Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk. Leveraging genome and plasma proteome data from the INTERVAL study, we established and validated models to predict protein levels using genetic variants. By examining 8,275 PDAC cases and 6,723 controls, we identified 40 associated proteins, of which 16 are novel. Functionally validating these candidates by focusing on 2 selected novel protein-encoding genes, GOLM1 and B4GALT1, we demonstrated their pivotal roles in driving PDAC cell proliferation, migration, and invasion. Furthermore, we also identified potential drug repurposing opportunities for treating PDAC. SIGNIFICANCE: PDAC is a notoriously difficult-to-treat malignancy, and our limited understanding of causal protein markers hampers progress in developing effective early detection strategies and treatments. Our study identifies novel causal proteins using genetic instruments and subsequently functionally validates selected novel proteins. This dual approach enhances our understanding of PDAC etiology and potentially opens new avenues for therapeutic interventions.

Subverting the Canon: Novel Cancer-Promoting Functions and Mechanisms for snoRNAs.

Small nucleolar RNAs (snoRNAs) constitute a class of intron-derived non-coding RNAs ranging from 60 to 300 nucleotides. Canonically localized in the nucleolus, snoRNAs play a pivotal role in RNA modifications and pre-ribosomal RNA processing. Based on the types of modifications they involve, such as methylation and pseudouridylation, they are classified into two main families-box C/D and H/ACA snoRNAs. Recent investigations have revealed the unconventional synthesis and biogenesis strategies of snoRNAs, indicating their more profound roles in pathogenesis than previously envisioned. This review consolidates recent discoveries surrounding snoRNAs and provides insights into their mechanistic roles in cancer. It explores the intricate interactions of snoRNAs within signaling pathways and speculates on potential therapeutic solutions emerging from snoRNA research. In addition, it presents recent findings on the long non-coding small nucleolar RNA host gene (lncSNHG), a subset of long non-coding RNAs (lncRNAs), which are the transcripts of parental SNHGs that generate snoRNA. The nucleolus, the functional epicenter of snoRNAs, is also discussed. Through a deconstruction of the pathways driving snoRNA-induced oncogenesis, this review aims to serve as a roadmap to guide future research in the nuanced field of snoRNA-cancer interactions and inspire potential snoRNA-related cancer therapies.

Pan-cancer analyses of bromodomain containing 9 as a novel therapeutic target reveals its diagnostic, prognostic potential and biological mechanism in human tumours.

BACKGROUND: Mutations in one or more genes responsible for encoding subunits within the SWItch/Sucrose Non-Fermentable (SWI/SNF) chromatin-remodelling complexes are found in approximately 25% of cancer patients. Bromodomain containing 9 (BRD9) is a more recently identified protein coding gene, which can encode SWI/SNF chromatin-remodelling complexes subunits. Although initial evaluations of the potential of BRD9-based targeted therapy have been explored in the clinical application of a small number of cancer types, more detailed study of the diagnostic and prognostic potential, as well as the detailed biological mechanism of BRD9 remains unreported. METHODS: We used various bioinformatics tools to generate a comprehensive, pan-cancer analyses of BRD9 expression in multiple disease types described in The Cancer Genome Atlas (TCGA). Experimental validation was conducted in tissue microarrays and cell lines derived from lung and colon cancers. RESULTS: Our study revealed that BRD9 exhibited elevated expression in a wide range of tumours. Analysis of survival data and DNA methylation for BRD9 indicated distinct conclusions for multiple tumours. mRNA splicing and molecular binding were involved in the functional mechanism of BRD9. BRD9 may affect cancer progression through different phosphorylation sites or N6 -methyladenosine site modifications. BRD9 could potentially serve as a novel biomarker for diagnosing different cancer types, especially could accurately forecast the prognosis of melanoma patients receiving anti-programmed cell death 1 immunotherapy. BRD9 has the potential to serve as a therapeutic target, when pairing with etoposide in patients with melanoma. The BRD9/SMARCD1 axis exhibited promising discriminative performance in forecasting the prognosis of patients afflicted with liver hepatocellular carcinoma (LIHC) and mesothelioma. Additionally, this axis appears to potentially influence the immune response in LIHC by regulating the programmed death-ligand 1 immune checkpoint. For experimental validation, high expression levels of BRD9 were observed in tumour tissue samples from both lung and colon cancer patients. Knocking down BRD9 led to the inhibition of lung and colon cancer development, likely via the Wnt/ß-catenin signalling pathway. CONCLUSIONS: These pan-cancer study revealed the diagnostic and prognostic potential, along with the biological mechanism of BRD9 as a novel therapeutic target in human tumours.

