNA-seq data. CIRI-deep is trained on an extensive dataset of 25 million high-confidence circRNA regulation events and achieved high performances on both test and leave-out data, ensuring its accuracy in inferring differential events from RNA-seq data. It is demonstrated that CIRI-deep and its adapted version enable various circRNA analyses, including cluster- or region-specific circRNA detection, BSJ ratio map visualization, and trans and cis feature importance evaluation. Collectively, CIRI-deep's adaptability extends to all major types of RNA-seq datasets including single-cell and spatial transcriptomic data, which will undoubtedly broaden the horizons of circRNA research.

Spatiotemporal patterns of the pregnancy microbiome and links to reproductive disorders.

The microbiome of females undergoes extensive remodeling during pregnancy, which is likely to have an impact on the health of both mothers and offspring. Nevertheless, large-scale integrated investigations characterizing microbiome dynamics across key body habitats are lacking. Here, we performed an extensive meta-analysis that compiles and analyzes microbiome profiles from  >10,000 samples across the gut, vagina, and oral cavity of pregnant women from diverse geographical regions. We have unveiled unexpected variations in the taxonomic, functional, and ecological characteristics of microbial communities throughout the course of pregnancy. The gut microbiota showed distinct trajectories between Western and non-Western populations. The vagina microbiota exhibited fluctuating transitions at the genus level across gestation, while the oral microbiota remained relatively stable. We also identified distinctive microbial signatures associated with prevalent pregnancy-related disorders, including opposite variations in the oral and gut microbiota of patients with gestational diabetes and disrupted microbial networks in preterm birth. This study establishes a comprehensive atlas of the pregnancy microbiome by integrating multidimensional datasets and offers foundational insights into the intricate interplay between microbes and host factors that underlie reproductive health.

SEVtras delineates small extracellular vesicles at droplet resolution from single-cell transcriptomes.

Small extracellular vesicles (sEVs) are emerging as pivotal players in a wide range of physiological and pathological processes. However, a pressing challenge has been the lack of high-throughput techniques capable of unraveling the intricate heterogeneity of sEVs and decoding the underlying cellular behaviors governing sEV secretion. Here we leverage droplet-based single-cell RNA sequencing (scRNA-seq) and introduce an algorithm, SEVtras, to identify sEV-containing droplets and estimate the sEV secretion activity (ESAI) of individual cells. Through extensive validations on both simulated and real datasets, we demonstrate SEVtras' efficacy in capturing sEV-containing droplets and characterizing the secretion activity of specific cell types. By applying SEVtras to four tumor scRNA-seq datasets, we further illustrate that the ESAI can serve as a potent indicator of tumor progression, particularly in the early stages. With the increasing importance and availability of scRNA-seq datasets, SEVtras holds promise in offering valuable extracellular insights into the cell heterogeneity.

circAtlas 3.0: a gateway to 3 million curated vertebrate circular RNAs based on a standardized nomenclature scheme.

Recent studies have demonstrated the important regulatory role of circRNAs, but an in-depth understanding of the comprehensive landscape of circRNAs across various species still remains unexplored. The current circRNA databases are often species-restricted or based on outdated datasets. To address this challenge, we have developed the circAtlas 3.0 database, which contains a rich collection of 2674 circRNA sequencing datasets, curated to delineate the landscape of circRNAs within 33 distinct tissues spanning 10 vertebrate species. Notably, circAtlas 3.0 represents a substantial advancement over its precursor, circAtlas 2.0, with the number of cataloged circRNAs escalating from 1 007 087 to 3 179 560, with 2 527 528 of them being reconstructed into full-length isoforms. circAtlas 3.0 also introduces several notable enhancements, including: (i) integration of both Illumina and Nanopore sequencing datasets to detect circRNAs of extended lengths; (ii) employment of a standardized nomenclature scheme for circRNAs, providing information of the host gene and full-length circular exons; (iii) inclusion of clinical cancer samples to explore the biological function of circRNAs within the context of cancer and (iv) links to other useful resources to enable user-friendly analysis of target circRNAs. The updated circAtlas 3.0 provides an important platform for exploring the evolution and biological implications of vertebrate circRNAs, and is freely available at http://circatlas.biols.ac.cn and https://ngdc.cncb.ac.cn/circatlas.

Gut microbiome as a key monitoring indicator for reintroductions of captive animals.

