Factors associated with gastric and duodenal neuroendocrine tumors: A multicenter case-control study.

BACKGROUND AND AIMS: The incidence of gastric and duodenal neuroendocrine tumors (GNET and DNET, respectively) is increasing, however associated factors of these diseases are not well known. Here, we investigated the factors associated with GNET and DNET. METHODS: Patients with GNET and DNET presenting at eight tertiary referral centers between 2001 and 2020 were included and compared with healthy controls who underwent upper endoscopic screening. Clinical factors and laboratory data were analyzed to determine associated factors of GNET and DNET. RESULTS: Overall, 396 patients with GNET and 193 patients with DNET were included and compared with 1725 healthy controls. Multivariate analysis showed that age (odds ratio [OR] 0.98), diabetes (OR 1.72), hypertension (OR 1.97), low serum high-density lipoprotein cholesterol (HDL-C) levels (OR 2.54), and past/present H. pylori infection (OR 1.46) were significantly associated with GNET. In contrast, DNET was significantly associated with diabetes (OR 1.80), hypertension (OR 1.68), low serum HDL-C levels (OR 2.29), and past/present H. pylori infection (OR 5.42). In the sex-based subgroup analysis in GNET, current smoking was strongly associated in women (OR 9.85), but not in men. CONCLUSIONS: This study identified several common metabolic factors associated with GNET and DNET. Additionally, some factors had sex-specific associations.

Similarity-assisted variational autoencoder for nonlinear dimension reduction with application to single-cell RNA sequencing data.

BACKGROUND: Deep generative models naturally become nonlinear dimension reduction tools to visualize large-scale datasets such as single-cell RNA sequencing datasets for revealing latent grouping patterns or identifying outliers. The variational autoencoder (VAE) is a popular deep generative method equipped with encoder/decoder structures. The encoder and decoder are useful when a new sample is mapped to the latent space and a data point is generated from a point in a latent space. However, the VAE tends not to show grouping pattern clearly without additional annotation information. On the other hand, similarity-based dimension reduction methods such as t-SNE or UMAP present clear grouping patterns even though these methods do not have encoder/decoder structures. RESULTS: To bridge this gap, we propose a new approach that adopts similarity information in the VAE framework. In addition, for biological applications, we extend our approach to a conditional VAE to account for covariate effects in the dimension reduction step. In the simulation study and real single-cell RNA sequencing data analyses, our method shows great performance compared to existing state-of-the-art methods by producing clear grouping structures using an inferred encoder and decoder. Our method also successfully adjusts for covariate effects, resulting in more useful dimension reduction. CONCLUSIONS: Our method is able to produce clearer grouping patterns than those of other regularized VAE methods by utilizing similarity information encoded in the data via the highly celebrated UMAP loss function.

Sex-specific effects of fruit, vegetable, and red meat intake on the risk of gastric and esophageal cancer in a large cohort.

BACKGROUND: Dietary effects on gastric and esophageal cancer by sex and smoking has rarely been investigated. METHODS: Individuals who had undergone national gastric cancer screening during 2008 and had no any cancer at baseline were enrolled and followed up to 2017. The gastric and esophageal cancer risk was measured using adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs). RESULTS: Among 3.645 million (44.1% men), 45,741 gastric cancers (67.7% men) and 3,550 esophageal cancers (89.5% men) developed during 9 years follow-up. In adjusted analysis, a frequent intake of fruit (≥ 7 servings per week) reduced the gastric cancer risk (aHR=0.91; 95% CI, 0.83-0.99) comparing to nearly no intake in women but slightly increased male gastric cancer risk (aHR=1.06; 95% CI, 1.00-1.13). A frequent intake of dietary fruit reduced the esophageal cancer risk only in men (aHR=0.75; 95% CI, 0.62-0.92). Frequent intake of red meat (3-4/week) slightly increased the gastric cancer risk only in men (aHR=1.04; 95% CI, 1.01-1.09). The favorable effect of fruit on the gastric and esophageal cancer risk was observed only in never smoker. CONCLUSIONS: The effect of fruit and red meat intake on the gastric and esophageal cancer risk differed according to sex and smoking status.

