Gut microbiota plays a significant role in gout.

With the development of social economy, the incidence of gout is increasing, which is closely related to people's increasingly rich diet. Eating a diet high in purine, fat, sugar and low-fibre for a long time further aggravates gout by affecting uric acid metabolism. The renal metabolism mechanism of uric acid has been thoroughly studied. To find a new treatment method for gout, increasing studies have recently been conducted on the mechanism of intestinal excretion, metabolism and absorption of uric acid. The most important research is the relationship between intestinal microbiota and the risk of gout. Gut microbiota represent bacteria that reside in a host's gastrointestinal tract. The composition of the gut microbiota is associated with protection against pathogen colonization and disease occurrence. This review focuses on how gut microbiota affects gout through uric acid and discusses the types of bacteria that may be involved in the occurrence and progression of gout. We also describe potential therapy for gout by restoring gut microbiota homeostasis and reducing uric acid levels. We hold the perspective that changing intestinal microbiota may become a vital method for effectively preventing or treating gout.

Perspective view of allogeneic IgG tumor immunotherapy.

Allogeneic tumors are eradicated by host immunity; however, it is unknown how it is initiated until the report in Nature by Yaron Carmi et al. in 2015. Currently, we know that allogeneic tumors are eradicated by allogeneic IgG via dendritic cells. AlloIgG combined with the dendritic cell stimuli tumor necrosis factor alpha and CD40L induced tumor eradication via the reported and our proposed potential signaling pathways. AlloIgG triggers systematic immune responses targeting multiple antigens, which is proposed to overcome current immunotherapy limitations. The promising perspectives of alloIgG immunotherapy would have advanced from mouse models to clinical trials; however, there are only 6 published articles thus far. Therefore, we hope this perspective view will provide an initiative to promote future discussion.

Spatial transcriptomics reveals heterogeneity of histological subtypes between lepidic and acinar lung adenocarcinoma.

BACKGROUND: Patients who possess various histological subtypes of early-stage lung adenocarcinoma (LUAD) have considerably diverse prognoses. The simultaneous existence of several histological subtypes reduces the clinical accuracy of the diagnosis and prognosis of early-stage LUAD due to intratumour intricacy. METHODS: We included 11 postoperative LUAD patients pathologically confirmed to be stage IA. Single-cell RNA sequencing (scRNA-seq) was carried out on matched tumour and normal tissue. Three formalin-fixed and paraffin-embedded cases were randomly selected for 10× Genomics Visium analysis, one of which was analysed by digital spatial profiler (DSP). RESULTS: Using DSP and 10× Genomics Visium analysis, signature gene profiles for lepidic and acinar histological subtypes were acquired. The percentage of histological subtypes predicted for the patients from samples of 11 LUAD fresh tissues by scRNA-seq showed a degree of concordance with the clinicopathologic findings assessed by visual examination. DSP proteomics and 10× Genomics Visium transcriptomics analyses revealed that a negative correlation (Spearman correlation analysis: r = -.886; p = .033) between the expression levels of CD8 and the expression trend of programmed cell death 1(PD-L1) on tumour endothelial cells. The percentage of CD8+ T cells in the acinar region was lower than in the lepidic region. CONCLUSIONS: These findings illustrate that assessing patient histological subtypes at the single-cell level is feasible. Additionally, tumour endothelial cells that express PD-L1 in stage IA LUAD suppress immune-responsive CD8+ T cells.

CRCS: An automatic image processing pipeline for hormone level analysis of Cushing's disease.

