vSPACE: Exploring Virtual Spatial Representation of Articular Chondrocytes at the Single-Cell Level.

Single cell RNA sequencing technology has been dramatically changing how gene expression studies are performed. However, its use has been limited to identifying subtypes of cells by comparing cells' gene expression levels in an unbiased manner to produce a 2D plot (e.g., UMAP/tSNE). We developed a new method of placing cells in 2D space. This system, called vSPACE, shows a virtual spatial representation of scRNAseq data obtained from human articular cartilage by emulating the concept of spatial transcriptomics technology, but virtually. This virtual 2D plot presentation of human articular cartage cells generates several zonal distribution patterns, in one or multiple genes at a time, reveling patterns that scientists can appreciate as imputed spatial distribution patterns along the zonal axis. The discovered patterns are explainable and remarkably consistent across all six healthy doners despite their respectively different clinical variables (age and sex), suggesting the confidence of the discovered patterns.

Effectiveness and Safety of Progressive Loading-Motion Style Acupuncture Treatment for Acute Low Back Pain after Traffic Accidents: A Randomized Controlled Trial.

BACKGROUND: Traffic injuries include acute low back pain (LBP) needing active treatment to prevent chronicity. This two-armed, parallel, assessor-blinded, randomized controlled trial evaluated the effectiveness and safety of progressive loading-motion style acupuncture treatment (PL-MSAT) for acute LBP following traffic accidents. METHODS: Based on an effect size of 1.03, 104 participants were recruited and divided in a 1:1 ratio into PL-MAST and control groups using block randomization. Both groups underwent integrative Korean medicine treatment (IKMT) daily; only the PL-MSAT group underwent three PL-MSAT sessions. The outcomes were assessed before and after the treatment sessions and at 1 and 3 months post-discharge. The primary outcome was the difference in the numeric rating scale (NRS) for LBP. The secondary outcomes included a visual analog scale for LBP, leg pain status, the Oswestry disability index, lumbar active range of motion (ROM), quality of life, Patient Global Impression of Change, and Post-Traumatic Stress Disorder Checklist adverse events. RESULTS: In the modified intention-to-treat analysis, 50 and 51 participants were included in the PL-MSAT and control groups. On Day 4, the mean LBP NRS score was 3.67 (3.44-3.90) in the PL-MSAT group, indicating a significantly lower NRS 0.77 (0.44-1.11) compared to 4.44 (4.20-4.68) for the control group (p < 0.001). The PL-MSAT group exhibited greater ROM flexion (-5.31; -8.15 to -2.48) and extension (-2.09; -3.39 to -0.80). No significant differences were found for the secondary outcomes and follow-ups. CONCLUSIONS: Compared with IKMT alone, PL-MSAT plus IKMT showed significantly better outcomes for reducing pain and increasing the ROM in acute LBP.

CGCom: a framework for inferring Cell-cell Communication based on Graph Neural Network.

Cell-cell communication is crucial in maintaining cellular homeostasis, cell survival and various regulatory relationships among interacting cells. Thanks to recent advances of spatial transcriptomics technologies, we can now explore if and how cells' proximal information available from spatial transcriptomics datasets can be used to infer cell-cell communication. Here we present a cell-cell communication inference framework, called CGCom, which uses a graph neural network (GNN) to learn communication patterns among interacting cells by combining single-cell spatial transcriptomic datasets with publicly available ligand-receptor information and the molecular regulatory information down-stream of the ligand-receptor signaling. To evaluate the performance of CGCom, we applied it to mouse embryo seqFISH datasets. Our results demonstrate that CGCom can not only accurately infer cell communication between individual cell pairs but also generalize its learning to predict communication between different cell types. We compared the performance of CGCom with two existing methods, CellChat and CellPhoneDB, and our comparative study revealed both common and unique communication patterns from the three approaches. Commonly found communication patterns include three sets of ligand-receptor communication relationships, one between surface ectoderm cells and spinal cord cells, one between gut tube cells and endothelium, and one between neural crest and endothelium, all of which have already been reported in the literature thus offering credibility of all three methods. However, we hypothesize that CGCom is superior in reducing false positives thanks to its use of cell proximal information and its learning between specific cell pairs rather than between cell types. CGCom is a GNN-based solution that can take advantage of spatially resolved single-cell transcriptomic data in predicting cell-cell communication with a higher accuracy.