Deep Nonnegative Matrix Factorization Using a Variational Autoencoder With Application to Single-Cell RNA Sequencing Data.

Single-cell RNA sequencing is used to analyze the gene expression data of individual cells, thereby adding to existing knowledge of biological phenomena. Accordingly, this technology is widely used in numerous biomedical studies. Recently, the variational autoencoder has emerged and has been adopted for the analysis of single-cell data owing to its high capacity to manage large-scale data. Many different variants of the variational autoencoder have been applied, and have yielded superior results. However, because it is nonlinear, the model does not provide parameters that can be used to explain the underlying biological patterns. In this paper, we propose an interpretable nonnegative matrix factorization method that decomposes parameters into those shared across cells and those that are cell-specific. Effective nonlinear dimension reduction was achieved via a variational autoencoder applied to the cell-specific parameters. In addition to achieving nonlinear dimension reduction, our model could estimate the cell-type-specific gene expression. To improve the estimation accuracy, we introduced log-regularization, which reflects the single-cell property. Overall, our approach displayed excellent performance in a simulation study and in real data analyses, while maintaining good biological interpretability.

Suppression of heterotopic ossification in fibrodysplasia ossificans progressiva using AAV gene delivery.

Heterotopic ossification is the most disabling feature of fibrodysplasia ossificans progressiva, an ultra-rare genetic disorder for which there is currently no prevention or treatment. Most patients with this disease harbor a heterozygous activating mutation (c.617 G > A;p.R206H) in ACVR1. Here, we identify recombinant AAV9 as the most effective serotype for transduction of the major cells-of-origin of heterotopic ossification. We use AAV9 delivery for gene replacement by expression of codon-optimized human ACVR1, ACVR1R206H allele-specific silencing by AAV-compatible artificial miRNA and a combination of gene replacement and silencing. In mouse skeletal cells harboring a conditional knock-in allele of human mutant ACVR1 and in patient-derived induced pluripotent stem cells, AAV gene therapy ablated aberrant Activin A signaling and chondrogenic and osteogenic differentiation. In Acvr1(R206H) knock-in mice treated locally in early adulthood or systemically at birth, trauma-induced endochondral bone formation was markedly reduced, while inflammation and fibroproliferative responses remained largely intact in the injured muscle. Remarkably, spontaneous heterotopic ossification also substantially decreased in in Acvr1(R206H) knock-in mice treated systemically at birth or in early adulthood. Collectively, we develop promising gene therapeutics that can prevent disabling heterotopic ossification in mice, supporting clinical translation to patients with fibrodysplasia ossificans progressiva.

Biphasic regulation of osteoblast development via the ERK MAPK-mTOR pathway.

Emerging evidence supports that osteogenic differentiation of skeletal progenitors is a key determinant of overall bone formation and bone mass. Despite extensive studies showing the function of mitogen-activated protein kinases (MAPKs) in osteoblast differentiation, none of these studies show in vivo evidence of a role for MAPKs in osteoblast maturation subsequent to lineage commitment. Here, we describe how the extracellular signal-regulated kinase (ERK) pathway in osteoblasts controls bone formation by suppressing the mechanistic target of rapamycin (mTOR) pathway. We also show that, while ERK inhibition blocks the differentiation of osteogenic precursors when initiated at an early stage, ERK inhibition surprisingly promotes the later stages of osteoblast differentiation. Accordingly, inhibition of the ERK pathway using a small compound inhibitor or conditional deletion of the MAP2Ks Map2k1 (MEK1) and Map2k2 (MEK2), in mature osteoblasts and osteocytes, markedly increased bone formation due to augmented osteoblast differentiation. Mice with inducible deletion of the ERK pathway in mature osteoblasts also displayed similar phenotypes, demonstrating that this phenotype reflects continuous postnatal inhibition of late-stage osteoblast maturation. Mechanistically, ERK inhibition increases mitochondrial function and SGK1 phosphorylation via mTOR2 activation, which leads to osteoblast differentiation and production of angiogenic and osteogenic factors to promote bone formation. This phenotype was partially reversed by inhibiting mTOR. Our study uncovers a surprising dichotomy of ERK pathway functions in osteoblasts, whereby ERK activation promotes the early differentiation of osteoblast precursors, but inhibits the subsequent differentiation of committed osteoblasts via mTOR-mediated regulation of mitochondrial function and SGK1.