Due to the abnormal secretion of adreno-cortico-tropic-hormone (ACTH) by tumors, Cushing's disease leads to hypercortisonemia, a precursor to a series of metabolic disorders and serious complications. Cushing's disease has high recurrence rate, short recurrence time and undiscovered recurrence reason after surgical resection. Qualitative or quantitative automatic image analysis of histology images can potentially in providing insights into Cushing's disease, but still no software has been available to the best of our knowledge. In this study, we propose a quantitative image analysis-based pipeline CRCS, which aims to explore the relationship between the expression level of ACTH in normal cell tissues adjacent to tumor cells and the postoperative prognosis of patients. CRCS mainly consists of image-level clustering, cluster-level multi-modal image registration, patch-level image classification and pixel-level image segmentation on the whole slide imaging (WSI). On both image registration and classification tasks, our method CRCS achieves state-of-the-art performance compared to recently published methods on our collected benchmark dataset. In addition, CRCS achieves an accuracy of 0.83 for postoperative prognosis of 12 cases. CRCS demonstrates great potential for instrumenting automatic diagnosis and treatment for Cushing's disease.

Recent Advances on the Molecular Mechanism and Clinical Trials of Venous Thromboembolism.

Venous thromboembolism is a condition that includes deep vein thrombosis and pulmonary embolism. It is the third most common cardiovascular disease behind acute coronary heart disease and stroke. Over the past few years, growing research suggests that venous thrombosis is also related to the immune system and inflammatory factors have been confirmed to be involved in venous thrombosis. The role of inflammation and inflammation-related biomarkers in cerebrovascular thrombotic disease is the subject of ongoing debate. P-selectin leads to platelet-monocyte aggregation and stimulates vascular inflammation and thrombosis. The dysregulation of miRNAs has also been reported in venous thrombosis, suggesting the involvement of miRNAs in the progression of venous thrombosis. Plasminogen activator inhibitor-1 (PAI-1) is a crucial component of the plasminogen-plasmin system, and elevated levels of PAI-1 in conjunction with advanced age are significant risk factors for thrombosis. In addition, it has been showed that one of the ways that neutrophils promote venous thrombosis is the formation of neutrophil extracellular traps (NETs). In recent years, the role of extracellular vesicles (EVs) in the occurrence and development of VTE has been continuously revealed. With the advancement of research technology, the complex regulatory role of EVs on the coagulation process has been gradually discovered. However, our understanding of the causes and consequences of these changes in venous thrombosis is still limited. Therefore, we review our current understanding the molecular mechanisms of venous thrombosis and the related clinical trials, which is crucial for the future treatment of venous thrombosis.

Oncolytic virotherapy evolved into the fourth generation as tumor immunotherapy.

BACKGROUND: Oncolytic virotherapy (OVT) is a promising anti-tumor modality that utilizes oncolytic viruses (OVs) to preferentially attack cancers rather than normal tissues. With the understanding particularly in the characteristics of viruses and tumor cells, numerous innovative OVs have been engineered to conquer cancers, such as Talimogene Laherparepvec (T-VEC) and tasadenoturev (DNX-2401). However, the therapeutic safety and efficacy must be further optimized and balanced to ensure the superior safe and efficient OVT in clinics, and reasonable combination therapy strategies are also important challenges worthy to be explored. MAIN BODY: Here we provided a critical review of the development history and status of OVT, emphasizing the mechanisms of enhancing both safety and efficacy. We propose that oncolytic virotherapy has evolved into the fourth generation as tumor immunotherapy. Particularly, to arouse T cells by designing OVs expressing bi-specific T cell activator (BiTA) is a promising strategy of killing two birds with one stone. Amazing combination of therapeutic strategies of OVs and immune cells confers immense potential for managing cancers. Moreover, the attractive preclinical OVT addressed recently, and the OVT in clinical trials were systematically reviewed. CONCLUSION: OVs, which are advancing into clinical trials, are being envisioned as the frontier clinical anti-tumor agents coming soon.

Nanoparticles advanced from preclinical studies to clinical trials for lung cancer therapy.

