Comparison of Normalization Methods for Analysis of TempO-Seq Targeted RNA Sequencing Data

Developing a Screening Strategy to Identify Hepatotoxicity and Drug Interaction Potential of Botanicals

Botanical supplements, herbal remedies, and plant-derived products are used globally. However, botanical dietary supplements are rarely subjected to robust safety testing unless there are adverse reports in post-market surveillance. Botanicals are complex and difficult to assess using current frameworks designed for single constituent substances (e.g. small molecules or discrete chemicals), making safety assessments costly and time-consuming. The liver is a primary organ of concern for potential botanical-induced hepatotoxicity and botanical-drug interactions as it plays a crucial role in xenobiotic metabolism. The NIH-funded Drug Induced Liver Injury Network noted that the number of botanical-induced liver injuries in 2017 nearly tripled from those observed in 2004-2005. New approach methodologies (NAMs) can aid in the rapid and cost-effective assessment of botanical supplements for potential hepatotoxicity. The Hepatotoxicity Working Group within the Botanical Safety Consortium is working to develop a screening strategy that can help reliably identify potential hepatotoxic botanicals and inform mechanisms of toxicity. This manuscript outlines the Hepatotoxicity Working Group's strategy and describes the assays selected and the rationale for the selection of botanicals used in case studies. The selected NAMs evaluated as a part of this effort are intended to be incorporated into a larger battery of assays to evaluate multiple endpoints related to botanical safety. This work will contribute to a botanical safety toolkit, providing researchers with tools to better understand hepatotoxicity associated with botanicals, prioritize and plan future testing as needed, and gain a deeper insight into the botanicals being tested.

ToxDAR: A Workflow Software for Analyzing Toxicologically Relevant Proteomic and Transcriptomic Data, from Data Preparation to Toxicological Mechanism Elucidation

Exploration of toxicological mechanisms is imperative for the assessment of potential adverse reactions to chemicals and pharmaceutical agents, the engineering of safer compounds, and the preservation of public health. It forms the foundation of drug development and disease treatment. High-throughput proteomics and transcriptomics can accurately capture the body's response to toxins and have become key tools for revealing complex toxicological mechanisms. Recently, a vast amount of omics data related to toxicological mechanisms have been accumulated. However, analyzing and utilizing these data remains a major challenge for researchers, especially as there is a lack of a knowledge-based analysis system to identify relevant biological pathways associated with toxicity from the data and to establish connections between omics data and existing toxicological knowledge. To address this, we have developed ToxDAR, a workflow-oriented R package for preprocessing and analyzing toxicological multi-omics data. ToxDAR integrates packages like NormExpression, DESeq2, and igraph, and utilizes R functions such as prcomp and phyper. It supports data preparation, quality control, differential expression analysis, functional analysis, and network analysis. ToxDAR's architecture also includes a knowledge graph with five major categories of mechanism-related biological entities and details fifteen types of interactions among them, providing comprehensive knowledge annotation for omics data analysis results. As a case study, we used ToxDAR to analyze a transcriptomic dataset on the toxicology of triphenyl phosphate (TPP). The results indicate that TPP may impair thyroid function by activating thyroid hormone receptor β (THRB), impacting pathways related to programmed cell death and inflammation. As a workflow-oriented data analysis tool, ToxDAR is expected to be crucial for understanding toxic mechanisms from omics data, discovering new therapeutic targets, and evaluating chemical safety.

Normalization of gene counts affects principal components-based exploratory analysis of RNA-sequencing data

Normalization of gene expression count data is an essential step of in the analysis of RNA-sequencing data. Its statistical analysis has been mostly addressed in the context of differential expression analysis, that is in the univariate setting. However, relationships among genes and samples are better explored and quantified using multivariate exploratory data analysis tools like Principal Component Analysis (PCA). In this study we investigate how normalization impacts the PCA model and its interpretation, considering twelve different widely used normalization methods that were applied on simulated and experimental data. Correlation patterns in the normalized data were explored using both summary statistics and Covariance Simultaneous Component Analysis. The impact of normalization on the PCA solution was assessed by exploring the model complexity, the quality of sample clustering in the low-dimensional PCA space and gene ranking in the model fit to normalized data. PCA models upon normalization were interpreted in the context gene enrichment pathway analysis. We found that although PCA score plots are often similar independently form the normalization used, biological interpretation of the models can depend heavily on the normalization method applied.