Single-cell Transcriptome Landscape of DNA Methylome Regulators Associated with Orofacial Clefts in the Mouse Dental Pulp.

OBJECTIVE: Significant evidence links epigenetic processes governing the dynamics of DNA methylation and demethylation to an increased risk of syndromic and nonsyndromic cleft lip and/or cleft palate (CL/P). Previously, we characterized mesenchymal stem/stromal cells (MSCs) at different stages of osteogenic differentiation in the mouse incisor dental pulp. The main objective of this research was to characterize the transcriptional landscape of regulatory genes associated with DNA methylation and demethylation at a single-cell resolution. DESIGN: We used single-cell RNA sequencing (scRNA-seq) data to characterize transcriptome in individual subpopulations of MSCs in the mouse incisor dental pulp. SETTINGS: The biomedical research institution. PATIENTS/PARTICIPANTS: This study did not include patients. INTERVENTIONS: This study collected and analyzed data on the single-cell RNA expssion in the mouse incisor dental pulp. MAIN OUTCOME MEASURE(S): Molecular regulators of DNA methylation/demethylation exhibit differential transcriptional landscape in different subpopulations of osteogenic progenitor cells. RESULTS: scRNA-seq analysis revealed that genes encoding DNA methylation and demethylation enzymes (DNA methyltransferases and members of the ten-eleven translocation family of methylcytosine dioxygenases), methyl-DNA binding domain proteins, as well as transcription factors and chromatin remodeling proteins that cooperate with DNA methylation machinery are differentially expressed within distinct subpopulations of MSCs that undergo different stages of osteogenic differentiation. CONCLUSIONS: These findings suggest some mechanistic insights into a potential link between epigenetic alterations and multifactorial causes of CL/P phenotypes.

Single-Cell Transcriptome Analysis Defines Expression of Kabuki Syndrome-Associated KMT2D Targets and Interacting Partners.

Objectives. Kabuki syndrome (KS) is a rare genetic disorder characterized by developmental delay, retarded growth, and cardiac, gastrointestinal, neurocognitive, renal, craniofacial, dental, and skeletal defects. KS is caused by mutations in the genes encoding histone H3 lysine 4 methyltransferase (KMT2D) and histone H3 lysine 27 demethylase (KDM6A), which are core components of the complex of proteins associated with histone H3 lysine 4 methyltransferase SET1 (SET1/COMPASS). Using single-cell RNA data, we examined the expression profiles of Kmt2d and Kdm6a in the mouse dental pulp. In the incisor pulp, Kmt2d and Kdm6a colocalize with other genes of the SET1/COMPASS complex comprised of the WD-repeat protein 5 gene (Wdr5), the retinoblastoma-binding protein 5 gene (Rbbp5), absent, small, and homeotic 2-like protein-encoding gene (Ash2l), nuclear receptor cofactor 6 gene (Ncoa6), and Pax-interacting protein 1 gene (Ptip1). In addition, we found that Kmt2d and Kdm6a coexpress with the downstream target genes of the Wingless and Integrated (WNT) and sonic hedgehog signaling pathways in mesenchymal stem/stromal cells (MSCs) at different stages of osteogenic differentiation. Taken together, our results suggest an essential role of KMT2D and KDK6A in directing lineage-specific gene expression during differentiation of MSCs.

The chromatin accessibility landscape in the dental pulp of mouse molars and incisors.

The dental pulp is a promising source of progenitor cells for regenerative medicine. The natural function of dental pulp is to produce odontoblasts to generate reparative dentin. Stem cells within the pulp tissue originate from the migrating neural crest cells and possess mesenchymal stem cell properties with the ability to differentiate into multiple lineages. To elucidate the transcriptional control mechanisms underlying cell fate determination, we compared the transcriptome and chromatin accessibility in primary dental pulp tissue derived from 5-6-day-old mice. Using RNA sequencing and assay for transposase-accessible chromatin using sequencing (ATAC-seq), we correlated gene expression with chromatin accessibility. We found that the majority of ATAC-seq peaks were concentrated at genes associated with development and cell differentiation. Most of these genes were highly expressed in the mouse dental pulp. Surprisingly, we uncovered a group of genes encoding master transcription factors that were not expressed in the dental pulp but retained open chromatin states. Within this group, we identified key developmental genes important for specification of the neural crest, adipocyte, neural, myoblast, osteoblast and hepatocyte lineages. Collectively, our results uncover a complex relationship between gene expression and the chromatin accessibility landscape in the mouse dental pulp.

