scBSP: A fast and accurate tool for identifying spatially variable genes from spatial transcriptomic data.

Spatially resolved transcriptomics have enabled the inference of gene expression patterns within two and three-dimensional space, while introducing computational challenges due to growing spatial resolutions and sparse expressions. Here, we introduce scBSP, an open-source, versatile, and user-friendly package designed for identifying spatially variable genes in large-scale spatial transcriptomics. scBSP implements sparse matrix operation to significantly increase the computational efficiency in both computational time and memory usage, processing the high-definition spatial transcriptomics data for 19,950 genes on 181,367 spots within 10 seconds. Applied to diverse sequencing data and simulations, scBSP efficiently identifies spatially variable genes, demonstrating fast computational speed and consistency across various sequencing techniques and spatial resolutions for both two and three-dimensional data with up to millions of cells. On a sample with hundreds of thousands of sports, scBSP identifies SVGs accurately in seconds to on a typical desktop computer.

Altered mucosal bacteria and metabolomics in patients with Peutz-Jeghers syndrome.

BACKGROUND: Peutz-Jeghers syndrome (PJS) is a rare genetic disorder characterized by the development of pigmented spots, gastrointestinal polyps and increased susceptibility to cancers. Currently, most studies have investigated intestinal microbiota through fecal microbiota, and there are few reports about mucosa-associated microbiota. It remains valuable to search for the key intestinal microbiota or abnormal metabolic pathways linked to PJS. AIM: This study aimed to assess the structure and composition of mucosa-associated microbiota in patients with PJS and to explore the potential influence of intestinal microbiota disorders and metabolite changes on PJS. METHODS: The bacterial composition was analyzed in 13 PJS patients and 12 controls using 16S rRNA gene sequencing (Illumina MiSeq) for bacteria. Differential analyses of the intestinal microbiota were performed from the phylum to species level. Liquid chromatography-tandem mass spectrometry (LC‒MS) was used to detect the differentially abundant metabolites of PJS patients and controls to identify different metabolites and metabolic biomarkers of small intestinal mucosa samples. RESULTS: High-throughput sequencing confirmed the special characteristics and biodiversity of the mucosa microflora in patients with PJS. They had lower bacterial biodiversity than controls. The abundance of intestinal mucosal microflora was significantly lower than that of fecal microflora. In addition, lipid metabolism, amino acid metabolism, carbohydrate metabolism, nucleotide metabolism and other pathways were significantly different from those of controls, which were associated with the development of the enteric nervous system, intestinal inflammation and development of tumors. CONCLUSION: This is the first report on the mucosa-associated microbiota and metabolite profile of subjects with PJS, which may be meaningful to provide a structural basis for further research on intestinal microecology in PJS.

Hydrogen Regulates Ulcerative Colitis by Affecting the Intestinal Redox Environment.

The redox balance in the intestine plays an important role in maintaining intestinal homeostasis, and it is closely related to the intestinal mucosal barrier, intestinal inflammation, and the gut microbiota. Current research on the treatment of ulcerative colitis has focused on immune disorders, excessive inflammation, and oxidative stress. However, an imbalance in intestinal redox reaction plays a particularly critical role. Hydrogen is produced by some anaerobic bacteria via hydrogenases in the intestine. Increasing evidence suggests that hydrogen, as an inert gas, is crucial for immunity, inflammation, and oxidative stress and plays a protective role in ulcerative colitis. Hydrogen maintains the redox state balance in the intestine in ulcerative colitis and reduces damage to intestinal epithelial cells by exerting its selective antioxidant ability. Hydrogen also regulates the intestinal flora, reduces the harmful effects of bacteria on the intestinal epithelial barrier, promotes the restoration of normal anaerobic bacteria in the intestines, and ultimately improves the integrity of the intestinal epithelial barrier. The present review focuses on the therapeutic mechanisms of hydrogen-targeting ulcerative colitis.

Dimension-agnostic and granularity-based spatially variable gene identification using BSP.

Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a non-parametric model by comparing gene expression pattens at two spatial granularities to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.

Clinical and endoscopic features of primary small bowel lymphoma: a single-center experience from mainland China.