Identification of Tumor Budding-Associated Genes in Breast Cancer through Transcriptomic Profiling and Network Diffusion Analysis

Breast cancer has the highest diagnosis rate among all cancers. Tumor budding (TB) is recognized as a recent prognostic marker. Identifying genes specific to high-TB samples is crucial for hindering tumor progression and metastasis. In this study, we utilized an RNA sequencing technique, called TempO-Seq, to profile transcriptomic data from breast cancer samples, aiming to identify biomarkers for high-TB cases. Through differential expression analysis and mutual information, we identified seven genes (NOL4, STAR, C8G, NEIL1, SLC46A3, FRMD6, and SCARF2) that are potential biomarkers in breast cancer. To gain more relevant proteins, further investigation based on a protein-protein interaction network and the network diffusion technique revealed enrichment in the Hippo signaling and Wnt signaling pathways, promoting tumor initiation, invasion, and metastasis in several cancer types. In conclusion, these novel genes, recognized as overexpressed in high-TB samples, along with their associated pathways, offer promising therapeutic targets, thus advancing treatment and diagnosis for breast cancer.

Specific gut microbiome signature predicts hepatitis B virus-related hepatocellular carcinoma patients with microvascular invasion

BACKGROUND

We aimed to assess differences in intestinal microflora between patients with operable hepatitis B virus-related hepatocellular carcinoma (HBV-HCC) with microvascular invasion (MVI) and those without MVI. Additionally, we investigated the potential of the microbiome as a non-invasive biomarker for patients with MVI.

METHODS

We analyzed the preoperative gut microbiomes (GMs) of two groups, the MVI (n = 46) and non-MVI (n = 56) groups, using 16S ribosomal RNA gene sequencing data. At the operational taxonomic unit level, we employed random forest models to predict MVI risk and validated the results in independent validation cohorts [MVI group (n = 17) and non-MVI group (n = 15)].

RESULTS

β diversity analysis, utilizing weighted UniFrac distances, revealed a significant difference between the MVI and non-MVI groups, as indicated by non-metric multidimensional scaling and principal coordinate analysis. We also observed a significant correlation between the characteristic intestinal microbial communities at the genus level and their main functions. Nine optimal microbial markers were identified, with an area under the curve of 79.76% between 46 MVI and 56 non-MVI samples and 79.80% in the independent verification group.

CONCLUSION

This pioneering analysis of the GM in patients with operable HBV-HCC with and without MVI opens new avenues for treating HBV-HCC with MVI. We successfully established a diagnostic model and independently verified microbial markers for patients with MVI. As preoperative targeted biomarkers, GM holds potential as a non-invasive tool for patients with HBV-HCC with MVI.

Modelling the effectiveness of surveillance based on metagenomics in detecting, monitoring, and forecasting antimicrobial resistance in livestock production under economic constraints

Current surveillance of antimicrobial resistance (AMR) is mostly based on testing indicator bacteria using minimum inhibitory concentration (MIC) panels. Metagenomics has the potential to identify all known antimicrobial resistant genes (ARGs) in complex samples and thereby detect changes in the occurrence earlier. Here, we simulate the results of an AMR surveillance program based on metagenomics in the Danish pig population. We modelled both an increase in the occurrence of ARGs and an introduction of a new ARG in a few farms and the subsequent spread to the entire population. To make the simulation realistic, the total cost of the surveillance was constrained, and the sampling schedule was set at one pool per month with 5, 20, 50, or 100 samples. Our simulations demonstrate that a pool of 20-50 samples and a sequencing depth of 250 million fragments resulted in the shortest time to detection in both scenarios, with a time delay to detection of change of [Formula: see text]15 months in all scenarios. Compared with culture-based surveillance, our simulation indicates that there are neither significant reductions nor increases in time to detect a change using metagenomics. The benefit of metagenomics is that it is possible to monitor all known resistance in one sampling and laboratory procedure in contrast to the current monitoring that is based on the phenotypic characterisation of selected indicator bacterial species. Therefore, overall changes in AMR in a population will be detected earlier using metagenomics due to the fact that the resistance gene does not have to be transferred to and expressed by an indicator bacteria before it is possible to detect.

Radiogenomics Reveals Correlation between Quantitative Texture Radiomic Features of Biparametric MRI and Hypoxia-Related Gene Expression in Men with Localised Prostate Cancer

OBJECTIVES

To perform multiscale correlation analysis between quantitative texture feature phenotypes of pre-biopsy biparametric MRI (bpMRI) and targeted sequence-based RNA expression for hypoxia-related genes.