Regulome-wide association study identifies genetically driven accessible regions associated with pancreatic cancer risk.

Genome-wide association studies (GWAS) have identified two dozen genetic variants that are associated with the risk of pancreatic ductal adenocarcinoma (PDAC), a deadly malignancy. However, a majority of these variants are located in noncoding regions of the genome, which limits the translation of GWAS findings into clinical applications. The regulome-wide association study (RWAS) is a recently developed method for identifying TF binding-induced accessibility regions for diseases. However, their potential connection to PDAC has yet to be fully explored. We evaluated the associations between genetically predicted levels of chromatin accessibility and risk of PDAC by using pan-cancer chromatin accessibility genetic prediction models. Our analysis included 8275 cases and 6723 controls from the PanScan (I, II, and III) and PanC4 consortia. To further refine our results, we also integrated genes associated to allele-specific accessibility quantitative trait loci (as-aQTL) and TF motifs located in the as-aQTL. We found that 50 chromatin accessibility features were associated with PDAC risk at a false discovery rate (FDR) of less than 0.05. A total of 28 RWAS peaks were identified as conditionally significant. By integrating the results from as-aQTL, motif analysis, and RWAS, we identified candidate causal regulatory elements for two potential chromatin accessibility regions (THCA_89956 and ESCA_89167) that are associated with PDAC risk. Our study identified chromatin accessibility features in noncoding genomic regions that are associated with PDAC risk. We also predicted the associated genes and disrupt motifs. Our findings provide new insights into the regulatory mechanisms of noncoding regions for pancreatic tumorigenesis.

Splicing transcriptome-wide association study to identify splicing events for pancreatic cancer risk.

A large proportion of the heritability of pancreatic cancer risk remains elusive, and the contribution of specific mRNA splicing events to pancreatic cancer susceptibility has not been systematically evaluated. In this study, we performed a large splicing transcriptome-wide association study (spTWAS) using three modeling strategies (Enet, LASSO and MCP) to develop alternative splicing genetic prediction models for identifying novel susceptibility loci and splicing introns for pancreatic cancer risk by assessing 8275 pancreatic cancer cases and 6723 controls of European ancestry. Data from 305 subjects of whom the majority are of European descent in the Genotype-Tissue Expression Project (GTEx) were used and both cis-acting and promoter-enhancer interaction regions were considered to build these models. We identified nine splicing events of seven genes (ABO, UQCRC1, STARD3, ETAA1, CELA3B, LGR4 and SFT2D1) that showed an association of genetically predicted expression with pancreatic cancer risk at a false discovery rate ≤0.05. Of these genes, UQCRC1 and LGR4 have not yet been reported to be associated with pancreatic cancer risk. Fine-mapping analyses supported likely causal associations corresponding to six splicing events of three genes (P4HTM, ABO and PGAP3). Our study identified novel genes and splicing events associated with pancreatic cancer risk, which can improve our understanding of the etiology of this deadly malignancy.

Mitochondria-derived small RNAs as diagnostic biomarkers in lung cancer patients through a novel ratio-based expression analysis methodology.

Small non-coding RNAs are potential diagnostic biomarkers for lung cancer. Mitochondria-derived small RNA (mtRNA) is a novel regulatory small non-coding RNA that only recently has been identified and cataloged. Currently, there are no reports of studies of mtRNA in human lung cancer. Currently, normalization methods are unstable, and they often fail to identify differentially expressed small non-coding RNAs (sncRNAs). In order to identify reliable biomarkers for lung cancer screening, we used a ratio-based method using mtRNAs newly discovered in human peripheral blood mononuclear cells. In the discovery cohort (AUC = 0.981) and independent validation cohort (AUC = 0.916) the prediction model of eight mtRNA ratios distinguished lung cancer patients from controls. The prediction model will provide reliable biomarkers that will allow blood-based screening to become more feasible and will help make lung cancer diagnosis more accurate in clinical practice.