FKBP11 rewires UPR signaling to promote glucose homeostasis in type 2 diabetes and obesity.

Chronic endoplasmic reticulum (ER) stress and sustained activation of unfolded protein response (UPR) signaling contribute to the development of type 2 diabetes in obesity. UPR signaling is a complex signaling pathway, which is still being explored in many different cellular processes. Here, we demonstrate that FK506-binding protein 11 (FKBP11), which is transcriptionally regulated by XBP1s, is severely reduced in the livers of obese mice. Restoring hepatic FKBP11 expression in obese mice initiates an atypical UPR signaling pathway marked by rewiring of PERK signaling toward NRF2, away from the eIF2α-ATF4 axis of the UPR. This alteration in UPR signaling establishes glucose homeostasis without changing hepatic ER stress, food consumption, or body weight. We conclude that ER stress during obesity can be beneficially rewired to promote glucose homeostasis. These findings may uncover possible new avenues in the development of novel approaches to treat diseases marked by ER stress.

Regulation of sclerostin by the SIRT1 stabilization pathway in osteocytes.

Osteocytes play a critical role in bone remodeling through the secretion of paracrine factors regulating the differentiation and activity of osteoblasts and osteoclasts. Sclerostin is a key osteocyte-derived factor that suppresses bone formation and promotes bone resorption, therefore regulators of sclerostin secretion are a likely source of new therapeutic strategies for treatment of skeletal disorders. Here, we demonstrate that protein kinase CK2 (casein kinase 2) controls sclerostin expression in osteocytes via the deubiquitinase ubiquitin-specific peptidase 4 (USP4)-mediated stabilization of Sirtuin1 (SIRT1). Deletion of CK2 regulatory subunit, Csnk2b, in osteocytes (Csnk2bDmp1) results in low bone mass due to elevated levels of sclerostin. This phenotype in Csnk2bDmp1 mice was partly reversed when sclerostin expression was downregulated by a single intravenous injection with bone-targeting adeno-associated virus 9 (AAV9) carrying an artificial-microRNA that targets Sost. Mechanistically, CK2-induced phosphorylation of USP4 is important for stabilization of SIRT1 by suppressing ubiquitin-dependent proteasomal degradation. Upregulated expression of SIRT1 inhibits sclerostin transcription in osteocytes. Collectively, the CK2-USP4-SIRT1 pathway is crucial for the regulation of sclerostin expression in osteocytes to maintain bone homeostasis.

scAVENGERS: a genotype-based deconvolution of individuals in multiplexed single-cell ATAC-seq data without reference genotypes.

Genetic differences inferred from sequencing reads can be used for demultiplexing of pooled single-cell RNA-seq (scRNA-seq) data across multiple donors without WGS-based reference genotypes. However, such methods could not be directly applied to single-cell ATAC-seq (scATAC-seq) data owing to the lower read coverage for each variant compared to scRNA-seq. We propose a new software, scATAC-seq Variant-based EstimatioN for GEnotype ReSolving (scAVENGERS), which resolves this issue by calling more individual-specific germline variants and using an optimized mixture model for the scATAC-seq. The benchmark conducted with three synthetic multiplexed scATAC-seq datasets of peripheral blood mononuclear cells and prefrontal cortex tissues showed outstanding performance compared to existing methods in terms of accuracy, doublet detection, and a portion of donor-assigned cells. Furthermore, analyzing the effect of the improved sections provided insight into handling pooled single-cell data in the future. Our source code of the devised software is available at GitHub: https://github.com/kaistcbfg/scAVENGERS.

