Persistent gene expression and DNA methylation alterations linked to carcinogenic effects of dichloroacetic acid.

Background: Mechanistic understanding of transient exposures that lead to adverse health outcomes will enhance our ability to recognize biological signatures of disease. Here, we measured the transcriptomic and epigenomic alterations due to exposure to the metabolic reprogramming agent, dichloroacetic acid (DCA). Previously, we showed that exposure to DCA increased liver tumor incidence in B6C3F1 mice after continuous or early life exposures significantly over background level. Methods: Using archived formalin-fixed liver samples, we utilized modern methodologies to measure gene expression and DNA methylation levels to link to previously generated phenotypic measures. Gene expression was measured by targeted RNA sequencing (TempO-seq 1500+ toxicity panel: 2754 total genes) in liver samples collected from 10-, 32-, 57-, and 78-week old mice exposed to deionized water (controls), 3.5 g/L DCA continuously in drinking water ("Direct" group), or DCA for 10-, 32-, or 57-weeks followed by deionized water until sample collection ("Stop" groups). Genome-scaled alterations in DNA methylation were measured by Reduced Representation Bisulfite Sequencing (RRBS) in 78-week liver samples for control, Direct, 10-week Stop DCA exposed mice. Results: Transcriptomic changes were most robust with concurrent or adjacent timepoints after exposure was withdrawn. We observed a similar pattern with DNA methylation alterations where we noted attenuated differentially methylated regions (DMRs) in the 10-week Stop DCA exposure groups compared to the Direct group at 78-weeks. Gene pathway analysis indicated cellular effects linked to increased oxidative metabolism, a primary mechanism of action for DCA, closer to exposure windows especially early in life. Conversely, many gene signatures and pathways reversed patterns later in life and reflected more pro-tumorigenic patterns for both current and prior DCA exposures. DNA methylation patterns correlated to early gene pathway perturbations, such as cellular signaling, regulation and metabolism, suggesting persistence in the epigenome and possible regulatory effects. Conclusion: Liver metabolic reprogramming effects of DCA interacted with normal age mechanisms, increasing tumor burden with both continuous and prior DCA exposure in the male B6C3F1 rodent model.

Differences in molecular sampling and data processing explain variation among single-cell and single-nucleus RNA-seq experiments.

A mechanistic understanding of the biological and technical factors that impact transcript measurements is essential to designing and analyzing single-cell and single-nucleus RNA sequencing experiments. Nuclei contain the same pre-mRNA population as cells, but they contain a small subset of the mRNAs. Nonetheless, early studies argued that single-nucleus analysis yielded results comparable to cellular samples if pre-mRNA measurements were included. However, typical workflows do not distinguish between pre-mRNA and mRNA when estimating gene expression, and variation in their relative abundances across cell types has received limited attention. These gaps are especially important given that incorporating pre-mRNA has become commonplace for both assays, despite known gene length bias in pre-mRNA capture. Here, we reanalyze public data sets from mouse and human to describe the mechanisms and contrasting effects of mRNA and pre-mRNA sampling on gene expression and marker gene selection in single-cell and single-nucleus RNA-seq. We show that pre-mRNA levels vary considerably among cell types, which mediates the degree of gene length bias and limits the generalizability of a recently published normalization method intended to correct for this bias. As an alternative, we repurpose an existing post hoc gene length-based correction method from conventional RNA-seq gene set enrichment analysis. Finally, we show that inclusion of pre-mRNA in bioinformatic processing can impart a larger effect than assay choice itself, which is pivotal to the effective reuse of existing data. These analyses advance our understanding of the sources of variation in single-cell and single-nucleus RNA-seq experiments and provide useful guidance for future studies.

Evaluation of software impact designed for biomedical research: Are we measuring what's meaningful?

Software is vital for the advancement of biology and medicine. Through analysis of usage and impact metrics of software, developers can help determine user and community engagement. These metrics can be used to justify additional funding, encourage additional use, and identify unanticipated use cases. Such analyses can help define improvement areas and assist with managing project resources. However, there are challenges associated with assessing usage and impact, many of which vary widely depending on the type of software being evaluated. These challenges involve issues of distorted, exaggerated, understated, or misleading metrics, as well as ethical and security concerns. More attention to the nuances, challenges, and considerations involved in capturing impact across the diverse spectrum of biological software is needed. Furthermore, some tools may be especially beneficial to a small audience, yet may not have comparatively compelling metrics of high usage. Although some principles are generally applicable, there is not a single perfect metric or approach to effectively evaluate a software tool's impact, as this depends on aspects unique to each tool, how it is used, and how one wishes to evaluate engagement. We propose more broadly applicable guidelines (such as infrastructure that supports the usage of software and the collection of metrics about usage), as well as strategies for various types of software and resources. We also highlight outstanding issues in the field regarding how communities measure or evaluate software impact. To gain a deeper understanding of the issues hindering software evaluations, as well as to determine what appears to be helpful, we performed a survey of participants involved with scientific software projects for the Informatics Technology for Cancer Research (ITCR) program funded by the National Cancer Institute (NCI). We also investigated software among this scientific community and others to assess how often infrastructure that supports such evaluations is implemented and how this impacts rates of papers describing usage of the software. We find that although developers recognize the utility of analyzing data related to the impact or usage of their software, they struggle to find the time or funding to support such analyses. We also find that infrastructure such as social media presence, more in-depth documentation, the presence of software health metrics, and clear information on how to contact developers seem to be associated with increased usage rates. Our findings can help scientific software developers make the most out of the evaluations of their software so that they can more fully benefit from such assessments.

Differential alternative splicing between hepatocellular carcinoma with normal and elevated serum alpha-fetoprotein.