Reintroduction programs seek to restore degraded populations and reverse biodiversity loss. To examine the hypothesis that gut symbionts could be used as an indicator of reintroduction success, we performed intensive metagenomic monitoring over 10 years to characterize the ecological succession and adaptive evolution of the gut symbionts of captive giant pandas reintroduced to the wild. We collected 63 fecal samples from 3 reintroduced individuals and 22 from 9 wild individuals and used 96 publicly available samples from another 3 captive individuals. By microbial composition analysis, we identified 3 community clusters of the gut microbiome (here termed enterotypes) with interenterotype succession that was closely related to the reintroduction process. Each of the 3 enterotypes was identified based on significant variation in the levels of 1 of 3 genera: Clostridium, Pseudomonas, and Escherichia. The enterotype of captive pandas was Escherichia. This enterotype was gradually replaced by the Clostridium enterotype during the wild-training process, which in turn was replaced by the Pseudomonas enterotype that resembled the enterotype of wild pandas, an indicator of conversion to wildness and a successful reintroduction. We also isolated 1 strain of Pseudomonas protegens from the wild enterotype, a previously reported free-living microbe, and found that its within-host evolution contributed to host dietary adaptation in the wild. Monitoring gut microbial structure provides a novel, noninvasive tool that can be used as an indicator of successful reintroduction of a captive individual to the wild.

Microbiomas intestinales como indicadores clave de monitoreo para la reintroducción de animales cautivos Resumen Los programas de reintroducción buscan restaurar las poblaciones degradadas y revertir la pérdida de la biodiversidad. Realizamos un monitoreo metagenómico intensivo durante más de diez años para caracterizar la sucesión ecológica y la evolución adaptativa de los simbiontes intestinales de pandas reintroducidos en la naturaleza y así comprobar la hipótesis de que estos simbiontes pueden usarse como indicadores de una reintroducción exitosa. Recolectamos 63 muestras fecales de tres individuos reintroducidos y 22 de nueve individuos silvestres y usamos 96 muestras disponibles al público de otros tres individuos cautivos. Mediante el análisis de la composición microbiana identificamos tres grupos comunitarios del microbioma intestinal (denominados como enterotipos) con una sucesión entre enterotipos relacionada cercanamente al proceso de reintroducción. Identificamos cada uno de los tres enterotipos con base en la variación significativa en los niveles de uno de los tres géneros: Clostridium, Pseudomonas, y Escherichia. El enterotipo de los pandas cautivos fue Escherichia. A este enterotipo lo reemplazó gradualmente el enterotipo de Clostridium durante el proceso de adaptación a la naturaleza, y a su vez fue reemplazado por el enterotipo de Pseudomonas similar al de los pandas silvestres, un indicador de la conversión a la vida silvestre y de una reintroducción exitosa. También aislamos una cepa de Pseudomonas protegens del enterotipo silvestre, un microbio reportado previamente como de vida libre, y descubrimos que su evolución dentro del hospedero contribuyó a que este se adaptara a la naturaleza de la dieta. El monitoreo de la estructura microbiana intestinal proporciona una herramienta novedosa y no invasiva que puede usarse como indicador del éxito de la reintroducción de un individuo cautivo a la naturaleza.

MAMI: a comprehensive database of mother-infant microbiome and probiotic resources.

Extensive evidence has demonstrated that the human microbiome and probiotics confer great impacts on human health, particularly during critical developmental stages such as pregnancy and infancy when microbial communities undergo remarkable changes and maturation. However, a major challenge in understanding the microbial community structure and interactions between mothers and infants lies in the current lack of comprehensive microbiome databases specifically focused on maternal and infant health. To address this gap, we have developed an extensive database called MAMI (Microbiome Atlas of Mothers and Infants) that archives data on the maternal and neonatal microbiome, as well as abundant resources on edible probiotic strains. By leveraging this resource, we can gain profound insights into the dynamics of microbial communities, contributing to lifelong wellness for both mothers and infants through precise modulation of the developing microbiota. The functionalities incorporated into MAMI provide a unique perspective on the study of the mother-infant microbiome, which not only advance microbiome-based scientific research but also enhance clinical practice. MAMI is publicly available at https://bioinfo.biols.ac.cn/mami/.

PandaGUT provides new insights into bacterial diversity, function, and resistome landscapes with implications for conservation.