A zero-inflated non-negative matrix factorization for the deconvolution of mixed signals of biological data.

A latent factor model for count data is popularly applied in deconvoluting mixed signals in biological data as exemplified by sequencing data for transcriptome or microbiome studies. Due to the availability of pure samples such as single-cell transcriptome data, the accuracy of the estimates could be much improved. However, the advantage quickly disappears in the presence of excessive zeros. To correctly account for this phenomenon in both mixed and pure samples, we propose a zero-inflated non-negative matrix factorization and derive an effective multiplicative parameter updating rule. In simulation studies, our method yielded the smallest bias. We applied our approach to brain gene expression as well as fecal microbiome datasets, illustrating the superior performance of the approach. Our method is implemented as a publicly available R-package, iNMF.

Proteomic and metabolomic profiling reveals the involvement of apoptosis in meat quality characteristics of ovine M. longissimus from different callipyge genotypes.

Proteome and metabolome changes in muscles from callipyge mutation (+/C) and non-callipyge phenotype (+/+, C/+, and C/C) lambs were profiled to provide insight into the biochemical changes affecting meat quality attributes. M. longissimus thoracis from lambs with all four possible callipyge genotype (n = 4, C/+, C/C, +/C, and +/+) were collected after 3d aging and analyzed using mass-spectrometry based platforms. Among identified proteomes, cytochrome c (pro-apoptotic protein) was detected with significantly lower abundances in +/C. Anti-apoptotic HSP70, BAG3, and PARK7 were over-abundant in +/C, which could result in delayed apoptosis and possibly attributed to tougher meat in callipyge lambs. Eight glycolysis enzymes were overabundant in +/C lambs, whereas 3 enzymes involved in TCA cycle were overabundant in non-callipyge ones (C/C and/or C/+). Twenty-five metabolites were affected by genotypes (P < .05), including metabolic co-factors, polyphenols, and AA/short peptides. Our omics results provided insightful information for revealing the differences in biochemical attributes caused by callipyge mutation.

FIREVAT: finding reliable variants without artifacts in human cancer samples using etiologically relevant mutational signatures.

BACKGROUND: Accurate identification of real somatic variants is a primary part of cancer genome studies and precision oncology. However, artifacts introduced in various steps of sequencing obfuscate confidence in variant calling. Current computational approaches to variant filtering involve intensive interrogation of Binary Alignment Map (BAM) files and require massive computing power, data storage, and manual labor. Recently, mutational signatures associated with sequencing artifacts have been extracted by the Pan-cancer Analysis of Whole Genomes (PCAWG) study. These spectrums can be used to evaluate refinement quality of a given set of somatic mutations. RESULTS: Here we introduce a novel variant refinement software, FIREVAT (FInding REliable Variants without ArTifacts), which uses known spectrums of sequencing artifacts extracted from one of the largest publicly available catalogs of human tumor samples. FIREVAT performs a quick and efficient variant refinement that accurately removes artifacts and greatly improves the precision and specificity of somatic calls. We validated FIREVAT refinement performance using orthogonal sequencing datasets totaling 384 tumor samples with respect to ground truth. Our novel method achieved the highest level of performance compared to existing filtering approaches. Application of FIREVAT on additional 308 The Cancer Genome Atlas (TCGA) samples demonstrated that FIREVAT refinement leads to identification of more biologically and clinically relevant mutational signatures as well as enrichment of sequence contexts associated with experimental errors. FIREVAT only requires a Variant Call Format file (VCF) and generates a comprehensive report of the variant refinement processes and outcomes for the user. CONCLUSIONS: In summary, FIREVAT facilitates a novel refinement strategy using mutational signatures to distinguish artifactual point mutations called in human cancer samples. We anticipate that FIREVAT results will further contribute to precision oncology efforts that rely on accurate identification of variants, especially in the context of analyzing mutational signatures that bear prognostic and therapeutic significance. FIREVAT is freely available at https://github.com/cgab-ncc/FIREVAT.

Comparison of the fecal, cecal, and mucus microbiome in male and female mice after TNBS-induced colitis.