Predicting the targets of IRF8 and NFATc1 during osteoclast differentiation using the machine learning method framework cTAP.

BACKGROUND: Interferon regulatory factor-8 (IRF8) and nuclear factor-activated T cells c1 (NFATc1) are two transcription factors that have an important role in osteoclast differentiation. Thanks to ChIP-seq technology, scientists can now estimate potential genome-wide target genes of IRF8 and NFATc1. However, finding target genes that are consistently up-regulated or down-regulated across different studies is hard because it requires analysis of a large number of high-throughput expression studies from a comparable context. METHOD: We have developed a machine learning based method, called, Cohort-based TF target prediction system (cTAP) to overcome this problem. This method assumes that the pathway involving the transcription factors of interest is featured with multiple "functional groups" of marker genes pertaining to the concerned biological process. It uses two notions, Gene-Present Sufficiently (GP) and Gene-Absent Insufficiently (GA), in addition to log2 fold changes of differentially expressed genes for the prediction. Target prediction is made by applying multiple machine-learning models, which learn the patterns of GP and GA from log2 fold changes and four types of Z scores from the normalized cohort's gene expression data. The learned patterns are then associated with the putative transcription factor targets to identify genes that consistently exhibit Up/Down gene regulation patterns within the cohort. We applied this method to 11 publicly available GEO data sets related to osteoclastgenesis. RESULT: Our experiment identified a small number of Up/Down IRF8 and NFATc1 target genes as relevant to osteoclast differentiation. The machine learning models using GP and GA produced NFATc1 and IRF8 target genes different than simply using a log2 fold change alone. Our literature survey revealed that all predicted target genes have known roles in bone remodeling, specifically related to the immune system and osteoclast formation and functions, suggesting confidence and validity in our method. CONCLUSION: cTAP was motivated by recognizing that biologists tend to use Z score values present in data sets for the analysis. However, using cTAP effectively presupposes assembling a sizable cohort of gene expression data sets within a comparable context. As public gene expression data repositories grow, the need to use cohort-based analysis method like cTAP will become increasingly important.

Single-cell transcriptomics defines Dot1L interacting partners and downstream target genes in the mouse molar dental pulp.

Although histone methyltransferases are implicated in many key developmental processes, the contribution of individual chromatin modifiers in dental tissues is not well understood. Using single-cell RNA sequencing, we examined the expression profiles of the disruptor of telomeric silencing 1-like (Dot1L) gene in the postnatal day 5 mouse molar dental pulp. Dot1L is the only known enzyme that methylates histone 3 on lysine 79, a modification associated with gene expression. Our research revealed 15 distinct clusters representing different populations of mesenchymal stromal cells (MSCs), immune cells, pericytes, ameloblasts and endothelial cells. We documented heterogeneity in gene expression across different subpopulations of MSCs, a good indicator that these stromal progenitors undergo different phases of osteogenic differentiation. Interestingly, although Dot1L was broadly expressed across all cell clusters within the molar dental pulp, our analyses indicated specific enrichment of Dot1L within two clusters of MSCs, as well as cell clusters characterized as ameloblasts and endothelial cells. Moreover, we detected Dot1L co-expression with protein interactors involved in epigenetic activation such as Setd2, Sirt1, Brd4, Isw1, Bptf and Suv39h1. In addition, Dot1L was co-expressed with Eed2, Cbx3 and Dnmt1, which encode epigenetic factors associated with gene silencing and heterochromatin formation. Dot1l was co-expressed with downstream targets of the insulin growth factor and WNT signaling pathways, as well as genes involved in cell cycle progression. Collectively, our results suggest that Dot1L may play key roles in orchestrating lineage-specific gene expression during MSC differentiation.

Genome-wide distribution of 5hmC in the dental pulp of mouse molars and incisors.