BACKGROUND: Serum alpha-fetoprotein (AFP) is the approved serum marker for hepatocellular carcinoma (HCC) screening. However, not all HCC patients show high (≥ 20 ng/mL) serum AFP, and the molecular mechanisms of HCCs with normal (< 20 ng/mL) serum AFP remain to be elucidated. Therefore, we aimed to identify biological features of HCCs with normal serum AFP by investigating differential alternative splicing (AS) between HCCs with normal and high serum AFP. METHODS: We performed a genome-wide survey of AS events in 249 HCCs with normal (n = 131) and high (n = 118) serum AFP levels using RNA-sequencing data obtained from The Cancer Genome Atlas. RESULTS: In group comparisons of RNA-seq profiles from HCCs with normal and high serum AFP levels, 161 differential AS events (125 genes; ΔPSI > 0.05, FDR < 0.05) were identified to be alternatively spliced between the two groups. Those genes were enriched in cell migration or proliferation terms such as "the cell migration and growth-cone collapse" and "regulation of insulin-like growth factor (IGF) transport and uptake by IGF binding proteins". Most of all, two AS genes (FN1 and FAM20A) directly interact with AFP; these relate to the regulation of IGF transport and post-translational protein phosphorylation. Interestingly, 42 genes and 27 genes were associated with gender and vascular invasion (VI), respectively, but only eighteen genes were significant in survival analysis. We especially highlight that FN1 exhibited increased differential expression of AS events (ΔPSI > 0.05), in which exons 25 and 33 were more frequently skipped in HCCs with normal (low) serum AFP compared to those with high serum AFP. Moreover, these events were gender and VI dependent. CONCLUSION: We found that AS may influence the regulation of transcriptional differences inherent in the occurrence of HCC maintaining normal rather than elevated serum AFP levels.

Differential alternative splicing regulation among hepatocellular carcinoma with different risk factors.

BACKGROUND: Hepatitis B virus (HBV), hepatitis C virus (HCV), and alcohol consumption are predominant causes of hepatocellular carcinoma (HCC). However, the molecular mechanisms underlying how differently these causes are implicated in HCC development are not fully understood. Therefore, we investigated differential alternative splicing (AS) regulation among HCC patients with these risk factors. METHODS: We conducted a genome-wide survey of AS events associated with HCCs among HBV (n = 95), HCV (n = 47), or alcohol (n = 76) using RNA-sequencing data obtained from The Cancer Genome Atlas. RESULTS: In three group comparisons of HBV vs. HCV, HBV vs. alcohol, and HCV vs. alcohol for RNA seq (ΔPSI> 0.05, FDR < 0.05), 133, 93, and 29 differential AS events (143 genes) were identified, respectively. Of 143 AS genes, eight and one gene were alternatively spliced specific to HBV and HCV, respectively. Through functional analysis over the canonical pathways and gene ontologies, we identified significantly enriched pathways in 143 AS genes including immune system, mRNA splicing-major pathway, and nonsense-mediated decay, which may be important to carcinogenesis in HCC risk factors. Among eight genes with HBV-specific splicing events, HLA-A, HLA-C, and IP6K2 exhibited more differential expression of AS events (ΔPSI> 0.1). Intron retention of HLA-A was observed more frequently in HBV-associated HCC than HCV- or alcohol-associated HCC, and intron retention of HLA-C showed vice versa. Exon 3 (based on ENST00000432678) of IP6K2 was less skipped in HBV-associated in HCC compared to HCV- or alcohol-associated HCC. CONCLUSION: AS may play an important role in regulating transcription differences implicated in HBV-, HCV-, and alcohol-related HCC development.

Impact of Nutrient Availability on Tertiary Wastewater Treatment by Chlorella vulgaris.

Algae can use excessive nutrients from wastewater effluent to generate beneficial products such as biofuels. However, fluctuation of wastewater characteristics could hinder the implementation of tertiary algal treatment. This study aims to identify the impact of nutrient availability on Chlorella vulgaris's ability to remove nitrogen and phosphorus from wastewater, and its potential as a biofuel feedstock. Experiments using synthetic wastewater with varying concentrations of nitrate and phosphate showed C. vulgaris continued to remove either nutrient when the other was exhausted. Nitrogen starvation led the algae to accumulate the highest amount of neutral lipid; however, the exhaustion of phosphorus did not produce such impact. Synergistic effect was also observed between C. vulgaris and indigenous microorganisms in nutrient removal from real wastewater effluent. The results showed C. vulgaris can survive in a range of nutrient-limiting conditions, making tertiary algal treatment applicable following various secondary treatment regimes.

Leveraging human genetic and adverse outcome pathway (AOP) data to inform susceptibility in human health risk assessment.

Estimation of susceptibility differences in human health risk assessment (HHRA) has been challenged by a lack of available susceptibility and variability data after exposure to a specific environmental chemical or pharmaceutical. With the increasingly large number of available data sources that contain polymorphism and other genetic data, human genetic variability that informs susceptibility can be better incorporated into HHRA. A recent policy, the 2016 The Frank R. Lautenberg Chemical Safety for the twenty-first Century Act, requires the US Environmental Protection Agency to evaluate new and existing toxic chemicals with explicit consideration of susceptible populations of all types (life stage, exposure, genetic, etc.). We propose using the adverse outcome pathway (AOP) construct to organize, identify, and characterize human genetic susceptibility in HHRA. We explore how publicly available human genetic datasets can be used to gain mechanistic understanding of molecular events and characterize human susceptibility for an adverse outcome. We present a computational method that implements publicly available human genetic data to prioritize AOPs with potential for human genetic variability. We describe the application of this approach across multiple described AOPs for health outcomes of interest, and by focusing on a single molecular initiating event. This contributes to a long-term goal to improve estimates of human susceptibility for use in HHRA for single and multiple chemicals.