Crohn's Disease and Ulcerative Colitis are chronic, inflammatory conditions of the digestive tract, collectively known as Inflammatory Bowel Disease (IBD). The combined influence of lifestyle factors, genetics, and the gut microbiome contribute to IBD pathogenesis. Studies of the gut microbiome have shown significant differences in its composition between healthy individuals and those with IBD. Due to the high inter-individual microbiome variation seen in humans, mouse models of IBD are often used to investigate potential IBD mechanisms and their interplay between host, microbial, and environmental factors. While fecal samples are the predominant material used for microbial community analysis, they may not be the ideal sample to use for analysis of the microbiome of mice with experimental colitis, such as that induced by 2, 4, 6 trinitrobenzesulfonic acid (TNBS). As TNBS is administered intrarectally to induce colitis and inflammation is confined to the colon in this model, we hypothesized that the microbiome of the colonic mucus would most closely correlate with TNBS colitis severity. Based on our previous research, we also hypothesized that sex would be associated with both disease severity and microbial differences in mice with chronic TNBS colitis. We examined and compared the fecal, cecal content, and colonic mucus microbiota of 8-week old male and female C57BL/6J wild-type mice prior to and after the induction of TNBS colitis via 16S rRNA gene sequencing. We found that the colonic mucus microbiome was more closely correlated with disease severity than were alterations in the fecal and cecal microbiomes. We also found that the microbiomes of the feces, cecum, and mucus were distinct, but found no significant differences that were associated with sex in either compartment. Our findings highlight the importance of sampling colonic mucus in TNBS-induced colitis. Moreover, consideration of the differential impact of sex on the microbiome across mouse strains may be critical for the appropriate application of TNBS colitis models and robust comparisons across studies in the future.

IL1R1 is required for celastrol's leptin-sensitization and antiobesity effects.

Celastrol, a pentacyclic triterpene, is the most potent antiobesity agent that has been reported thus far1. The mechanism of celastrol's leptin-sensitizing and antiobesity effects has not yet been elucidated. In this study, we identified interleukin-1 receptor 1 (IL1R1) as a mediator of celastrol's action by using temporally resolved analysis of the hypothalamic transcriptome in celastrol-treated DIO, lean, and db/db mice. We demonstrate that IL1R1-deficient mice are completely resistant to the effects of celastrol in leptin sensitization and treatment of obesity, diabetes, and nonalcoholic steatohepatitis. Thus, we conclude that IL1R1 is a gatekeeper for celastrol's metabolic actions.

Mutalisk: a web-based somatic MUTation AnaLyIS toolKit for genomic, transcriptional and epigenomic signatures.

Somatic genome mutations occur due to combinations of various intrinsic/extrinsic mutational processes and DNA repair mechanisms. Different molecular processes frequently generate different signatures of somatic mutations in their own favored contexts. As a result, the regional somatic mutation rate is dependent on the local DNA sequence, the DNA replication/RNA transcription dynamics and epigenomic chromatin organization landscape in the genome. Here, we propose an online computational framework, termed Mutalisk, which correlates somatic mutations with various genomic, transcriptional and epigenomic features in order to understand mutational processes that contribute to the generation of the mutations. This user-friendly tool explores the presence of localized hypermutations (kataegis), dissects the spectrum of mutations into the maximum likelihood combination of known mutational signatures and associates the mutation density with numerous regulatory elements in the genome. As a result, global patterns of somatic mutations in any query sample can be efficiently screened, thus enabling a deeper understanding of various mutagenic factors. This tool will facilitate more effective downstream analyses of cancer genome sequences to elucidate the diversity of mutational processes underlying the development and clonal evolution of cancer cells. Mutalisk is freely available at http://mutalisk.org.

Age, sex, and TNF associated differences in the gut microbiota of mice and their impact on acute TNBS colitis.