BACKGROUND: The gut microbiota play important roles in host adaptation and evolution, but are understudied in natural population of wild mammals. To address host adaptive evolution and improve conservation efforts of threatened mammals from a metagenomic perspective, we established a high-quality gut microbiome catalog of the giant panda (pandaGUT) to resolve the microbiome diversity, functional, and resistome landscapes using approximately 7 Tbp of long- and short-read sequencing data from 439 stool samples. RESULTS: The pandaGUT catalog comprises 820 metagenome-assembled genomes, including 40 complete closed genomes, and 64.5% of which belong to species that have not been previously reported, greatly expanding the coverage of most prokaryotic lineages. The catalog contains 2.37 million unique genes, with 74.8% possessing complete open read frames, facilitating future mining of microbial functional potential. We identified three microbial enterotypes across wild and captive panda populations characterized by Clostridium, Pseudomonas, and Escherichia, respectively. We found that wild pandas exhibited host genetic-specific microbial structures and functions, suggesting host-gut microbiota phylosymbiosis, while the captive cohorts encoded more multi-drug resistance genes. CONCLUSIONS: Our study provides largely untapped resources for biochemical and biotechnological applications as well as potential intervention avenues via the rational manipulation of microbial diversity and reducing antibiotic usage for future conservation management of wildlife. Video Abstract.

[Characteristics of genetic variants in 134 patients with Acute myeloid leukemia].

OBJECTIVE: To analyze the characteristics of genetic variants in 134 patients diagnosed with Acute myeloid leukemia (AML). METHODS: Clinical data of the 134 patients with AML (non-acute promyelocytic leukemia) initially diagnosed at the 940th Hospital of the Joint Logistics Support Force of the Chinese People's Liberation Army from June 2017 to June 2022 were retrospectively analyzed. Potential variants of AML-related genes were detected by next-generation sequencing, and the frequency of variants was analyzed by using SPSS v26.0 software, and likelihood ratio χ2 test and Fisher exact test were used for data analysis. RESULTS: The patients had included 72 males and 62 females, with a gender ratio of 1.7 : 1 and a median age of 51 years (9 ~ 86 years old). One hundred twenty patients (76.1%) had harbored at least one genetic variant, including 26 (19.4%) having a single variant, 27 (20.1%) having two variants, and 49 (36.6%) having >= 3 variants. 32 (23.9%) had no detectable variants. Genetic variants detected in over 10% of the 134 patients had included NPM1 (n = 24, 17.91%), FLT3-ITD (n = 21, 15.67%), DNMT3A (n = 20, 14.93%), CEBPA (single variant; n = 14, 10.45%), TET2 (n = 14, 10.45%), and NRAS (n = 14, 10.45%). The patients were also divided into low risk, intermediate risk and high risk groups based on their chromosomal karyotypes. The mutational rates for genes in different groups have varied, with 19 patients from the low risk group harboring variants of NRAS (n = 4, 21.05%), KRAS (n = 4, 21.05%), and KIT (n = 2, 10.53%); and 96 patients from the intermediate risk group harboring variants of NPM1 (n = 24, 25.00%), FLT3-ITD (n = 20, 20.83%), DNMT3A (n = 18, 18.75%), CEBPA (n = 12, 12.50%), and TET2 genes (n = 12, 12.50%). The mutational frequencies for the 19 patients from the high risk group were ASXL1 (n = 7, 21.05%), NRAS (n = 3, 15.97%), TP53 (n = 3, 15.79%), and EZH2 (n = 2, 10.53%). A significant difference was found in the frequencies of KIT, NPM1, FLT3-ITD, DNMT3A, and ASXL1 gene variants among the low-risk, medium-risk, and high-risk groups. CONCLUSION: AML patients have a high frequency for genetic variants, with 76.1% harboring at least one variant. The frequency of genetic variants have varied among patients with different chromosomal karyotypes, and there are apparent dominant variants. KIT, NPM1, FLT3-ITD, DNMT3A, and ASXL1 may be used as prognostic factors for evaluating their prognosis.

Large-scale benchmarking of circRNA detection tools reveals large differences in sensitivity but not in precision.

The detection of circular RNA molecules (circRNAs) is typically based on short-read RNA sequencing data processed using computational tools. Numerous such tools have been developed, but a systematic comparison with orthogonal validation is missing. Here, we set up a circRNA detection tool benchmarking study, in which 16 tools detected more than 315,000 unique circRNAs in three deeply sequenced human cell types. Next, 1,516 predicted circRNAs were validated using three orthogonal methods. Generally, tool-specific precision is high and similar (median of 98.8%, 96.3% and 95.5% for qPCR, RNase R and amplicon sequencing, respectively) whereas the sensitivity and number of predicted circRNAs (ranging from 1,372 to 58,032) are the most significant differentiators. Of note, precision values are lower when evaluating low-abundance circRNAs. We also show that the tools can be used complementarily to increase detection sensitivity. Finally, we offer recommendations for future circRNA detection and validation.

Full-length circular RNA profiling by nanopore sequencing with CIRI-long.