Objective: The worldwide incidence of primary small intestinal lymphoma (PSIL) is increasing. However, little is known about the clinical and endoscopic characteristics of this disease. The aim of this study was to investigate the clinical and endoscopic data of patients with PSIL, with the goal of enhancing our understanding of the disease, improving diagnostic accuracy, and facilitating more accurate prognosis estimation. Methods: Ninety-four patients diagnosed with PSIL were retrospectively studied at Qilu Hospital of Shandong University between 2012 and 2021. The clinical data, enteroscopy findings, treatment modalities, and survival times were collected and analyzed. Results: Ninety-four patients (52 males) with PSIL were included in this study. The median age of onset was 58.5 years (range: 19-80 years). Diffuse large B-cell lymphoma (n=37) was the most common pathological type. Abdominal pain (n=59) was the most frequent clinical presentation. The ileocecal region (n=32) was the most commonly affected site, and 11.7% of patients had multiple lesions. At the time of diagnosis, the majority of patients (n=68) were in stages I-II. A new endoscopic classification of PSIL was developed, including hypertrophic type, exophytic type, follicular/polypoid type, ulcerative type, and diffusion type. Surgery did not show a significant increase in overall survival; chemotherapy was the most commonly administered treatment. T-cell lymphoma, stages III-IV, "B" symptoms, and ulcerative type were associated with poor prognosis. Conclusion: This study provides a comprehensive analysis of the clinical and endoscopic features of PSIL in 94 patients. This highlights the importance of considering clinical and endoscopic characteristics for accurate diagnosis and prognosis estimation during small bowel enteroscopy. Early detection and treatment of PSIL is associated with a favorable prognosis. Our findings also suggest that certain risk factors, such as pathological type, "B" symptoms, and endoscopic type, may affect the survival of PSIL patients. These results underscore the need for careful consideration of these factors in the diagnosis and treatment of PSIL.

The Allele Catalog Tool: a web-based interactive tool for allele discovery and analysis.

BACKGROUND: The advancement of sequencing technologies today has made a plethora of whole-genome re-sequenced (WGRS) data publicly available. However, research utilizing the WGRS data without further configuration is nearly impossible. To solve this problem, our research group has developed an interactive Allele Catalog Tool to enable researchers to explore the coding region allelic variation present in over 1,000 re-sequenced accessions each for soybean, Arabidopsis, and maize. RESULTS: The Allele Catalog Tool was designed originally with soybean genomic data and resources. The Allele Catalog datasets were generated using our variant calling pipeline (SnakyVC) and the Allele Catalog pipeline (AlleleCatalog). The variant calling pipeline is developed to parallelly process raw sequencing reads to generate the Variant Call Format (VCF) files, and the Allele Catalog pipeline takes VCF files to perform imputations, functional effect predictions, and assemble alleles for each gene to generate curated Allele Catalog datasets. Both pipelines were utilized to generate the data panels (VCF files and Allele Catalog files) in which the accessions of the WGRS datasets were collected from various sources, currently representing over 1,000 diverse accessions for soybean, Arabidopsis, and maize individually. The main features of the Allele Catalog Tool include data query, visualization of results, categorical filtering, and download functions. Queries are performed from user input, and results are a tabular format of summary results by categorical description and genotype results of the alleles for each gene. The categorical information is specific to each species; additionally, available detailed meta-information is provided in modal popups. The genotypic information contains the variant positions, reference or alternate genotypes, the functional effect classes, and the amino-acid changes of each accession. Besides that, the results can also be downloaded for other research purposes. CONCLUSIONS: The Allele Catalog Tool is a web-based tool that currently supports three species: soybean, Arabidopsis, and maize. The Soybean Allele Catalog Tool is hosted on the SoyKB website ( https://soykb.org/SoybeanAlleleCatalogTool/ ), while the Allele Catalog Tool for Arabidopsis and maize is hosted on the KBCommons website ( https://kbcommons.org/system/tools/AlleleCatalogTool/Zmays and https://kbcommons.org/system/tools/AlleleCatalogTool/Athaliana ). Researchers can use this tool to connect variant alleles of genes with meta-information of species.

Dimension-agnostic and granularity-based spatially variable gene identification.

Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a spatial granularity-guided and non-parametric model to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This new method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.

Dimension-agnostic and granularity-based spatially variable gene identification.

Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a spatial granularity-guided and non-parametric model to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This new method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.

Single-cell biological network inference using a heterogeneous graph transformer.

Single-cell multi-omics (scMulti-omics) allows the quantification of multiple modalities simultaneously to capture the intricacy of complex molecular mechanisms and cellular heterogeneity. Existing tools cannot effectively infer the active biological networks in diverse cell types and the response of these networks to external stimuli. Here we present DeepMAPS for biological network inference from scMulti-omics. It models scMulti-omics in a heterogeneous graph and learns relations among cells and genes within both local and global contexts in a robust manner using a multi-head graph transformer. Benchmarking results indicate DeepMAPS performs better than existing tools in cell clustering and biological network construction. It also showcases competitive capability in deriving cell-type-specific biological networks in lung tumor leukocyte CITE-seq data and matched diffuse small lymphocytic lymphoma scRNA-seq and scATAC-seq data. In addition, we deploy a DeepMAPS webserver equipped with multiple functionalities and visualizations to improve the usability and reproducibility of scMulti-omics data analysis.

Mapping Genetic Variation in Arabidopsis in Response to Plant Growth-Promoting Bacterium Azoarcus olearius DQS-4T.

Plant growth-promoting bacteria (PGPB) can enhance plant health by facilitating nutrient uptake, nitrogen fixation, protection from pathogens, stress tolerance and/or boosting plant productivity. The genetic determinants that drive the plant-bacteria association remain understudied. To identify genetic loci highly correlated with traits responsive to PGPB, we performed a genome-wide association study (GWAS) using an Arabidopsis thaliana population treated with Azoarcus olearius DQS-4T. Phenotypically, the 305 Arabidopsis accessions tested responded differently to bacterial treatment by improving, inhibiting, or not affecting root system or shoot traits. GWA mapping analysis identified several predicted loci associated with primary root length or root fresh weight. Two statistical analyses were performed to narrow down potential gene candidates followed by haplotype block analysis, resulting in the identification of 11 loci associated with the responsiveness of Arabidopsis root fresh weight to bacterial inoculation. Our results showed considerable variation in the ability of plants to respond to inoculation by A. olearius DQS-4T while revealing considerable complexity regarding statistically associated loci with the growth traits measured. This investigation is a promising starting point for sustainable breeding strategies for future cropping practices that may employ beneficial microbes and/or modifications of the root microbiome.

Prediction of protein stability changes upon single-point variant using 3D structure profile.

Identifying protein thermodynamic stability changes upon single-point variants is crucial for studying mutation-induced alterations in protein biophysics, genomic variants, and mutation-related diseases. In the last decade, various computational methods have been developed to predict the effects of single-point variants, but the prediction accuracy is still far from satisfactory for practical applications. Herein, we review approaches and tools for predicting stability changes upon the single-point variant. Most of these methods require tertiary protein structure as input to achieve reliable predictions. However, the availability of protein structures limits the immediate application of these tools. To improve the performance of a computational prediction from a protein sequence without experimental structural information, we introduce a new computational framework: MU3DSP. This method assesses the effects of single-point variants on protein thermodynamic stability based on point mutated protein 3D structure profile. Given a protein sequence with a single variant as input, MU3DSP integrates both sequence-level features and averaged features of 3D structures obtained from sequence alignment to PDB to assess the change of thermodynamic stability induced by the substitution. MU3DSP outperforms existing methods on various benchmarks, making it a reliable tool to assess both somatic and germline substitution variants and assist in protein design. MU3DSP is available as an open-source tool at https://github.com/hurraygong/MU3DSP.

A novel Synthetic phenotype association study approach reveals the landscape of association for genomic variants and phenotypes.