Mouse models are often used to determine the interactions between the microbiota and inflammatory processes and overcome the confounding effect of the naturally high inter-individual variation of the gut microbiota in humans. However, the microbiomes of mice are also variable and data detailing the degree to which factors like mouse sex and age contribute to mouse gut microbiota variation is limited. Our objective was to determine the impact sex and age have on the mouse gut microbiota and the severity of acute 2, 4, 6-trinitrobenzenesulfonic acid (TNBS) induced colitis. We used Illumina sequencing of 16S rRNA genes to characterize the fecal microbiota of B6.129S wild-type (WT) mice and mice lacking tumor necrosis factor (Tnf-/-) before and after acute TNBS colitis. There were differences between the fecal microbiota of male and female WT mice as well as Tnf-/- mice, both pre-and post-colitis. Male WT mice had more severe colitis than female WT mice and Tnf-/- mice of both sexes. We also identified microbial taxa differences between 4-5 and 6-7-week old WT and Tnf-/- mice both pre-and post-colitis. Here we provide evidence that the mouse fecal microbiome is shaped, in part, by sex, age and TNF production and that these effects correlate with the degree of animals' colitis.

Metabolomics Profiling to Determine the Effect of Postmortem Aging on Color and Lipid Oxidative Stabilities of Different Bovine Muscles.

The objective of this study was to identify metabolites that could be associated with oxidative stability of aged bovine muscles. Three muscles (longissimus lumbrum (LL), semimembranosus (SM), and psoas major (PM)) from seven beef carcasses at 1 day postmortem were divided into three sections and assigned to three aging periods (9, 16, and 23 days). Although an increase in discoloration was found in all muscles with aging, LL was the most color/lipid oxidative stable, followed by SM and PM (P < 0.05). Lower myoglobin and nonheme iron contents were observed in LL compared to those in SM and PM (P < 0.05). The HPLC-ESI-MS-based metabolomics analysis identified metabolites that were significantly responsive to aging and/or muscle type, such as acyl carnitines, free amino acids, nucleotides, nucleosides, and glucuronides. The results from the current study suggest that color and oxidative stability could be associated with aging but are also muscle-specific. Further studies determining the exact role of the identified metabolites in the color and oxidative stability of beef muscles are warranted.

Using Pre-Assessment and In-Class Questions to Change Student Understanding of Molecular Movements.

Understanding how different types of molecules move through cell membranes is a fundamental part of cell biology. To identify and address student misconceptions surrounding molecular movement through cell membranes, we surveyed student understanding on this topic using pre-class questions, in-class clicker questions, and subsequent exam questions in a large introductory biology course. Common misconceptions identified in student responses to the pre-class assessment questions were used to generate distractors for clicker questions. Two-tier diagnostic clicker questions were used to probe incoming common student misconceptions (first tier) and their reasoning (second tier). Two subsequent lectures with assessment clicker questions were used to help students construct a new framework to understand molecular movement through cell membranes. Comparison of pre-assessment and post-assessment (exam) performance showed dramatic improvement in students' understanding of molecular movement: student answers to exam questions were 74.6% correct with correct reasoning while only 1.3% of the student answers were correct with correct reasoning on the pre-class assessment. Our results show that students' conceptual understanding of molecular movement through cell membranes progressively increases through discussions of a series of clicker questions and suggest that this clicker-based teaching strategy was highly effective in correcting common student misconceptions on this topic.

Gene regulation network inference with joint sparse Gaussian graphical models.

Revealing biological networks is one key objective in systems biology. With microarrays, researchers now routinely measure expression profiles at the genome level under various conditions, and, such data may be utilized to statistically infer gene regulation networks. Gaussian graphical models (GGMs) have proven useful for this purpose by modeling the Markovian dependence among genes. However, a single GGM may not be adequate to describe the potentially differing networks across various conditions, and hence it is more natural to infer multiple GGMs from such data. In the present study, we propose a class of nonconvex penalty functions aiming at the estimation of multiple GGMs with a flexible joint sparsity constraint. We illustrate the property of our proposed nonconvex penalty functions by simulation study. We then apply the method to a gene expression data set from the GenCord Project, and show that our method can identify prominent pathways across different conditions.