The dental pulp is critical for the production of odontoblasts to create reparative dentin. In recent years, dental pulp has become a promising source of mesenchymal stem cells that are capable of differentiating into multiple cell types. To elucidate the transcriptional control mechanisms specifying the early phases of odontoblast differentiation, we analysed the DNA demethylation pattern associated with 5-hydroxymethylcytosine (5hmC) in the primary murine dental pulp. 5hmC plays an important role in chromatin accessibility and transcriptional control by modelling a dynamic equilibrium between DNA methylation and demethylation. Our research revealed 5hmC enrichment along genes and non-coding regulatory regions associated with specific developmental pathways in the genome of mouse incisor and molar dental pulp. Although the overall distribution of 5hmC is similar, the intensity and location of the 5hmC peaks significantly differs between the incisor and molar pulp genome, indicating cell type-specific epigenetic variations. Our study suggests that the differential DNA demethylation pattern could account for the distinct regulatory mechanisms underlying the tooth-specific ontogenetic programs.

rPAC: Route based pathway analysis for cohorts of gene expression data sets.

Pathway analysis is a popular method aiming to derive biological interpretation from high-throughput gene expression studies. However, existing methods focus mostly on identifying which pathway or pathways could have been perturbed, given differential gene expression patterns. In this paper, we present a novel pathway analysis framework, namely rPAC, which decomposes each signaling pathway route into two parts, the upstream portion of a transcription factor (TF) block and the downstream portion from the TF block and generates a pathway route perturbation analysis scheme examining disturbance scores assigned to both parts together. This rPAC scoring is further applied to a cohort of gene expression data sets which produces two summary metrics, "Proportion of Significance" (PS) and "Average Route Score" (ARS), as quantitative measures discerning perturbed pathway routes within and/or between cohorts. To demonstrate rPAC's scoring competency, we first used a large amount of simulated data and compared the method's performance against those by conventional methods in terms of power curve. Next, we performed a case study involving three epithelial cancer data sets from The Cancer Genome Atlas (TCGA). The rPAC method revealed specific pathway routes as potential cancer type signatures. A deeper pathway analysis of sub-groups (i.e., age groups in COAD or cancer sub-types in BRCA) resulted in pathway routes that are known to be associated with the sub-groups. In addition, multiple previously uncharacterized pathways routes were identified, potentially suggesting that rPAC is better in deciphering etiology of a disease than conventional methods particularly in isolating routes and sections of perturbed pathways in a finer granularity.

Excitotoxic stimulation activates distinct pathogenic and protective expression signatures in the hippocampus.

Excitotoxic events underlying ischaemic and traumatic brain injuries activate degenerative and protective pathways, particularly in the hippocampus. To understand opposing pathways that determine the brain's response to excitotoxicity, we used hippocampal explants, thereby eliminating systemic variables during a precise protocol of excitatory stimulation. N-methyl-d-aspartate (NMDA) was applied for 20 min and total RNA isolated one and 24 h later for neurobiology-specific microarrays. Distinct groups of genes exhibited early vs. delayed induction, with 63 genes exclusively reduced 24-h post-insult. Egr-1 and NOR-1 displayed biphasic transcriptional modulation: early induction followed by delayed suppression. Opposing events of NMDA-induced genes linked to pathogenesis and cell survival constituted the early expression signature. Delayed degenerative indicators (up-regulated pathogenic genes, down-regulated pro-survival genes) and opposing compensatory responses (down-regulated pathogenic genes, up-regulated pro-survival genes) generated networks with temporal gene profiles mirroring coexpression network clustering. We then used the expression profiles to test whether NF-κB, a potent transcription factor implicated in both degenerative and protective pathways, is involved in the opposing responses. The NF-κB inhibitor MG-132 indeed altered NMDA-mediated transcriptional changes, revealing components of opposing expression signatures that converge on the single response element. Overall, this study identified counteracting avenues among the distinct responses to excitotoxicity, thereby suggesting multi-target treatment strategies and implications for predictive medicine.

Deep Pathway Analysis V2.0: A Pathway Analysis Framework Incorporating Multi-Dimensional Omics Data.