Circular RNAs (circRNAs) have important roles in regulating developmental processes and disease progression. As most circRNA sequences are highly similar to their cognate linear transcripts, the current short-read sequencing-based methods rely on the back-spliced junction signal for distinguishing circular and linear reads, which does not allow circRNAs' full-length structure to be effectively reconstructed. Here we describe a long-read sequencing-based protocol, CIRI-long, for the detection of full-length circular RNAs. The CIRI-long protocol combines rolling circular reverse transcription and nanopore sequencing to capture full-length circRNA sequences. After poly(A) tailing, RNase R treatment, and size selection of polymerase chain reaction products, CIRI-long achieves an increased percentage (6%) of circular reads in the constructed library, which is 20-fold higher compared with previous Illumina-based strategies. This method can be applied in cell lines or tissue samples, enabling accurate detection of full-length circRNAs in the range of 100-3,000 bp. The entire protocol can be completed in 1 d, and can be scaled up for large-scale analysis using the nanopore barcoding kit and PromethION sequencing device. CIRI-long can serve as an effective and user-friendly protocol for characterizing full-length circRNAs, generating direct and convincing evidence for the existence of detected circRNAs. The analytical pipeline offers convenient functions for identification of full-length circRNA isoforms and integration of multiple datasets. The assembled full-length transcripts and their splicing patterns provide indispensable information to explore the biological function of circRNAs.

STEEL enables high-resolution delineation of spatiotemporal transcriptomic data.

Advances in spatial transcriptomics enlarge the use of single cell technologies to unveil the expression landscape of the tissues with valuable spatial context. Here, we propose an unsupervised and manifold learning-based algorithm, Spatial Transcriptome based cEll typE cLustering (STEEL), which identifies domains from spatial transcriptome by clustering beads exhibiting both highly similar gene expression profiles and close spatial distance in the manner of graphs. Comprehensive evaluation of STEEL on spatial transcriptomic datasets from 10X Visium platform demonstrates that it not only achieves a high resolution to characterize fine structures of mouse brain but also enables the integration of multiple tissue slides individually analyzed into a larger one. STEEL outperforms previous methods to effectively distinguish different cell types/domains of various tissues on Slide-seq datasets, featuring in higher bead density but lower transcript detection efficiency. Application of STEEL on spatial transcriptomes of early-stage mouse embryos (E9.5-E12.5) successfully delineates a progressive development landscape of tissues from ectoderm, mesoderm and endoderm layers, and further profiles dynamic changes on cell differentiation in heart and other organs. With the advancement of spatial transcriptome technologies, our method will have great applicability on domain identification and gene expression atlas reconstruction.

Gut microbiota dysbiosis associated with different types of demyelinating optic neuritis in patients.

BACKGROUND: Demyelinating optic neuritis (DON) causes rapid vision loss in young and middle-aged people. The limited efficacy of treatment and the toxic side effects of drugs significantly affect the quality of life of patients with DON. Therefore, DON pathogenesis has always been a research hotspot in terms of prevention and treatment. Studies have suggested that gut microbiota imbalances may be involved in autoimmune disease development via the modulation of multiple inflammatory cytokines and anti-inflammatory metabolites. Therefore, this study aims to explore gut microbiota differences between healthy controls (HCs) and patients with DON. METHODS: A total of 54 patients with DON and 41 HCs were recruited. Fecal and blood samples were collected before and after intravenous methylprednisolone pulse (IVMP) treatment. The Shannon index, gut microbiota structure, and differential bacteria were evaluated and compared. RESULTS: The Shannon diversity index was decreased in patients with DON (p < 0.001) but was higher after IVMP treatment (p < 0.05). In patients with DON, Blautia, Escherichia-Shigella, and Ruminococcus showed higher abundances, whereas Bacteroides, Faecalibacterium, Roseburia, Parabacteroides, Romboutsia, and Alistipes showed lower abundances compared to that in the HCs. After IVMP treatment, the Shannon index of the myelin oligodendrocyte glycoprotein-immunoglobulin G (+) (MOG-IgG (+)) and both aquaporin-4 (AQP4)-IgG (-) and MOG-IgG (-) groups increased (p < 0.05). Bacteroides was negatively correlated with interleukin (IL)-21, IL-17E, and tumor necrosis factor-α levels (p < 0.05, r = -0.54; p < 0.05, r= -0.50; p < 0.05, r =-0.55, respectively). Escherichia was positively correlated with macrophage inflammatory protein-3α (p < 0.05, r = 0.51). Alistipes was negatively correlated with soluble CD40 ligand (p < 0.05, r = -0.52). CONCLUSION: The gut microbiota differed significantly between patients with DON and HCs; however, IVMP treatment may restore gut microbiota diversity and structure in patients with DON. Moreover, gut microbiota changes may play a role in DON pathogenesis.