Lung cancer is the leading cause of cancer mortality. As a heterogeneous disease, it has different subtypes and various treatment modalities. In addition to conventional surgery, radiotherapy and chemotherapy, targeted therapy and immunotherapy have also been applied in the clinics. However, drug resistance and systemic toxicity still cannot be avoided. Based on the unique properties of nanoparticles, it provides a new idea for lung cancer therapy, especially for targeted immunotherapy. When nanoparticles are used as carriers of drugs with special physical properties, the nanodrug delivery system ensures the accuracy of targeting and the stability of drugs while increasing the permeability and the aggregation of drugs in tumor tissues, showing good anti-tumor effects. This review introduces the properties of various nanoparticles including polymer nanoparticles, liposome nanoparticles, quantum dots, dendrimers, and gold nanoparticles and their applications in tumor tissues. In addition, the specific application of nanoparticle-based drug delivery for lung cancer therapy in preclinical studies and clinical trials is discussed.

Deep learning of endoscopic features for the assessment of neoadjuvant therapy response in locally advanced rectal cancer.

BACKGROUND: For locally advanced rectal cancer (LARC), accurate response evaluation is necessary to select complete responders after neoadjuvant therapy (NAT) for a watch-and-wait (W&W) strategy. Algorithms based on deep learning have shown great value in medical image analyses. Here we used deep learning algorithms of endoscopic images for the assessment of NAT response in LARC. METHOD: 214 LARC patients were retrospectively included in the study. After NAT, these patients underwent total mesorectal excision (TME) surgery. Among them, 51 (23.8%) of the patients achieved a pathological complete response (pCR). 160 patients from Shanghai Changzheng Hospital were regarded as primary dataset, and the other 54 patients from Zhejiang Cancer Hospital were regarded as validation dataset. ResNet-18 and DenseNet-121 were applied to train the models based on endoscopic images after NAT. Deep learning models were valid in the validation dataset and compared to manual method. RESULTS: The performances were comparable in AUC between deep learning models and manual method. For mean metrics, sensitivity (0.750 vs. 0.417) and AUC (0.716 vs. 0.601) in ResNet-18 deep learning model were higher than those in the manual method. The deep learning models were able to identify the endoscopic features associated with NAT response by the heatmaps. A diagnostic flow diagram which integrated the deep learning model to assist the clinicians in making decisions for W&W strategy was constructed. CONCLUSIONS: We created deep learning models using endoscopic features for assessment of NAT in LARC. The deep learning models achieved modest accuracies and performed comparably to manual method.

The microbiota regulates hematopoietic stem and progenitor cell development by mediating inflammatory signals in the niche.

The commensal microbiota regulates the self-renewal and differentiation of hematopoietic stem and progenitor cells (HSPCs) in bone marrow. Whether and how the microbiota influences HSPC development during embryogenesis is unclear. Using gnotobiotic zebrafish, we show that the microbiota is necessary for HSPC development and differentiation. Individual bacterial strains differentially affect HSPC formation, independent of their effects on myeloid cells. Early-life dysbiosis in chd8-/- zebrafish impairs HSPC development. Wild-type microbiota promote HSPC development by controlling basal inflammatory cytokine expression in kidney niche, and chd8-/- commensals elicit elevated inflammatory cytokines that reduce HSPCs and enhance myeloid differentiation. We identify an Aeromonas veronii strain with immuno-modulatory activities that fails to induce HSPC development in wild-type fish but selectively inhibits kidney cytokine expression and rebalances HSPC development in chd8-/- zebrafish. Our studies highlight the important roles of a balanced microbiome during early HSPC development that ensure proper establishment of lineal precursor for adult hematopoietic system.

TIST: Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics.

Sequencing-based spatial transcriptomics (ST) is an emerging technology to study in situ gene expression patterns at the whole-genome scale. Currently, ST data analysis is still complicated by high technical noises and low resolution. In addition to the transcriptomic data, matched histopathological images are usually generated for the same tissue sample along the ST experiment. The matched high-resolution histopathological images provide complementary cellular phenotypical information, providing an opportunity to mitigate the noises in ST data. We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST (TIST), which enables the identification of spatial clusters (SCs) and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images. TIST devises a histopathological feature extraction method based on Markov random field (MRF) to learn the cellular features from histopathological images, and integrates them with the transcriptomic data and location information as a network, termed TIST-net. Based on TIST-net, SCs are identified by a random walk-based strategy, and gene expression patterns are enhanced by neighborhood smoothing. We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods. Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological scenarios. TIST is available at http://lifeome.net/software/tist/ and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.