Pathway analysis is essential in cancer research particularly when scientists attempt to derive interpretation from genome-wide high-throughput experimental data. If pathway information is organized into a network topology, its use in interpreting omics data can become very powerful. In this paper, we propose a topology-based pathway analysis method, called DPA V2.0, which can combine multiple heterogeneous omics data types in its analysis. In this method, each pathway route is encoded as a Bayesian network which is initialized with a sequence of conditional probabilities specifically designed to encode directionality of regulatory relationships defined in the pathway. Unlike other topology-based pathway tools, DPA is capable of identifying pathway routes as representatives of perturbed regulatory signals. We demonstrate the effectiveness of our model by applying it to two well-established TCGA data sets, namely, breast cancer study (BRCA) and ovarian cancer study (OV). The analysis combines mRNA-seq, mutation, copy number variation, and phosphorylation data publicly available for both TCGA data sets. We performed survival analysis and patient subtype analysis and the analysis outcomes revealed the anticipated strengths of our model. We hope that the availability of our model encourages wet lab scientists to generate extra data sets to reap the benefits of using multiple data types in pathway analysis. The majority of pathways distinguished can be confirmed by biological literature. Moreover, the proportion of correctly indentified pathways is 10 percent higher than previous work where only mRNA-seq and mutation data is incorporated for breast cancer patients. Consequently, such an in-depth pathway analysis incorporating more diverse data can give rise to the accuracy of perturbed pathway detection.

Skeletal screening IMPC/KOMP using µCT and computer automated cryohistology: Application to the Efna4 KO mouse line.

The IMPC/KOMP program provides the opportunity to screen mice harboring well defined gene-inactivation mutations in a uniform genetic background. The program performs a global tissue phenotyping survey that includes skeletal x-rays and bone density measurements. Because of the relative insensitivity of the two screening tests for detecting variance in bone architecture, we initiated a secondary screen based on µCT and a cryohistolomorphological workflow that was performed on the femur and vertebral compartments on 220 randomly selected knockouts (KOs) and 36 control bone samples over a 2 1/2 year collection period provided by one of the production/phenotyping centers. The performance of the screening protocol was designed to balance throughput and cost versus sensitivity and informativeness such that the output would be of value to the skeletal biology community. Here we report the reliability of this screening protocol to establish criteria for control skeletal variance at the architectural, dynamic and cellular histomorphometric level. Unexpected properties of the control population include unusually high variance in BV/TV in male femurs and greater bone formation and bone turnover rates in the female femur and vertebral trabeculae bone compartments. However, the manner for maintaining bone formation differed between these two bone sites. The vertebral compartment relies on maintaining a greater number of bone forming surfaces while the femoral compartment utilized more matrix production per cell. The comparison of the architectural properties obtained by µCT and histomorphology revealed significant differences in values for BV/TV, Tb.Th and Tb.N which is attributable to sampling density of the two methods. However, as a screening tool, expressing the ratio of KO to the control line as obtained by either method was remarkably similar. It identified KOs with significant variance which led to a more detailed histological analysis. Our findings are exemplified by the Efna4 KO, in which a high BV/TV was identified by µCT and confirmed by histomorphometry in the femur but not in the vertebral compartment. Dynamic labeling showed a marked increase in BFR which was attributable to increased labeling surfaces. Cellular analysis confirmed partitioning of osteoblast to labeling surfaces and a marked decrease in osteoclastic activity on both labeling and quiescent surfaces. This pattern of increased bone modeling would not be expected based on prior studies of the Ephrin-Ephrin receptor signaling pathways between osteoblasts and osteoclasts. Overall, our findings underscore why unbiased screening is needed because it can reveal unknown or unanticipated genes that impact skeletal variation.

A framework using topological pathways for deeper analysis of transcriptome data.

BACKGROUND: Pathway analysis is one of the later stage data analysis steps essential in interpreting high-throughput gene expression data. We propose a set of algorithms which given gene expression data can recognize which portion of sub-pathways are actively utilized in the biological system being studied. The degree of activation is measured by conditional probability of the input expression data based on the Bayesian Network model constructed from the topological pathway. RESULTS: We demonstrate the effectiveness of our pathway analysis method by conducting two case studies. The first one applies our method to a well-studied temporal microarray data set for the cell cycle using the KEGG Cell Cycle pathway. Our method closely reproduces the biological claims associated with the data sets, but unlike the original work ours can produce how pathway routes interact with each other above and beyond merely identifying which pathway routes are involved in the process. The second study applies the method to the p53 mutation microarray data to perform a comparative study. CONCLUSIONS: We show that our method achieves comparable performance against all other pathway analysis systems included in this study in identifying p53 altered pathways. Our method could pave a new way of carrying out next generation pathway analysis.

