Enhancer-driven gene regulatory networks inference from single-cell RNA-seq and ATAC-seq data.

Deciphering the intricate relationships between transcription factors (TFs), enhancers, and genes through the inference of enhancer-driven gene regulatory networks (eGRNs) is crucial in understanding gene regulatory programs in a complex biological system. This study introduces STREAM, a novel method that leverages a Steiner forest problem model, a hybrid biclustering pipeline, and submodular optimization to infer eGRNs from jointly profiled single-cell transcriptome and chromatin accessibility data. Compared to existing methods, STREAM demonstrates enhanced performance in terms of TF recovery, TF-enhancer linkage prediction, and enhancer-gene relation discovery. Application of STREAM to an Alzheimer's disease dataset and a diffuse small lymphocytic lymphoma dataset reveals its ability to identify TF-enhancer-gene relations associated with pseudotime, as well as key TF-enhancer-gene relations and TF cooperation underlying tumor cells.

CEMIG: prediction of the cis-regulatory motif using the de Bruijn graph from ATAC-seq.

Sequence motif discovery algorithms enhance the identification of novel deoxyribonucleic acid sequences with pivotal biological significance, especially transcription factor (TF)-binding motifs. The advent of assay for transposase-accessible chromatin using sequencing (ATAC-seq) has broadened the toolkit for motif characterization. Nonetheless, prevailing computational approaches have focused on delineating TF-binding footprints, with motif discovery receiving less attention. Herein, we present Cis rEgulatory Motif Influence using de Bruijn Graph (CEMIG), an algorithm leveraging de Bruijn and Hamming distance graph paradigms to predict and map motif sites. Assessment on 129 ATAC-seq datasets from the Cistrome Data Browser demonstrates CEMIG's exceptional performance, surpassing three established methodologies on four evaluative metrics. CEMIG accurately identifies both cell-type-specific and common TF motifs within GM12878 and K562 cell lines, demonstrating its comparative genomic capabilities in the identification of evolutionary conservation and cell-type specificity. In-depth transcriptional and functional genomic studies have validated the functional relevance of CEMIG-identified motifs across various cell types. CEMIG is available at https://github.com/OSU-BMBL/CEMIG, developed in C++ to ensure cross-platform compatibility with Linux, macOS and Windows operating systems.

A dual-energy CT reconstruction method based on anchor network from dual quarter scans.

Compared with conventional single-energy computed tomography (CT), dual-energy CT (DECT) provides better material differentiation but most DECT imaging systems require dual full-angle projection data at different X-ray spectra. Relaxing the requirement of data acquisition is an attractive research to promote the applications of DECT in wide range areas and reduce the radiation dose as low as reasonably achievable. In this work, we design a novel DECT imaging scheme with dual quarter scans and propose an efficient method to reconstruct the desired DECT images from the dual limited-angle projection data. We first study the characteristics of limited-angle artifacts under dual quarter scans scheme, and find that the negative and positive artifacts of DECT images are complementarily distributed in image domain because the corresponding X-rays of high- and low-energy scans are symmetric. Inspired by this finding, a fusion CT image is generated by integrating the limited-angle DECT images of dual quarter scans. This strategy enhances the true image information and suppresses the limited-angle artifacts, thereby restoring the image edges and inner structures. Utilizing the capability of neural network in the modeling of nonlinear problem, a novel Anchor network with single-entry double-out architecture is designed in this work to yield the desired DECT images from the generated fusion CT image. Experimental results on the simulated and real data verify the effectiveness of the proposed method. This work enables DECT on imaging configurations with half-scan and largely reduces scanning angles and radiation doses.

A weighted two-stage sequence alignment framework to identify motifs from ChIP-exo data.

In this study, we introduce TESA (weighted two-stage alignment), an innovative motif prediction tool that refines the identification of DNA-binding protein motifs, essential for deciphering transcriptional regulatory mechanisms. Unlike traditional algorithms that rely solely on sequence data, TESA integrates the high-resolution chromatin immunoprecipitation (ChIP) signal, specifically from ChIP-exonuclease (ChIP-exo), by assigning weights to sequence positions, thereby enhancing motif discovery. TESA employs a nuanced approach combining a binomial distribution model with a graph model, further supported by a "bookend" model, to improve the accuracy of predicting motifs of varying lengths. Our evaluation, utilizing an extensive compilation of 90 prokaryotic ChIP-exo datasets from proChIPdb and 167 H. sapiens datasets, compared TESA's performance against seven established tools. The results indicate TESA's improved precision in motif identification, suggesting its valuable contribution to the field of genomic research.