Bispecific T cell engagers and their synergistic tumor immunotherapy with oncolytic viruses.

Tumor immunotherapy, especially T cell based therapy, is becoming the main force in clinical tumor therapies. Bispecific T cell engager (BiTE) uses the single chain variable fragments (scFv) of two antibodies to redirect T cells to kill target cells. BiTEs for hematologic tumors has been approved for clinical use, and BiTEs for solid tumors showed therapeutic effects in clinical trials. Oncolytic viruses (OVs) of the adenovirus expressing p53 and herpes simplex virus expressing GM-CSF was approved for clinical use in 2003 and 2015, respectively, while other OVs showed therapeutic effects in clinical trials. However, BiTE and Oncolytic virus (OV) have their own limitations. We propose that OV-BiTE has a synergistic effect on tumor immunotherapy. Feng Yu et al. designed the first OV-BiTE in 2014, which remarkably eradicated tumors in mice. Here we review the latest development of the structure, function, preclinical studies and/or clinical trials of BiTE and OV-BiTE and provide perspective views for optimizing the design of OV-BiTE. There is no doubt that OV-BiTE is becoming an exciting new platform for tumor immunotherapy and will enter clinical trial soon. Exploring the therapeutic effects and safety of OV-BiTE for synergistic tumor immunotherapy will bring new hope to tumor patients.

SODA: Detecting COVID-19 in Chest X-rays with Semi-supervised Open Set Domain Adaptation.

Due to the shortage of COVID-19 viral testing kits, radiology is used to complement the screening process. Deep learning methods are promising in automatically detecting COVID-19 disease in chest x-ray images. Most of these works first train a Convolutional Neural Network (CNN) on an existing large-scale chest x-ray image dataset and then fine-tune the model on the newly collected COVID-19 chest x-ray dataset, often at a much smaller scale. However, simple fine-tuning may lead to poor performance due to two issues, firstly the large domain shift present in chest x-ray datasets and secondly the relatively small scale of the COVID-19 chest x-ray dataset. In an attempt to address these issues, we formulate the problem of COVID-19 chest x-ray image classification in a semi-supervised open set domain adaptation setting and propose a novel domain adaptation method, Semi-supervised Open set Domain Adversarial network (SODA). SODA is designed to align the data distributions across different domains in the general domain space and also in the common subspace of source and target data. In our experiments, SODA achieves a leading classification performance compared with recent state-of-the-art models in separating COVID-19 with common pneumonia. We also present results showing that SODA produces better pathology localizations.

Gut microbiota homeostasis restoration may become a novel therapy for breast cancer.

Breast cancer is the most diagnosed cancer in women. It significantly impairs a patient's physical and mental health. Gut microbiota comprise the bacteria residing in a host's gastrointestinal tract. Through studies over the last decade, we now know that alterations in the composition of the gut microbiome are associated with protection against colonization by pathogens and other diseases, such as diabetes and cancer. This review focuses on how gut microbiota can affect breast cancer development through estrogen activity and discusses the types of bacteria that may be involved in the onset and the progression of breast cancer. We also describe potential therapies to curtail the risk of breast cancer by restoring gut microbiota homeostasis and reducing systemic estrogen levels. This review will further explore the relationship between intestinal microbes and breast cancer and propose a method to treat breast cancer by improving intestinal microbes. We aimed at discovering new methods to prevent or treat BC by changing intestinal microorganisms.

GMM-Demux: sample demultiplexing, multiplet detection, experiment planning, and novel cell-type verification in single cell sequencing.