A Splicing Transcriptome-Wide Association Study Identifies Candidate Altered Splicing for Prostate Cancer Risk.

Prostate cancer (PCa) represents a huge public health burden among men. Many susceptibility genetic factors for PCa still remain unknown. In this study, we performed a large splicing transcriptome-wide association study (spTWAS) using three modeling strategies to develop alternative splicing genetic prediction models for identifying novel susceptibility loci and splicing introns for PCa risk by assessing 79,194 cases and 61,112 controls of European ancestry in the PRACTICAL, CRUK, CAPS, BPC3, and PEGASUS consortia. We identified 120 splicing introns of 97 genes showing an association with PCa risk at false discovery rate (FDR)-corrected threshold (FDR <0.05). Of them, 33 genes were enriched in PCa-related diseases and function categories. Fine-mapping analysis suggested that 21 splicing introns of 19 genes were likely causally associated with PCa risk. Thirty-five splicing introns of 34 novel genes were identified to be related to PCa susceptibility for the first time, and 11 of the genes were enriched in a cancer-related network. Our study identified novel loci and splicing introns associated with PCa risk, which can improve our understanding of the etiology of this common malignancy.

Biology-inspired graph neural network encodes reactome and reveals biochemical reactions of disease.

Functional heterogeneity of healthy human tissues complicates interpretation of molecular studies, impeding precision therapeutic target identification and treatment. Considering this, we generated a graph neural network with Reactome-based architecture and trained it using 9,115 samples from Genotype-Tissue Expression (GTEx). Our graph neural network (GNN) achieves adjusted Rand index (ARI) = 0.7909, while a Resnet18 control model achieves ARI = 0.7781, on 370 held-out healthy human tissue samples from The Cancer Genome Atlas (TCGA), despite the Resnet18 using over 600 times the parameters. Our GNN also succeeds in separating 83 healthy skin samples from 95 lesional psoriasis samples, revealing that upregulation of 26S- and NUB1-mediated degradation of NEDD8, UBD, and their conjugates is central to the largest perturbed reaction network component in psoriasis. We show that our results are not discoverable using traditional differential expression and hypergeometric pathway enrichment analyses yet are supported by separate human multi-omics and small-molecule mouse studies, suggesting future molecular disease studies may benefit from similar GNN analytical approaches.

Circulating microRNA Biomarker for Detecting Breast Cancer in High-Risk Benign Breast Tumors.

High-risk benign breast tumors are known to develop breast cancer at high rates. However, it is still controversial whether they should be removed during diagnosis or followed up until cancer development becomes evident. Therefore, this study sought to identify circulating microRNAs (miRNAs) that could serve as detection markers of cancers arising from high-risk benign tumors. Small RNA-seq was performed using plasma samples collected from patients with early-stage breast cancer (CA) and high-risk (HB), moderate-risk (MB), and no-risk (Be) benign breast tumors. Proteomic profiling of CA and HB plasma was performed to investigate the underlying functions of the identified miRNAs. Our findings revealed that four miRNAs, hsa-mir-128-3p, hsa-mir-421, hsa-mir-130b-5p, and hsa-mir-28-5p, were differentially expressed in CA vs. HB and had diagnostic power to discriminate CA from HB with AUC scores greater than 0.7. Enriched pathways based on the target genes of these miRNAs indicated their association with IGF-1. Furthermore, the Ingenuity Pathway Analysis performed on the proteomic data revealed that the IGF-1 signaling pathway was significantly enriched in CA vs. HB. In conclusion, these findings suggest that these miRNAs could potentially serve as biomarkers for detecting early-stage breast cancer from high-risk benign tumors by monitoring IGF signaling-induced malignant transformation.

sRNAfrag: a pipeline and suite of tools to analyze fragmentation in small RNA sequencing data.