MISD-IR: material-image subspace decomposition-based iterative reconstruction with spectrum estimation for dual-energy computed tomography.

Background: Dual-energy computed tomography (DECT) is a promising technique, which can provide unique capability for material quantification. The iterative reconstruction of material maps requires spectral information and its accuracy is affected by spectral mismatch. Simultaneously estimating the spectra and reconstructing material maps avoids extra workload on spectrum estimation and the negative impact of spectral mismatch. However, existing methods are not satisfactory in image detail preservation, edge retention, and convergence rate. The purpose of this paper was to mine the similarity between the reconstructed images and the material images to improve the imaging quality, and to design an effective iteration strategy to improve the convergence efficiency. Methods: The material-image subspace decomposition-based iterative reconstruction (MISD-IR) with spectrum estimation was proposed for DECT. MISD-IR is an optimized model combining spectral estimation and material reconstruction with fast convergence speed and promising noise suppression capability. We proposed to reconstruct the material maps based on extended simultaneous algebraic reconstruction techniques and estimation of the spectrum with model spectral. To stabilize the iteration and alleviate the influence of errors, we introduced a weighted proximal operator based on the block coordinate descending algorithm (WP-BCD). Furthermore, the reconstructed computed tomography (CT) images were introduced to suppress the noise based on subspace decomposition, which relies on non-local regularization to prevent noise accumulation. Results: In numerical experiments, the results of MISD-IR were closer to the ground truth compared with other methods. In real scanning data experiments, the results of MISD-IR showed sharper edges and details. Compared with other one-step iterative methods in the experiment, the running time of MISD-IR was reduced by 75%. Conclusions: The proposed MISD-IR can achieve accurate material decomposition (MD) without known energy spectrum in advance, and has good retention of image edges and details. Compared with other one-step iterative methods, it has high convergence efficiency.

The interaction between oral microbiota and gut microbiota in atherosclerosis.

Atherosclerosis (AS) is a complex disease caused by multiple pathological factors threatening human health-the pathogenesis is yet to be fully elucidated. In recent years, studies have exhibited that the onset of AS is closely involved with oral and gut microbiota, which may initiate or worsen atherosclerotic processes through several mechanisms. As for how the two microbiomes affect AS, existing mechanisms include invading plaque, producing active metabolites, releasing lipopolysaccharide (LPS), and inducing elevated levels of inflammatory mediators. Considering the possible profound connection between oral and gut microbiota, the effect of the interaction between the two microbiomes on the initiation and progression of AS has been investigated. Findings are oral microbiota can lead to gut dysbiosis, and exacerbate intestinal inflammation. Nevertheless, relevant research is not commendably refined and a concrete review is needed. Hence, in this review, we summarize the most recent mechanisms of the oral microbiota and gut microbiota on AS, illustrate an overview of the current clinical and epidemiological evidence to support the bidirectional connection between the two microbiomes and AS.

Probiotic properties and the ameliorative effect on DSS-induced colitis of human milk-derived Lactobacillus gasseri SHMB 0001.

