Compendium of 5810 genomes of sheep and goat gut microbiomes provides new insights into the glycan and mucin utilization.

BACKGROUND: Ruminant gut microbiota are critical in ecological adaptation, evolution, and nutrition utilization because it regulates energy metabolism, promotes nutrient absorption, and improves immune function. To study the functional roles of key gut microbiota in sheep and goats, it is essential to construct reference microbial gene catalogs and high-quality microbial genomes database. RESULTS: A total of 320 fecal samples were collected from 21 different sheep and goat breeds, originating from 32 distinct farms. Metagenomic deep sequencing and binning assembly were utilized to construct a comprehensive microbial genome information database for the gut microbiota. We successfully generated the largest reference gene catalogs for gut microbiota in sheep and goats, containing over 162 million and 82 million nonredundant predicted genes, respectively, with 49 million shared nonredundant predicted genes and 1138 shared species. We found that the rearing environment has a greater impact on microbial composition and function than the host's species effect. Through subsequent assembly, we obtained 5810 medium- and high-quality metagenome-assembled genomes (MAGs), out of which 2661 were yet unidentified species. Among these MAGs, we identified 91 bacterial taxa that specifically colonize the sheep gut, which encode polysaccharide utilization loci for glycan and mucin degradation. CONCLUSIONS: By shedding light on the co-symbiotic microbial communities in the gut of small ruminants, our study significantly enhances the understanding of their nutrient degradation and disease susceptibility. Our findings emphasize the vast potential of untapped resources in functional bacterial species within ruminants, further expanding our knowledge of how the ruminant gut microbiota recognizes and processes glycan and mucins. Video Abstract.

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.

Exploring the landscape of symbiotic diversity and distribution in unicellular ciliated protists.

BACKGROUND: The eukaryotic-bacterial symbiotic system plays an important role in various physiological, developmental, and evolutionary processes. However, our current understanding is largely limited to multicellular eukaryotes without adequate consideration of diverse unicellular protists, including ciliates. RESULTS: To investigate the bacterial profiles associated with unicellular organisms, we collected 246 ciliate samples spanning the entire Ciliophora phylum and conducted single-cell based metagenome sequencing. This effort has yielded the most extensive collection of bacteria linked to unicellular protists to date. From this dataset, we identified 883 bacterial species capable of cohabiting with ciliates, unveiling the genomes of 116 novel bacterial cohabitants along with 7 novel archaeal cohabitants. Highlighting the intimate relationship between ciliates and their cohabitants, our study unveiled that over 90% of ciliates coexist with bacteria, with individual hosts fostering symbiotic relationships with multiple bacteria concurrently, resulting in the observation of seven distinct symbiotic patterns among bacteria. Our exploration of symbiotic mechanisms revealed the impact of host digestion on the intracellular diversity of cohabitants. Additionally, we identified the presence of eukaryotic-like proteins in bacteria as a potential contributing factor to their resistance against host digestion, thereby expanding their potential host range. CONCLUSIONS: As the first large-scale analysis of prokaryotic associations with ciliate protists, this study provides a valuable resource for future research on eukaryotic-bacterial symbioses. Video Abstract.

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.

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/.

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.

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.