Fragments derived from small RNAs such as small nucleolar RNAs are biologically relevant but remain poorly understood. To address this gap, we developed sRNAfrag, a modular and interoperable tool designed to standardize the quantification and analysis of small RNA fragmentation across various biotypes. The tool outputs a set of tables forming a relational database, allowing for an in-depth exploration of biologically complex events such as multi-mapping and RNA fragment stability across different cell types. In a benchmark test, sRNAfrag was able to identify established loci of mature microRNAs solely based on sequencing data. Furthermore, the 5' seed sequence could be rediscovered by utilizing a visualization approach primarily applied in multi-sequence-alignments. Utilizing the relational database outputs, we detected 1411 snoRNA fragment conservation events between two out of four eukaryotic species, providing an opportunity to explore motifs through evolutionary time and conserved fragmentation patterns. Additionally, the tool's interoperability with other bioinformatics tools like ViennaRNA amplifies its utility for customized analyses. We also introduce a novel loci-level variance-score which provides insights into the noise around peaks and demonstrates biological relevance by distinctly separating breast cancer and neuroblastoma cell lines after dimension reduction when applied to small nucleolar RNAs. Overall, sRNAfrag serves as a versatile foundation for advancing our understanding of small RNA fragments and offers a functional foundation to further small RNA research. Availability: https://github.com/kenminsoo/sRNAfrag.

Single-cell RNA sequencing reveals characteristics of myeloid cells in post-acute sequelae of SARS-CoV-2 patients with persistent respiratory symptoms.

Background: Although our understanding of the immunopathology and subsequent risk and severity of COVID-19 disease is evolving, a detailed account of immune responses that contribute to the long-term consequences of pulmonary complications in COVID-19 infection remains unclear. Few studies have detailed the immune and cytokine profiles associated with post-acute sequelae of SARS-CoV-2 infection (PASC) with persistent pulmonary symptoms. The dysregulation of the immune system that drives pulmonary sequelae in COVID-19 survivors and PASC sufferers remains largely unknown. Results: To characterize the immunological features of pulmonary PASC (PPASC), we performed droplet-based single-cell RNA sequencing (scRNA-seq) to study the transcriptomic profiles of peripheral blood mononuclear cells (PBMCs) from a participant naïve to SARS-CoV-2 (Control) (n=1) and infected with SARS-CoV-2 with chronic pulmonary symptoms (PPASC) (n=2). After integrating scRNA-seq data with a naïve participant from a published dataset, 11 distinct cell populations were identified based on the expression of canonical markers. The proportion of myeloid-lineage cells ([MLCs]; CD14+/CD16+monocytes, and dendritic cells) was increased in PPASC (n=2) compared to controls (n=2). MLCs from PPASC displayed up-regulation of genes associated with pulmonary symptoms/fibrosis, while glycolysis metabolism-related genes were downregulated. Similarly, pathway analysis showed that fibrosis-related (VEGF, WNT, and SMAD) and cell death pathways were up-regulated, but immune pathways were down-regulated in PPASC. Further comparison of PPASC with scRNA-seq data with Severe COVID-19 (n=4) data demonstrated enrichment of fibrotic transcriptional signatures. In PPASC, we observed interactive VEGF ligand-receptor pairs among MLCs, and network modules in CD14+ (cluster 4) and CD16+ (Cluster 5) monocytes displayed a significant enrichment for biological pathways linked to adverse COVID-19 outcomes, fibrosis, and angiogenesis. Further analysis revealed a distinct metabolic alteration in MLCs with a down-regulation of glycolysis/gluconeogenesis in PPASC compared to SARS-CoV-2 naïve samples. Conclusion: Analysis of a small scRNA-seq dataset demonstrated alterations in the immune response and cellular landscape in PPASC. The presence of elevated MLC levels and their corresponding gene signatures associated with fibrosis, immune response suppression, and altered metabolic states suggests a potential role in PPASC development.