Identifying and removing multiplets are essential to improving the scalability and the reliability of single cell RNA sequencing (scRNA-seq). Multiplets create artificial cell types in the dataset. We propose a Gaussian mixture model-based multiplet identification method, GMM-Demux. GMM-Demux accurately identifies and removes multiplets through sample barcoding, including cell hashing and MULTI-seq. GMM-Demux uses a droplet formation model to authenticate putative cell types discovered from a scRNA-seq dataset. We generate two in-house cell-hashing datasets and compared GMM-Demux against three state-of-the-art sample barcoding classifiers. We show that GMM-Demux is stable and highly accurate and recognizes 9 multiplet-induced fake cell types in a PBMC dataset.

Artificial-cell-type aware cell-type classification in CITE-seq.

MOTIVATION: Cellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), couples the measurement of surface marker proteins with simultaneous sequencing of mRNA at single cell level, which brings accurate cell surface phenotyping to single-cell transcriptomics. Unfortunately, multiplets in CITE-seq datasets create artificial cell types (ACT) and complicate the automation of cell surface phenotyping. RESULTS: We propose CITE-sort, an artificial-cell-type aware surface marker clustering method for CITE-seq. CITE-sort is aware of and is robust to multiplet-induced ACT. We benchmarked CITE-sort with real and simulated CITE-seq datasets and compared CITE-sort against canonical clustering methods. We show that CITE-sort produces the best clustering performance across the board. CITE-sort not only accurately identifies real biological cell types (BCT) but also consistently and reliably separates multiplet-induced artificial-cell-type droplet clusters from real BCT droplet clusters. In addition, CITE-sort organizes its clustering process with a binary tree, which facilitates easy interpretation and verification of its clustering result and simplifies cell-type annotation with domain knowledge in CITE-seq. AVAILABILITY AND IMPLEMENTATION: http://github.com/QiuyuLian/CITE-sort. SUPPLEMENTARY INFORMATION: Supplementary data is available at Bioinformatics online.

Context-aware seeds for read mapping.

MOTIVATION: Most modern seed-and-extend NGS read mappers employ a seeding scheme that requires extracting t non-overlapping seeds in each read in order to find all valid mappings under an edit distance threshold of t. As t grows, this seeding scheme forces mappers to use more and shorter seeds, which increases the seed hits (seed frequencies) and therefore reduces the efficiency of mappers. RESULTS: We propose a novel seeding framework, context-aware seeds (CAS). CAS guarantees finding all valid mappings but uses fewer (and longer) seeds, which reduces seed frequencies and increases efficiency of mappers. CAS achieves this improvement by attaching a confidence radius to each seed in the reference. We prove that all valid mappings can be found if the sum of confidence radii of seeds are greater than t. CAS generalizes the existing pigeonhole-principle-based seeding scheme in which this confidence radius is implicitly always 1. Moreover, we design an efficient algorithm that constructs the confidence radius database in linear time. We experiment CAS with E. coli genome and show that CAS significantly reduces seed frequencies when compared with the state-of-the-art pigeonhole-principle-based seeding algorithm, the Optimal Seed Solver. AVAILABILITY: https://github.com/Kingsford-Group/CAS_code.

BREM-SC: a bayesian random effects mixture model for joint clustering single cell multi-omics data.

Droplet-based single cell transcriptome sequencing (scRNA-seq) technology, largely represented by the 10× Genomics Chromium system, is able to measure the gene expression from tens of thousands of single cells simultaneously. More recently, coupled with the cutting-edge Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), the droplet-based system has allowed for immunophenotyping of single cells based on cell surface expression of specific proteins together with simultaneous transcriptome profiling in the same cell. Despite the rapid advances in technologies, novel statistical methods and computational tools for analyzing multi-modal CITE-Seq data are lacking. In this study, we developed BREM-SC, a novel Bayesian Random Effects Mixture model that jointly clusters paired single cell transcriptomic and proteomic data. Through simulation studies and analysis of public and in-house real data sets, we successfully demonstrated the validity and advantages of this method in fully utilizing both types of data to accurately identify cell clusters. In addition, as a probabilistic model-based approach, BREM-SC is able to quantify the clustering uncertainty for each single cell. This new method will greatly facilitate researchers to jointly study transcriptome and surface proteins at the single cell level to make new biological discoveries, particularly in the area of immunology.