Human milk contains a variety of microorganisms that exert benefit for human health. In the current study, we isolated a novel Lactobacillus gasseri strain named Lactobacillus gasseri (L. gasseri) SHMB 0001 from human milk and aimed to evaluate the probiotic characteristics and protective effects on murine colitis of the strain. The results showed that L. gasseri SHMB 0001 possessed promising potential probiotic characteristics, including good tolerance against artificial gastric and intestinal fluids, adhesion to Caco-2 cells, susceptibility to antibiotic, no hemolytic activity, and without signs of toxicity or infection in mice. Administration of L. gasseri SHMB 0001 (1 × 108 CFU per gram of mouse weight per day) reduced weight loss, the disease activity index, and colon shortening in mice during murine colitis conditions. Histopathological analysis revealed that L. gasseri SHMB 0001 treatment attenuated epithelial damage and inflammatory infiltration in the colon. L. gasseri SHMB 0001 treatment increased the expression of colonic occludin and claudin-1 while decreasing the expression of pro-inflammatory cytokine genes. L. gasseri SHMB 0001 modified the composition and structure of the gut microbiota community and partially recovered the Clusters of Orthologous Groups (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathways altered by dextran sulfate sodium (DSS). Overall, our results indicated that the human breast milk-derived L. gasseri SHMB 0001 exhibited promising probiotic properties and ameliorative effect on DSS-induced colitis in mice. L. gasseri SHMB 0001 may be applied as a promising probiotic against intestinal inflammation in the future. PRACTICAL APPLICATION: L. gasseri SHMB 0001 isolated from human breast milk showed good tolerance to gastrointestinal environment, safety, and protective effect against DSS-induced mice colitis via enforcing gut barrier, downregulating pro-inflammatory cytokines, and modulating gut microbiota. L. gasseri SHMB 0001 may be a promising probiotic candidate for the treatment of intestinal inflammation.

Prior image-based generative adversarial learning for multi-material decomposition in photon counting computed tomography.

BACKGROUND: Photon counting detector computed tomography (PCD-CT) is a novel promising technique providing higher spatial resolution, lower radiation dose and greater energy spectrum differentiation, which create more possibilities to improve image quality. Multi-material decomposition is an attractive application for PCD-CT to identify complicated materials and provide accurate quantitative analysis. However, limited by the finite photon counting rate in each energy window of photon counting detector, the noise problem hinders the decomposition of high-quality basis material images. METHODS: To address this issue, an end-to-end multi-material decomposition network based on prior images is proposed in this paper. First, the reconstructed images corresponding to the full spectrum with less noise are introduced as prior information to improve the overall signal-to-noise ratio of the data. Then, a generative adversarial network is designed to mine the relationship between reconstructed images and basis material images based on the information interaction of material decomposition. Furthermore, a weighted edge loss is introduced to adapt to the structural differences of different basis material images. RESULTS: To verify the performance of the proposed method, simulation and real studies are carried out. In simulation study of structured fibro-glandular tissue model, the results show that the proposed method decreased the root mean square error by 67 % and 26 % on adipose, 66 % and 28 % on fibroglandular, 52 % and 8 % on calcification, compared to butterfly network and dual interactive Wasserstein generative adversarial network. CONCLUSION: Experimentally, the proposed method shows certain advantages over other methods on noise suppression effect, detail retention ability and decomposition accuracy.

Effects and microbiota changes following oral lyophilized fecal microbiota transplantation in children with autism spectrum disorder.