Exploring Optimal Biomarker Sources: A Comparative Analysis of Exosomes and Whole Plasma in Fasting and Non-Fasting Conditions for Liquid Biopsy Applications.

The study of liquid biopsy with plasma samples is being conducted to identify biomarkers for clinical use. Exosomes, containing nucleic acids and metabolites, have emerged as possible sources for biomarkers. To evaluate the effectiveness of exosomes over plasma, we analyzed the small non-coding RNAs (sncRNAs) and metabolites extracted from exosomes in comparison to those directly extracted from whole plasma under both fasting and non-fasting conditions. We found that sncRNA profiles were not affected by fasting in either exosome or plasma samples. Our results showed that exosomal sncRNAs were found to have more consistent profiles. The plasma miRNA profiles contained high concentrations of cell-derived miRNAs that were likely due to hemolysis. We determined that certain metabolites in whole plasma exhibited noteworthy concentration shifts in relation to fasting status, while others did not. Here, we propose that (1) fasting is not required for a liquid biopsy study that involves both sncRNA and metabolomic profiling, as long as metabolites that are not influenced by fasting status are selected, and (2) the utilization of exosomal RNAs promotes robust and consistent findings in plasma samples, mitigating the impact of batch effects derived from hemolysis. These findings advance the optimization of liquid biopsy methodologies for clinical applications.

Integrated Analysis of Bulk RNA-Seq and Single-Cell RNA-Seq Unravels the Influences of SARS-CoV-2 Infections to Cancer Patients.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly contagious and pathogenic coronavirus that emerged in late 2019 and caused a pandemic of respiratory illness termed as coronavirus disease 2019 (COVID-19). Cancer patients are more susceptible to SARS-CoV-2 infection. The treatment of cancer patients infected with SARS-CoV-2 is more complicated, and the patients are at risk of poor prognosis compared to other populations. Patients infected with SARS-CoV-2 are prone to rapid development of acute respiratory distress syndrome (ARDS) of which pulmonary fibrosis (PF) is considered a sequelae. Both ARDS and PF are factors that contribute to poor prognosis in COVID-19 patients. However, the molecular mechanisms among COVID-19, ARDS and PF in COVID-19 patients with cancer are not well-understood. In this study, the common differentially expressed genes (DEGs) between COVID-19 patients with and without cancer were identified. Based on the common DEGs, a series of analyses were performed, including Gene Ontology (GO) and pathway analysis, protein-protein interaction (PPI) network construction and hub gene extraction, transcription factor (TF)-DEG regulatory network construction, TF-DEG-miRNA coregulatory network construction and drug molecule identification. The candidate drug molecules (e.g., Tamibarotene CTD 00002527) obtained by this study might be helpful for effective therapeutic targets in COVID-19 patients with cancer. In addition, the common DEGs among ARDS, PF and COVID-19 patients with and without cancer are TNFSF10 and IFITM2. These two genes may serve as potential therapeutic targets in the treatment of COVID-19 patients with cancer. Changes in the expression levels of TNFSF10 and IFITM2 in CD14+/CD16+ monocytes may affect the immune response of COVID-19 patients. Specifically, changes in the expression level of TNFSF10 in monocytes can be considered as an immune signature in COVID-19 patients with hematologic cancer. Targeting N6-methyladenosine (m6A) pathways (e.g., METTL3/SERPINA1 axis) to restrict SARS-CoV-2 reproduction has therapeutic potential for COVID-19 patients.

Comprehensive landscape of tRNA-derived fragments in lung cancer.