RNA-seq data science: From raw data to effective interpretation.

RNA sequencing (RNA-seq) has become an exemplary technology in modern biology and clinical science. Its immense popularity is due in large part to the continuous efforts of the bioinformatics community to develop accurate and scalable computational tools to analyze the enormous amounts of transcriptomic data that it produces. RNA-seq analysis enables genes and their corresponding transcripts to be probed for a variety of purposes, such as detecting novel exons or whole transcripts, assessing expression of genes and alternative transcripts, and studying alternative splicing structure. It can be a challenge, however, to obtain meaningful biological signals from raw RNA-seq data because of the enormous scale of the data as well as the inherent limitations of different sequencing technologies, such as amplification bias or biases of library preparation. The need to overcome these technical challenges has pushed the rapid development of novel computational tools, which have evolved and diversified in accordance with technological advancements, leading to the current myriad of RNA-seq tools. These tools, combined with the diverse computational skill sets of biomedical researchers, help to unlock the full potential of RNA-seq. The purpose of this review is to explain basic concepts in the computational analysis of RNA-seq data and define discipline-specific jargon.

Stop or Not: Genome-Wide Profiling of Reassigned Stop Codons in Ciliates.

Bifunctional stop codons that have both translation and termination functions in the same species are important for understanding the evolution and function of genetic codes in living organisms. Considering the high frequency of bifunctional codons but limited number of available genomes in ciliates, we de novo sequenced seven representative ciliate genomes to explore the evolutionary history of stop codons. We further propose a stop codon reassignment quantification method (stopCR) that can identify bifunctional codons and measure their frequencies in various eukaryotic organisms. Using our newly developed method, we found two previously undescribed genetic codes, illustrating the prevalence of bifunctional stop codons in ciliates. Overall, evolutionary genomic analyses suggest that gain or loss of reassigned stop codons in ciliates is shaped by their living environment, the eukaryotic release factor 1, and suppressor tRNAs. This study provides novel clues about the functional diversity and evolutionary history of stop codons in eukaryotic organisms.

Electric dipole modulation for boosting carrier recombination in green InP QLEDs under strong electron injection.

Enhanced and balanced carrier injection is essential to achieve highly efficient green indium phosphide (InP) quantum dot light-emitting diodes (QLEDs). However, due to the poor injection of holes in green InP QLEDs, the carrier injection is usually balanced by suppressing the strong electron injection, which decreases the radiation recombination rate dramatically. Here, an electric dipole layer is introduced to enhance the hole injection in the green InP QLED with a high mobility electron transport layer (ETL). The ultra-thin MoO3 electric dipole layer is demonstrated to form a positive built-in electric field at the interface of the hole injection layer (HIL) and hole transport layer (HTL) due to its deep conduction band level. Simulation and experimental results support that strong electric fields are produced for efficient hole hopping, and the carrier recombination rate is substantially increased. Consequently, the green InP QLEDs based on enhanced electron and hole injection have achieved a high luminance of 52 730 cd m-2 and 1.7 times external quantum efficiency (EQE) enhancement from 4.25% to 7.39%. This work has provided an effective approach to enhance carrier injection in green InP QLEDs and indicates the feasibility to realize highly efficient green InP QLEDs.

The Gut Microbiome Dynamically Associates with Host Glucose Metabolism throughout Pregnancy: Longitudinal Findings from a Matched Case-Control Study of Gestational Diabetes Mellitus.

Though gut microbiome disturbance may be involved in the etiology of gestational diabetes mellitus (GDM), data on the gut microbiome's dynamic change during pregnancy and associations with gestational glucose metabolism are still inadequate. In this prospective study comprising 120 pairs of GDM patients and matched pregnant controls, a decrease in the diversity of gut microbial species and changes in the microbial community composition with advancing gestation are found in controls, while no such trends are observed in GDM patients. Multivariable analysis identifies 10 GDM-related species (e.g., Alistipes putredinis), and the integrated associations of these species with glycemic traits are modified by habitual intake of fiber-rich plant foods. In addition, the microbial metabolic potentials related to fiber fermentation (e.g., mannan degradation pathways) and their key enzymes consistently emerge as associated with both GDM status and glycemic traits. Microbial features especially those involved in fiber fermentation, provide an incremental predictive value in a prediction model with established risk factors of GDM. These data suggest that the gut microbiome remodeling with advancing gestation is different in GDM patients compared with controls, and dietary fiber fermentation contributes to the influence of gut microbiome on gestational glycemic regulation.