MATERIALS AND METHODS

Images from pre-biopsy 3T bpMRI scans in clinically localised PCa patients of various risk categories (n = 15) were used to extract textural features. The genomic landscape of hypoxia-related gene expression was obtained using post-radical prostatectomy tissue for targeted RNA expression profiling using the TempO-sequence method. The nonparametric Games Howell test was used to correlate the differential expression of the important hypoxia-related genes with 28 radiomic texture features. Then, cBioportal was accessed, and a gene-specific query was executed to extract the Oncoprint genomic output graph of the selected hypoxia-related genes from The Cancer Genome Atlas (TCGA). Based on each selected gene profile, correlation analysis using Pearson's coefficients and survival analysis using Kaplan-Meier estimators were performed.

RESULTS

The quantitative bpMR imaging textural features, including the histogram and grey level co-occurrence matrix (GLCM), correlated with three hypoxia-related genes (ANGPTL4, VEGFA, and P4HA1) based on RNA sequencing using the TempO-Seq method. Further radiogenomic analysis, including data accessed from the cBioportal genomic database, confirmed that overexpressed hypoxia-related genes significantly correlated with a poor survival outcomes, with a median survival ratio of 81.11:133.00 months in those with and without alterations in genes, respectively.

CONCLUSION

This study found that there is a correlation between the radiomic texture features extracted from bpMRI in localised prostate cancer and the hypoxia-related genes that are differentially expressed. The analysis of expression data based on cBioportal revealed that these hypoxia-related genes, which were the focus of the study, are linked to an unfavourable survival outcomes in prostate cancer patients.

Immune gene expression in the mosquito vector Culex quinquefasciatus during an avian malaria infection

Plasmodium relictum is the most widespread avian malaria parasite in the world. It is listed as one of the 100 most dangerous invasive species, having been responsible for the extinction of several endemic bird species, and the near-demise of several others. Here we present the first transcriptomic study focused on the effect of P. relictum on the immune system of its vector (the mosquito Culex quinquefasciatus) at different times post-infection. We show that over 50% of immune genes identified as being part of the Toll pathway and 30%-40% of the immune genes identified within the Imd pathway are overexpressed during the critical period spanning the parasite's oocyst and sporozoite formation (8-12 days), revealing the crucial role played by both these pathways in this natural mosquito-Plasmodium combination. Comparison of infected mosquitoes with their uninfected counterparts also revealed some unexpected immune RNA expression patterns earlier and later in the infection: significant differences in expression of several immune effectors were observed as early as 30 min after ingestion of the infected blood meal. In addition, in the later stages of the infection (towards the end of the mosquito lifespan), we observed an unexpected increase in immune investment in uninfected, but not in infected, mosquitoes. In conclusion, our work extends the comparative transcriptomic analyses of malaria-infected mosquitoes beyond human and rodent parasites and provides insights into the degree of conservation of immune pathways and into the selective pressures exerted by Plasmodium parasites on their vectors.

Potential lineage transmission within the active microbiota of the eggs and the nauplii of the shrimp Litopenaeus stylirostris: possible influence of the rearing water and more

BACKGROUND

Microbial communities associated with animals are known to be key elements in the development of their hosts. In marine environments, these communities are largely under the influence of the surrounding water. In aquaculture, understanding the interactions existing between the microbiotas of farmed species and their rearing environment could help establish precise bacterial management.

METHOD

In light of these facts, we studied the active microbial communities associated with the eggs and the nauplii of the Pacific blue shrimp (Litopenaeus stylirostris) and their rearing water. All samples were collected in September 2018, November 2018 and February 2019. After RNA extractions, two distinct Illumina HiSeq sequencings were performed. Due to different sequencing depths and in order to compare samples, data were normalized using the Count Per Million method.

RESULTS

We found a core microbiota made of taxa related to Aestuariibacter, Alteromonas, Vibrio, SAR11, HIMB11, AEGEAN 169 marine group and Candidatus Endobugula associated with all the samples indicating that these bacterial communities could be transferred from the water to the animals. We also highlighted specific bacterial taxa in the eggs and the nauplii affiliated to Pseudomonas, Corynebacterium, Acinetobacter, Labrenzia, Rothia, Thalassolituus, Marinobacter, Aureispira, Oleiphilus, Profundimonas and Marinobacterium genera suggesting a possible prokaryotic vertical transmission from the breeders to their offspring. This study is the first to focus on the active microbiota associated with early developmental stages of a farmed shrimp species and could serve as a basis to comprehend the microbial interactions involved throughout the whole rearing process.