Background and purpose: Autism spectrum disorder (ASD) is a group of heterogeneous neurodevelopmental disorders that is characterized by core features in social communication impairment and restricted, repetitive sensory-motor behaviors. This study aimed to further investigate the utilization of fecal microbiota transplantation (FMT) in children with ASD, both with and without gastrointestinal (GI) symptoms, evaluate the effect of FMT and analyze the alterations in bacterial and fungal composition within the gut microbiota. Methods: A total of 38 children diagnosed with ASD participated in the study and underwent oral lyophilized FMT treatment. The dosage of the FMT treatment was determined based on a ratio of 1 g of donor stool per 1 kg of recipient body weight, with a frequency of once every 4 weeks for a total of 12 weeks. In addition, 30 healthy controls (HC) were included in the analysis. The clinical efficacy of FMT was evaluated, while the composition of fecal bacteria and fungi was determined using 16S rRNA and ITS gene sequencing methods. Results: Median age of the 38 children with ASD was 7 years. Among these children, 84.2% (32 of 38) were boys and 81.6% (31 of 38) exhibited GI symptoms, with indigestion, constipation and diarrhea being the most common symptoms. Sample collections and assessments were conducted at baseline (week 0), post-treatment (week 12) and follow-up (week 20). At the end of the follow-up phase after FMT treatment, the autism behavior checklist (ABC) scores decreased by 23% from baseline, and there was a 10% reduction in scores on the childhood autism rating scale (CARS), a 6% reduction in scores on the social responsiveness scale (SRS) and a 10% reduction in scores on the sleep disturbance scale for children (SDSC). In addition, short-term adverse events observed included vomiting and fever in 2 participants, which were self-limiting and resolved within 24 h, and no long-term adverse events were observed. Although there was no significant difference in alpha and beta diversity in children with ASD before and after FMT therapy, the FMT treatment resulted in alterations in the relative abundances of various bacterial and fungal genera in the samples of ASD patients. Comparisons between children with ASD and healthy controls (HC) revealed statistically significant differences in microbial abundance before and after FMT. Blautia, Sellimonas, Saccharomycopsis and Cystobasidium were more abundant in children with ASD than in HC, while Dorea were less abundant. After FMT treatment, levels of Blautia, Sellimonas, Saccharomycopsis and Cystobasidium decreased, while levels of Dorea increased. Moreover, the increased abundances of Fusicatenibacter, Erysipelotrichaceae_UCG-003, Saccharomyces, Rhodotorula, Cutaneotrichosporon and Zygosaccharomyces were negatively correlated with the scores of ASD core symptoms. Conclusions: Oral lyophilized FMT could improve GI and ASD related symptoms, as well as sleep disturbances, and alter the gut bacterial and fungal microbiota composition in children with ASD. Clinical Trial Registration: Chinese Clinical Trial Registry, ChiCTR2200055943. Registered 28 January 2022, www.chictr.org.cn.

Antimicrobial Resistance of Clostridioides difficile in Children from a Tertiary Pediatric Hospital in Shanghai, China.

Background: Our previous study reported a high rate of recurrence in children with Clostridioides difficile (C. difficile) infection (CDI) after conventional antibiotic therapy. Here, we aimed to explore whether metronidazole and vancomycin resistant C. difficile isolates are circulating in pediatric CDI. Methods: Antimicrobial susceptibility testing (AST) using the agar dilution method according to the Clinical and Laboratory Standard Institute (CLSI) were performed on C. difficile isolates collected from children with CDI between 2019 and 2022 at the Shanghai Children's Hospital. Whole-genome sequencing (WGS) was performed on all C. difficile isolates, and the presence of antibiotic resistance genes (ARGs) were identified using Resfinder and the Comprehensive Antibiotic Resistance Database (CARD). The presence of plasmid pCD-METRO was detected using SRST2 (v0.2.0) against 8 pCD-METRO coding sequences. Results: A total of 50 C. difficile isolates were collected from stools of CDI children. The overall resistance rate on all isolates was 30.00% for metronidazole, 6.00% for vancomycin, 0% for rifaximin, 2.00% for rifampin, 24.00% for meropenem, 100.00% for ceftriaxone and clindamycin, 86.00% for erythromycin, 30.0% for levofloxacin, and 50.0% for tetracycline. Multidrug-resistant (MDR) was presented in 44 isolates (88.00%). Sixteen reported potential ARGs relating with resistance to antibiotic classes of aminoglycoside (AAC(6')-Ie-APH(2")-Ia, aad(6), ANT(6)-Ib, APH(2")-If, APH(3')-IIIa), lincosamide-clindamycin-erythromycin (ErmB, ErmQ), fluoroquinolones (CdeA), glycopeptides (vanRG), nucleoside (SAT-4), tetracycline (tetM, tetA(P), tetB(P), tetO), and trimethoprim (dfrF) were identified. However, the pCD-METRO plasmid and vanA/B were not detected in any isolates. Conclusion: C. difficile isolates from children with reduced susceptibility to metronidazole and vancomycin are emerging in pediatric CDI in China. The lack of pCD-METRO plasmid and vanA/B associated with reduced antibiotic susceptibility suggests there are additional mechanisms of resistance.

Deep Learning-Assisted Measurements of Photoreceptor Ellipsoid Zone Area and Outer Segment Volume as Biomarkers for Retinitis Pigmentosa.