Transfer RNA (tRNA)-derived fragment (tRDF) is a novel small non-coding RNA that presents in different types of cancer. The comprehensive understanding of tRDFs in non-small cell lung cancer remains largely unknown. In this study, 1,550 patient samples of non-small cell lung cancer (NSCLC) were included, and 52 tRDFs with four subtypes were identified. Six tRDFs were picked as diagnostic signatures based on the tRDFs expression patterns, and area under the curve (AUC) in independent validations is up to 0.90. Two signatures were validated successfully in plasma samples, and six signatures confirmed the consistency of distinguished expression in NSCLC cell lines. Ten tRDFs along with independent risk scores can be used to predict survival outcomes by stages; 5a_tRF-Ile-AAT/GAT can be a prognosis biomarker for early stage. Association analysis of tRDFs-signatures-correlated mRNAs and microRNA (miRNA) were targeted to the cell cycle and oocyte meiosis signaling pathways. Five tRDFs were assessed to associate with PD-L1 immune checkpoint and correlated with the genes that target in PD-L1 checkpoint signaling pathway. Our study is the first to provide a comprehensive analysis of tRDFs in lung cancer, including four subtypes of tRDFs, investigating the diagnostic and prognostic values, and demonstrated their biological function and transcriptional role as well as potential immune therapeutic value.

RNA-Sequencing based analysis of bovine endometrium during the maternal recognition of pregnancy.

BACKGROUND: Maternal recognition is the crucial step for establishing pregnancy in cattle. This study aims to identify endometrial genes and biological pathways involved in the maternal recognition of pregnancy. Caruncular endometrial tissues were collected from Day 15-17 of gestation (pregnant), non-pregnant (absence of conceptus), and cyclic (non-bred) heifers. RESULTS: Total RNAs were isolated from the caruncular endometrial tissues of pregnant, non-pregnant, and cyclic heifers, and were subjected to high-throughput RNA-sequencing. The genes with at least two-fold change and Benjamini and Hochberg p-value ≤ 0.05 were considered differentially expressed genes and further confirmed with quantitative real-time PCR. A total of 107 genes (pregnant vs cyclic) and 98 genes (pregnant vs non-pregnant) were differentially expressed in the pregnant endometrium. The most highly up-regulated genes in the pregnant endometrium were MRS2, CST6, FOS, VLDLR, ISG15, IFI6, MX2, C15H11ORF34, EIF3M, PRSS22, MS4A8, and TINAGL1. Interferon signaling, immune response, nutrient transporter, synthesis, and secretion of proteins are crucial pathways during the maternal recognition of pregnancy. CONCLUSIONS: The study demonstrated that the presence of conceptus at Day 15-17 of gestation affects the endometrial gene expression related to endometrial remodeling, immune response, nutrients and ion transporters, and relevant signaling pathways in the caruncular region of bovine endometrium during the maternal recognition of pregnancy.

MRTF-A-mediated protection against amyloid-ß-induced neuronal injury correlates with restoring autophagy via miR-1273g-3p/mTOR axis in Alzheimer models.

Myocardia-Related Transcription Factors-A (MRTF-A), which is enriched in the hippocampus and cerebral cortex, has been shown to have a protective function against ischemia hypoxia-induced neuronal apoptosis. However, the function of MRTF-A on ß-amyloid peptide (Aß)-induced neurotoxicity and autophagy dysfunction in Alzheimer's disease is still unclear. This study shows that the expression of MRTF-A in the hippocampus of Tg2576 transgenic mice is reduced, and the overexpression of MRTF-A mediated by lentiviral vectors carrying MRTF-A significantly reduces the accumulation of hippocampal ß-amyloid peptide and reduces cognition defect. Overexpression of MRTF-A inhibits neuronal apoptosis, increases the protein levels of microtubule-associated protein 1 light chain 3-II (MAP1LC3/LC3-II) and Beclin1, reduces the accumulation of SQSTM1/p62 protein, and promotes autophagosomes-Lysosomal fusion in vivo and in vitro. Microarray analysis and bioinformatics analysis show that MRTF-A reverses Aß-induced autophagy impairment by up-regulating miR-1273g-3p level leading to negative regulation of the mammalian target of rapamycin (mTOR), which is confirmed in Aß1-42-treated SH-SY5Y cells. Further, overexpression of MRTF-A reduces Aß1-42-induced neuronal apoptosis. And the effect was abolished by miR-1273g-3p inhibitor or MHY1485 (mTOR agonist), indicating that the protection of MRTF-A on neuronal damage is through targeting miR-1273g-3p/mTOR axis. Targeting this signaling may be a promising approach to protect against Aß-induced neuronal injury.