Astaxanthin attenuates the increase in mitochondrial respiration during the activation of hepatic stellate cells.

Upon liver injury, quiescent hepatic stellate cells (qHSCs) transdifferentiate to myofibroblast-like activated HSCs (aHSCs), which are primarily responsible for the accumulation of extracellular matrix proteins during the development of liver fibrosis. Therefore, aHSCs may exhibit different energy metabolism from that of qHSCs to meet their high energy demand. We previously demonstrated that astaxanthin (ASTX), a xanthophyll carotenoid, prevents the activation of HSCs. The objective of this study was to determine if ASTX can exert its antifibrogenic effect by attenuating any changes in energy metabolism during HSC activation. To characterize the energy metabolism of qHSCs and aHSCs, mouse primary HSCs were cultured on uncoated plastic dishes for 7 days for spontaneous activation in the presence or absence of 25 µM ASTX. qHSCs (1 day after isolation) and aHSCs treated with or without ASTX for 7 days were used to determine parameters related to mitochondrial respiration using a Seahorse XFe24 Extracellular Flux analyzer. aHSCs had significantly higher basal respiration, maximal respiration, ATP production, spare respiratory capacity and proton leak than those of qHSCs. However, ASTX prevented most of the changes occurring during HSC activation and improved mitochondrial cristae structure with decreased cristae junction width, lumen width and the area in primary mouse aHSCs. Furthermore, qHSCs isolated from ASTX-fed mice had lower mitochondrial respiration and glycolysis than control qHSCs. Our findings suggest that ASTX may exert its antifibrogenic effect by attenuating the changes in energy metabolism during HSC activation.

BioTarget: A Computational Framework Identifying Cancer Type Specific Transcriptional Targets of Immune Response Pathways.

Transcriptome data can provide information on signaling pathways active in cancers, but new computational tools are needed to more accurately quantify pathway activity and identify tissue-specific pathway features. We developed a computational method called "BioTarget" that incorporates ChIP-seq data into cellular pathway analysis. This tool relates the expression of transcription factor TF target genes (based on ChIP-seq data) with the status of upstream signaling components for an accurate quantification of pathway activity. This analysis also reveals TF targets expressed in specific contexts/tissues. We applied BioTarget to assess the activity of TBX21 and GATA3 pathways in cancers. TBX21 and GATA3 are TF regulators that control the differentiation of T cells into Th1 and Th2 helper cells that mediate cell-based and humoral immune responses, respectively. Since tumor immune responses can impact cancer progression, the significance of our pathway scores should be revealed by effective patient stratification. We found that low Th1/Th2 activity ratios were associated with a significantly poorer survival of stomach and breast cancer patients, whereas an unbalanced Th1/Th2 response was correlated with poorer survival of colon cancer patients. Lung adenocarcinoma and lung squamous cell carcinoma patients had the lowest survival rates when both Th1 and Th2 responses were high. Our method also identified context-specific target genes for TBX21 and GATA3. Applying the BioTarget tool to BCL6, a TF associated with germinal center lymphocytes, we observed that patients with an active BCL6 pathway had significantly improved survival for breast, colon, and stomach cancer. Our findings support the effectiveness of the BioTarget tool for transcriptome analysis and point to interesting associations between some immune-response pathways and cancer progression.

Chemotherapy-induced oral mucositis is associated with detrimental bacterial dysbiosis.