Deep-learning-based Prediction of Late Age-Related Macular Degeneration Progression.

Both genetic and environmental factors influence the etiology of age-related macular degeneration (AMD), a leading cause of blindness. AMD severity is primarily measured by fundus images and recently developed machine learning methods can successfully predict AMD progression using image data. However, none of these methods have utilized both genetic and image data for predicting AMD progression. Here we jointly used genotypes and fundus images to predict an eye as having progressed to late AMD with a modified deep convolutional neural network (CNN). In total, we used 31,262 fundus images and 52 AMD-associated genetic variants from 1,351 subjects from the Age-Related Eye Disease Study (AREDS) with disease severity phenotypes and fundus images available at baseline and follow-up visits over a period of 12 years. Our results showed that fundus images coupled with genotypes could predict late AMD progression with an averaged area under the curve (AUC) value of 0.85 (95%CI: 0.83-0.86). The results using fundus images alone showed an averaged AUC of 0.81 (95%CI: 0.80-0.83). We implemented our model in a cloud-based application for individual risk assessment.

A Bayesian mixture model for clustering droplet-based single-cell transcriptomic data from population studies.

The recently developed droplet-based single-cell transcriptome sequencing (scRNA-seq) technology makes it feasible to perform a population-scale scRNA-seq study, in which the transcriptome is measured for tens of thousands of single cells from multiple individuals. Despite the advances of many clustering methods, there are few tailored methods for population-scale scRNA-seq studies. Here, we develop a Bayesian mixture model for single-cell sequencing (BAMM-SC) method to cluster scRNA-seq data from multiple individuals simultaneously. BAMM-SC takes raw count data as input and accounts for data heterogeneity and batch effect among multiple individuals in a unified Bayesian hierarchical model framework. Results from extensive simulation studies and applications of BAMM-SC to in-house experimental scRNA-seq datasets using blood, lung and skin cells from humans or mice demonstrate that BAMM-SC outperformed existing clustering methods with considerable improved clustering accuracy, particularly in the presence of heterogeneity among individuals.

Identification of Putative UL54 (ICP27) Transcription Regulatory Sequences Binding to Oct-1, v-Myb, Pax-6 and Hairy in Herpes Simplex Viruses.

An oncolytic herpes simplex virus (oHSV) has proven amenable in oncolytic virotherapy and was approved to treat melanoma. The immediate-early (IE) protein ICP27 encoded by gene UL54 is essential for HSV infection. Post-transcriptional modification of UL54 would increase tumor targeting of oHSVs. However, UL54 gene transcription regulatory sequences and factors were not reported yet. Here we isolated a new strain LXMW of type 1 HSV (HSV-1-LXMW) in China and found it's closely related to HSV-1 strains Patton and H129 in the US by the first and next generation DNA sequencing viral DNA phylogenetic analysis. Using a weight matrix-based program Match, we found the UL54 transcription regulatory sequences binding to the transcription factors Oct-1, v-Myb and Pax-6 in HSV-1-LXMW, while the sequences binding to Oct-1 and Hairy in a HSV-2 strain. Further validation showed that HSV-1 and HSV-2 shared the common sequence binding to Oct-1, but had unique sequences to bind v-Myb and Pax-6, or Hairy, respectively, by DNA sequence alignment of total 11 HSV strains. The published results howed that the expression of transcription factors is consistent with the tissue tropism of HSV-1 and HSV-2. In the current article a new HSV-1 strain LXMW was isolated and its putative HSV UL54 transcription regulatory sequences and factors were identified for the first time. Our findings highlight the new understanding of the principles of transcriptional regulation in HSV biology and oncolytic virotherapy.