Sulfite preservatives effects on the mouth microbiome: Changes in viability, diversity and composition of microbiota.

OVERVIEW: Processed foods make up about 70 percent of the North American diet. Sulfites and other food preservatives are added to these foods largely to limit bacterial contamination. The mouth microbiota and its associated enzymes are the first to encounter food and therefore likely to be the most affected. METHODS: Eight saliva samples from ten individuals were exposed to two sulfite preservatives, sodium sulfite and sodium bisulfite. One sample set was evaluated for bacteria composition utilizing 16s rRNA sequencing, and the number of viable cells in all sample sets was determined utilizing ATP assays at 10 and 40-minute exposure times. All untreated samples were analyzed for baseline lysozyme activity, and possible correlations between the number of viable cells and lysozyme activity. RESULTS: Sequencing indicated significant increases in alpha diversity with sodium bisulfite exposure and changes in relative abundance of 3 amplicon sequence variants (ASV). Sodium sulfite treated samples showed a significant decrease in the Firmicutes/Bacteroidetes ratio, a marginally significant change in alpha diversity, and a significant change in the relative abundance for Proteobacteria, Firmicutes, Bacteroidetes, and for 6 ASVs. Beta diversity didn't show separation between groups, however, all but one sample set was observed to be moving in the same direction under sodium sulfite treatment. ATP assays indicated a significant and consistent average decrease in activity ranging from 24-46% at both exposure times with both sulfites. Average initial rates of lysozyme activity between all individuals ranged from +/- 76% compared to individual variations of +/- 10-34%. No consistent, significant correlation was found between ATP and lysozyme activity in any sample sets. CONCLUSIONS: Sulfite preservatives, at concentrations regarded as safe by the FDA, alter the relative abundance and richness of the microbiota found in saliva, and decrease the number of viable cells, within 10 minutes of exposure.

Development of a tRNA-derived small RNA diagnostic and prognostic signature in liver cancer.

Liver cancer presents divergent clinical behaviors. There remain opportunities for molecular markers to improve liver cancer diagnosis and prognosis, especially since tRNA-derived small RNAs (tsRNA) have rarely been studied. In this study, a random forests (RF) diagnostic model was built based upon tsRNA profiling of paired tumor and adjacent normal samples and validated by independent validation (IV). A LASSO model was used to developed a seven-tsRNA-based risk score signature for liver cancer prognosis. Model performance was evaluated by a receiver operating characteristic curve (ROC curve) and Precision-Recall curve (PR curve). The five-tsRNA-based RF diagnosis model had area under the receiver operating characteristic curve (AUROC) 88% and area under the precision-recall curve (AUPR) 87% in the discovery cohort and 87% and 86% in IV-AUROC and IV-AUPR, respectively. The seven-tsRNA-based prognostic model predicts the overall survival of liver cancer patients (Hazard Ratio 2.02, 95% CI 1.36-3.00, P < 0.001), independent of standard clinicopathological prognostic factors. Moreover, the model successfully categorizes patients into high-low risk groups. Diagnostic and prognostic modeling can be reliably utilized in the diagnosis of liver cancer and high-low risk classification of patients based upon tsRNA characterization.

Analysis and Performance Assessment of the Whole Genome Bisulfite Sequencing Data Workflow: Currently Available Tools and a Practical Guide to Advance DNA Methylation Studies.

DNA methylation is associated with transcriptional repression, genomic imprinting, stem cell differentiation, embryonic development, and inflammation. Aberrant DNA methylation can indicate disease states, including cancer and neurological disorders. Therefore, the prevalence and location of 5-methylcytosine in the human genome is a topic of interest. Whole-genome bisulfite sequencing (WGBS) is a high-throughput method for analyzing DNA methylation. This technique involves library preparation, alignment, and quality control. Advancements in epigenetic technology have led to an increase in DNA methylation studies. This review compares the detailed experimental methodology of WGBS using accessible and up-to-date analysis tools. Practical codes for WGBS data processing are included as a general guide to assist progress in DNA methylation studies through a comprehensive case study.