BACKGROUND: Gastrointestinal mucosal injury (mucositis), commonly affecting the oral cavity, is a clinically significant yet incompletely understood complication of cancer chemotherapy. Although antineoplastic cytotoxicity constitutes the primary injury trigger, the interaction of oral microbial commensals with mucosal tissues could modify the response. It is not clear, however, whether chemotherapy and its associated treatments affect oral microbial communities disrupting the homeostatic balance between resident microorganisms and the adjacent mucosa and if such alterations are associated with mucositis. To gain knowledge on the pathophysiology of oral mucositis, 49 subjects receiving 5-fluorouracil (5-FU) or doxorubicin-based chemotherapy were evaluated longitudinally during one cycle, assessing clinical outcomes, bacterial and fungal oral microbiome changes, and epithelial transcriptome responses. As a control for microbiome stability, 30 non-cancer subjects were longitudinally assessed. Through complementary in vitro assays, we also evaluated the antibacterial potential of 5-FU on oral microorganisms and the interaction of commensals with oral epithelial tissues. RESULTS: Oral mucositis severity was associated with 5-FU, increased salivary flow, and higher oral granulocyte counts. The oral bacteriome was disrupted during chemotherapy and while antibiotic and acid inhibitor intake contributed to these changes, bacteriome disruptions were also correlated with antineoplastics and independently and strongly associated with oral mucositis severity. Mucositis-associated bacteriome shifts included depletion of common health-associated commensals from the genera Streptococcus, Actinomyces, Gemella, Granulicatella, and Veillonella and enrichment of Gram-negative bacteria such as Fusobacterium nucleatum and Prevotella oris. Shifts could not be explained by a direct antibacterial effect of 5-FU, but rather resembled the inflammation-associated dysbiotic shifts seen in other oral conditions. Epithelial transcriptional responses during chemotherapy included upregulation of genes involved in innate immunity and apoptosis. Using a multilayer epithelial construct, we show mucositis-associated dysbiotic shifts may contribute to aggravate mucosal damage since the mucositis-depleted Streptococcus salivarius was tolerated as a commensal, while the mucositis-enriched F. nucleatum displayed pro-inflammatory and pro-apoptotic capacity. CONCLUSIONS: Altogether, our work reveals that chemotherapy-induced oral mucositis is associated with bacterial dysbiosis and demonstrates the potential for dysbiotic shifts to aggravate antineoplastic-induced epithelial injury. These findings suggest that control of oral bacterial dysbiosis could represent a novel preventive approach to ameliorate oral mucositis.

Genome-Wide Analysis for Identifying FOXO Protein-Binding Sites.

Forkhead box O (FOXO) proteins comprise a superfamily of transcription factors that play important roles in controlling various biological processes. Transcriptional control constitutes a crucial component in regulating complex biological processes. The identification of cis-regulatory elements is essential to understand the regulatory mechanism of gene expression. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is widely used to identify the cis-regulatory elements of transcription factors and other DNA-binding proteins on a genome-wide level. It is a powerful tool to analyze the regulatory networks underlying the biological processes. Here, we describe a detailed protocol for preparing ChIP-seq samples that are used for sequencing and subsequent data analyses.

TriPOINT: a software tool to prioritize important genes in pathways and their non-coding regulators.

SUMMARY: Current approaches for pathway analyses focus on representing gene expression levels on graph representations of pathways and conducting pathway enrichment among differentially expressed genes. However, gene expression levels by themselves do not reflect the overall picture as non-coding factors play an important role to regulate gene expression. To incorporate these non-coding factors into pathway analyses and to systematically prioritize genes in a pathway we introduce a new software: Triangulation of Perturbation Origins and Identification of Non-Coding Targets. Triangulation of Perturbation Origins and Identification of Non-Coding Targets is a pathway analysis tool, implemented in Java that identifies the significance of a gene under a condition (e.g. a disease phenotype) by studying graph representations of pathways, analyzing upstream and downstream gene interactions and integrating non-coding regions that may be regulating gene expression levels. AVAILABILITY AND IMPLEMENTATION: The TriPOINT open source software is freely available at https://github.uconn.edu/ajt06004/TriPOINT under the GPL v3.0 license. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

FusorSV: an algorithm for optimally combining data from multiple structural variation detection methods.

Comprehensive and accurate identification of structural variations (SVs) from next generation sequencing data remains a major challenge. We develop FusorSV, which uses a data mining approach to assess performance and merge callsets from an ensemble of SV-calling algorithms. It includes a fusion model built using analysis of 27 deep-coverage human genomes from the 1000 Genomes Project. We identify 843 novel SV calls that were not reported by the 1000 Genomes Project for these 27 samples. Experimental validation of a subset of these calls yields a validation rate of 86.7%. FusorSV is available at https://github.com/TheJacksonLaboratory/SVE .