The manual segmentation of retinal layers from OCT scan images is time-consuming and costly. The deep learning approach has potential for the automatic delineation of retinal layers to significantly reduce the burden of human graders. In this study, we compared deep learning model (DLM) segmentation with manual correction (DLM-MC) to conventional manual grading (MG) for the measurements of the photoreceptor ellipsoid zone (EZ) area and outer segment (OS) volume in retinitis pigmentosa (RP) to assess whether DLM-MC can be a new gold standard for retinal layer segmentation and for the measurement of retinal layer metrics. Ninety-six high-speed 9 mm 31-line volume scans obtained from 48 patients with RPGR-associated XLRP were selected based on the following criteria: the presence of an EZ band within the scan limit and a detectable EZ in at least three B-scans in a volume scan. All the B-scan images in each volume scan were manually segmented for the EZ and proximal retinal pigment epithelium (pRPE) by two experienced human graders to serve as the ground truth for comparison. The test volume scans were also segmented by a DLM and then manually corrected for EZ and pRPE by the same two graders to obtain DLM-MC segmentation. The EZ area and OS volume were determined by interpolating the discrete two-dimensional B-scan EZ-pRPE layer over the scan area. Dice similarity, Bland-Altman analysis, correlation, and linear regression analyses were conducted to assess the agreement between DLM-MC and MG for the EZ area and OS volume measurements. For the EZ area, the overall mean dice score (SD) between DLM-MC and MG was 0.8524 (0.0821), which was comparable to 0.8417 (0.1111) between two MGs. For the EZ area > 1 mm2, the average dice score increased to 0.8799 (0.0614). When comparing DLM-MC to MG, the Bland-Altman plots revealed a mean difference (SE) of 0.0132 (0.0953) mm2 and a coefficient of repeatability (CoR) of 1.8303 mm2 for the EZ area and a mean difference (SE) of 0.0080 (0.0020) mm3 and a CoR of 0.0381 mm3 for the OS volume. The correlation coefficients (95% CI) were 0.9928 (0.9892-0.9952) and 0.9938 (0.9906-0.9958) for the EZ area and OS volume, respectively. The linear regression slopes (95% CI) were 0.9598 (0.9399-0.9797) and 1.0104 (0.9909-1.0298), respectively. The results from this study suggest that the manual correction of deep learning model segmentation can generate EZ area and OS volume measurements in excellent agreement with those of conventional manual grading in RP. Because DLM-MC is more efficient for retinal layer segmentation from OCT scan images, it has the potential to reduce the burden of human graders in obtaining quantitative measurements of biomarkers for assessing disease progression and treatment outcomes in RP.

Characteristics and management of children with Clostridioides difficile infection at a tertiary pediatric hospital in China

ABSTRACT Background: Clostridiodes difficile infection (CDI) is one of the most common causes of antibiotic-associated diarrhea in children. Conventional antibiotics and emerging fecal micro-biota transplantation (FMT) are used to treat CDI. Methods: Children with CDI admitted to the Shanghai Children's Hospital, from September 2014 to September 2020, were retrospectively included to this observational study. Pediatric patients were assigned as initial CDI and recurrent CDI (RCDI), and symptoms, comorbid-ities, imaging findings, laboratory tests, and treatments were systematically recorded and analyzed. Results: Of 109 pediatric patients with CDI, 58 were boys (53.2%), and the median age was 5 years (range, 2-9 years). The main clinical symptoms of CDI children were diarrhea (109/109, 100%), hematochezia (55/109, 50.46%), abdominal pain (40/109, 36.70%); fever, pseudomembrane, vomit, and bloating were observed in 39 (35.78%), 33 (30.28%), and 24 (22.02%) patients, respectively. For the primary therapy with conventional antibiotics, 68 patients received metronidazole, and 41 patients received vancomycin. RCDI occurred in 48.53% (33/68) of those initially treated with metronidazole compared with 46.33% (19/41) of those initially treated with vancomycin (p=0.825). The total resolution rate of FMT for RCDI children was significantly higher than with vancomycin treatment (28/29, 96.55% vs 11/23, 47.83%, p < 0.001). There were no serious adverse events (SAEs) reported after two months of FMT. Conclusions: The major manifestations of children with CDI were diarrhea, hematochezia, and abdominal pain. The cure rate of FMT for pediatric RCDI is superior to vancomycin treatment.