INTRODUCTION: Genome-Wide Association Studies (GWAS) identify tagging variants in the genome that are statistically associated with the phenotype because of their linkage disequilibrium (LD) relationship with the causative mutation (CM). When both low-density genotyped accession panels with phenotypes and resequenced data accession panels are available, tagging variants can assist with post-GWAS challenges in CM discovery. OBJECTIVES: Our objective was to identify additional GWAS evaluation criteria to assess correspondence between genomic variants and phenotypes, as well as enable deeper analysis of the localized landscape of association. METHODS: We used genomic variant positions as Synthetic phenotypes in GWAS that we named "Synthetic phenotype association study" (SPAS). The extreme case of SPAS is what we call an "Inverse GWAS" where we used CM positions of cloned soybean genes. We developed and validated the Accuracy concept as a measure of the correspondence between variant positions and phenotypes. RESULTS: The SPAS approach demonstrated that the genotype status of an associated variant used as a Synthetic phenotype enabled us to explore the relationships between tagging variants and CMs, and further, that utilizing CMs as Synthetic phenotypes in Inverse GWAS illuminated the landscape of association. We implemented the Accuracy calculation for a curated accession panel to an online Accuracy calculation tool (AccuTool) as a resource for gene identification in soybean. We demonstrated our concepts on three examples of soybean cloned genes. As a result of our findings, we devised an enhanced "GWAS to Genes" analysis (Synthetic phenotype to CM strategy, SP2CM). Using SP2CM, we identified a CM for a novel gene. CONCLUSION: The SP2CM strategy utilizing Synthetic phenotypes and the Accuracy calculation of correspondence provides crucial information to assist researchers in CM discovery. The impact of this work is a more effective evaluation of landscapes of GWAS associations.

scGNN 2.0: a graph neural network tool for imputation and clustering of single-cell RNA-Seq data.

MOTIVATION: Gene expression imputation has been an essential step of the single-cell RNA-Seq data analysis workflow. Among several deep-learning methods, the debut of scGNN gained substantial recognition in 2021 for its superior performance and the ability to produce a cell-cell graph. However, the implementation of scGNN was relatively time-consuming and its performance could still be optimized. RESULTS: The implementation of scGNN 2.0 is significantly faster than scGNN thanks to a simplified close-loop architecture. For all eight datasets, cell clustering performance was increased by 85.02% on average in terms of adjusted rand index, and the imputation Median L1 Error was reduced by 67.94% on average. With the built-in visualizations, users can quickly assess the imputation and cell clustering results, compare against benchmarks and interpret the cell-cell interaction. The expanded input and output formats also pave the way for custom workflows that integrate scGNN 2.0 with other scRNA-Seq toolkits on both Python and R platforms. AVAILABILITY AND IMPLEMENTATION: scGNN 2.0 is implemented in Python (as of version 3.8) with the source code available at https://github.com/OSU-BMBL/scGNN2.0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bile reflux alters the profile of the gastric mucosa microbiota.

Background: Bile reflux can cause inflammation, gastric mucosa atrophy, and diseases such as stomach cancer. Alkaline bile flowing back into the stomach affects the intragastric environment and can alter the gastric bacterial community. We sought to identify the characteristics of the stomach mucosal microbiota in patients with bile reflux. Methods: Gastric mucosal samples were collected from 52 and 40 chronic gastritis patients with and without bile reflux, respectively. The bacterial profile was determined using 16S rRNA gene analysis. Results: In the absence of H. pylori infection, the richness (based on the Sobs and Chao1 indices; P <0.05) and diversity (based on Shannon indices; P <0.05) of gastric mucosa microbiota were higher in patients with bile reflux patients than in those without. There was a marked difference in the microbiota structure between patients with and without bile reflux (ANOSIM, R=0.058, P=0.011). While the genera, Comamonas, Halomonas, Bradymonas, Pseudomonas, Marinobacter, Arthrobacter, and Shewanella were enriched in patients with bile reflux, the genera, Haemophilus, Porphyromonas, and Subdoligranulum, were enriched in those without bile reflux. Conclusion: Our results demonstrate that bile reflux significantly alters the composition of the gastric microbiota.

Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning.

Spatially resolved transcriptomics provides a new way to define spatial contexts and understand the pathogenesis of complex human diseases. Although some computational frameworks can characterize spatial context via various clustering methods, the detailed spatial architectures and functional zonation often cannot be revealed and localized due to the limited capacities of associating spatial information. We present RESEPT, a deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics. Given inputs such as gene expression or RNA velocity, RESEPT learns a three-dimensional embedding with a spatial retained graph neural network from spatial transcriptomics. The embedding is then visualized by mapping into color channels in an RGB image and segmented with a supervised convolutional neural network model. Based on a benchmark of 10x Genomics Visium spatial transcriptomics datasets on the human and mouse cortex, RESEPT infers and visualizes the tissue architecture accurately. It is noteworthy that, for the in-house AD samples, RESEPT can localize cortex layers and cell types based on pre-defined region- or cell-type-enriched genes and furthermore provide critical insights into the identification of amyloid-beta plaques in Alzheimer's disease. Interestingly, in a glioblastoma sample analysis, RESEPT distinguishes tumor-enriched, non-tumor, and regions of neuropil with infiltrating tumor cells in support of clinical and prognostic cancer applications.

Altered Gut Microbiota in Patients With Peutz-Jeghers Syndrome.

Background: Peutz-Jeghers syndrome (PJS) is a rare genetic disorder characterized by the development of pigmented spots and gastrointestinal polyps and increased susceptibility to cancers. It remains unknown whether gut microbiota dysbiosis is linked to PJS. Aim: This study aimed to assess the structure and composition of the gut microbiota, including both bacteria and fungi, in patients with PJS and investigate the relationship between gut microbiota dysbiosis and PJS pathogenesis. Methods: The bacterial and fungal composition of the fecal microbiota was analyzed in 23 patients with PJS (cases), 17 first-degree asymptomatic relatives (ARs), and 24 healthy controls (HCs) using 16S (MiSeq) and ITS2 (pyrosequencing) sequencing for bacteria and fungi, respectively. Differential analyses of the intestinal flora were performed from the phylum to species level. Results: Alpha-diversity distributions of bacteria and fungi indicated that the abundance of both taxa differed between PJS cases and controls. However, while the diversity and composition of fecal bacteria in PJS cases were significantly different from those in ARs and HCs, fungal flora was more stable. High-throughput sequencing confirmed the special characteristics and biodiversity of the fecal bacterial and fungal microflora in patients with PJS. They had lower bacterial biodiversity than controls, with a higher frequency of the Proteobacteria phylum, Enterobacteriaceae family, and Escherichia-Shigella genus, and a lower frequency of the Firmicutes phylum and the Lachnospiraceae and Ruminococcaceae families. Of fungi, Candida was significantly higher in PJS cases than in controls. Conclusion: The findings reported here confirm gut microbiota dysbiosis in patients with PJS. This is the first report on the bacterial and fungal microbiota profile of subjects with PJS, which may be meaningful to provide a structural basis for further research on intestinal microecology in PJS.

Astrocytic GABA transporter 1 deficit in novel SLC6A1 variants mediated epilepsy: Connected from protein destabilization to seizures in mice and humans.

OBJECTIVE: Mutations in γ-aminobutyric acid (GABA) transporter 1 (GAT-1)-encoding SLC6A1 have been associated with myoclonic atonic epilepsy and other phenotypes. We determined the patho-mechanisms of the mutant GAT-1, in order to identify treatment targets. METHODS: We conducted whole-exome sequencing of patients with myoclonic atonic epilepsy (MAE) and characterized the seizure phenotypes and EEG patterns. We studied the protein stability and structural changes with homology modeling and machine learning tools. We characterized the function and trafficking of the mutant GAT-1 with 3H radioactive GABA uptake assay and confocal microscopy. We utilized different models including a knockin mouse and human astrocytes derived from induced pluripotent stem cells (iPSCs). We focused on astrocytes because of their direct impact of astrocytic GAT-1 in seizures. RESULTS: We identified four novel SLC6A1 variants associated with MAE and 2 to 4 Hz spike-wave discharges as a common EEG feature. Machine learning tools predicted that the variant proteins are destabilized. The variant protein had reduced expression and reduced GABA uptake due to endoplasmic reticular retention. The consistent observation was made in cortical and thalamic astrocytes from variant-knockin mice and human iPSC-derived astrocytes. The Slc6a+/A288V mouse, representative of MAE, had increased 5-7 Hz spike-wave discharges and absence seizures. INTERPRETATION: SLC6A1 variants in various locations of the protein peptides can cause MAE with similar seizure phenotypes and EEG features. Reduced GABA uptake is due to decreased functional GAT-1, which, in thalamic astrocytes, could result in increased extracellular GABA accumulation and enhanced tonic inhibition, leading to seizures and abnormal EEGs.