Chen, Jun

AbstractMolecular prognostic factors for individualized treatment of squamous cell carcinoma (SCC) are poorly defined. Our study developed and validated a novel molecular tools aid in preinguinal and postinguinal lymphadenectomy risk stratification in node‐positive penile SCC. Patients with node‐positive penile SCC who underwent inguinal or ilioinguinal lymphadenectomy were divided into three cohorts: a discovery set, a development set and a validation set. The local ethics committee approved the study. The primary endpoint was cancer‐specific survival (CSS). At the discovery stage, 17 CpG sites were significantly associated with CSS. In the development set, we constructed a 3‐CpG‐based prognostic score for survival prediction. The hazard ratio (HR) of the panel (dichotomized using the optimal cutoff) was 5.8 in the multivariate analyses (P < .001). The addition of the methylation score significantly improved the pN‐stage C‐index from 0.70 to 0.79 (incremental C = 0.09, P < .001). In the validation set, the methylation panel showed a HR of 9.9 in the multivariate analyses. The addition of the molecular marker improved the pN‐stage C‐index from 0.69 to 0.78 (incremental C = 0.09, P < .001). The methylation score remarkably separated survival curves in different pN stages, which indicate that the tool can be applied to tailor the treatment in both preinguinal and postinguinal lymphadenectomy settings. We developed and validated a prognostic methylation panel for node‐positive penile SCC. The tool may aid in the risk stratification of the population with heterogeneous outcomes and needs prospective validation. Patients in high‐risk group may benefit from more aggressive therapy or clinical trials.

AbstractVariance component models have gained popularity for genetic analyses, driven by their flexibility to simultaneously analyze multiple genetic variants in a gene by kernel statistics, and their ability to account for population stratification via genomic relationship matrices. For exploratory analyses with modest sample sizes and a potentially large number of variance components, it can be challenging to use standard maximum‐likelihood or restricted maximum‐likelihood methods to estimate variance components, because these iterative methods often fail to converge when likelihood surfaces are fairly flat, and standard‐likelihood ratio statistical tests are not adequate. To overcome these limitations, we developed a penalized‐likelihood model, whereby the penalty function follows the popular elastic‐net approach, applying both L1 and L2 penalties to the variance components. By simulations, we demonstrate the potential gain in power by using both L1 and L2 penalties, and results from our simulations suggest that assigning 80% of the penalty parameter to the L1 penalty and 20% to the L2 penalty provides a reasonable balance between false‐positive and false‐negative results. Larger sample size improves the properties of our methods, at the cost of longer computation time. Application of our methods to a study of the influence of DNA methylation on levels of cortisol in reaction to stress testing shows how our method can be used to prioritize findings for further functional studies.

A major direction for future microbiome research is implementation of fecal sample collections in large-scale, prospective epidemiologic studies. Studying microbiome-disease associations likely requires microbial data to be pooled from multiple studies. Our findings suggest collection methods that are most optimal to be used standardly across future WGSS microbiome studies.

Colorectal adenomas are precursors of CRC. Recently, the gut microbiota, i.e., the collection of microbes residing in our gut, has been recognized as a key player in CRC development. There have been a number of gut microbiota profiling studies for colorectal adenoma and CRC; however, fewer studies have considered the gut metabolome, which serves as the chemical interface between the host and gut microbiota. Here, we conducted a gut metabolome profiling study of colorectal adenoma and CRC and analyzed the metabolomic profiles together with paired microbiota composition profiles. We found several chemical signatures of colorectal adenoma that were associated with some gut microbes and potentially indicative of future CRC. This study highlights potential early-driver metabolites in CRC pathogenesis and guides further targeted experiments and thus provides an important stepping stone toward developing better CRC prevention strategies.

INTRODUCTION: Primary sclerosing cholangitis (PSC) is a chronic, fibroinflammatory, cholestatic liver disease of unknown etiopathogenesis which is recognized as a major risk factor for cholangiocarcinoma (CCA). We aimed to investigate the roles of the biliary and intestinal microflora in the inflammatory process of PSC and to determine if the microflora is associated with progression to CCA. METHODS: We studied the bile and stool microbiomes in samples obtained from patients with PSC (31 stool and 35 bile samples), sporadic CCA (11 stool and 26 bile samples), and PSC coexisting with CCA (16 stool and bile 17 samples). Control bile was obtained from patients with cholelithiasis or choledocholithiasis (n = 17). DNA extraction was followed by amplification and high-throughput next generation sequencing (MiSeq) of the 16S rDNA (V3-V5) region to identify the microbiota present. We performed: 1. analysis of the stool microbiome between PSC and CCA patients; 2. analysis of the biliary microbiome in PSC and CCA patients; 3. comparison of the biliary microbiome collected before and after biliary stent placement. RESULTS: Although bile samples from study patients had significantly lower sequencing depths than stool, their sequencing depths were much higher than bile from negative controls. The microbiomes of stool and bile samples from the same patients were significantly correlated ( P = 0.022). CCA bile samples had significant difference of alpha diversity from control bile samples with less Streptococcaceae and Desulfovibrionaceae (FDR < 10%) than PSC whereas no significant change of stool microbiome was identified between CCA and PSC. Fusobacterium and Gemella were correlated to PSC duration. An increased abundance of Fusobacteria after stent placement was revealed. Fusobacteria, which is inflammation-promoting, was also increased with the number of stents in the bile duct. CONCLUSION: Our study suggests that the bile microbiome is potentially a more distinctive biomarker than the stool microbiome for PSC and CCA development. There is increased inhabitance of inflammation-promoting bacteria in the bile of CCA patients and after biliary stent placement. Increased PSC duration also increases the microbial burden in the bile, which may also promote CCA development. We further detected specific bile microbes statistically associated with CCA or stent presence in the bile ducts, which raises the possibility of their role in the CCA progression or stent obstruction. Additional analyses with more samples are needed.

ABSTRACT The adult zebrafish is an emerging vertebrate model for studying human cardiomyopathies; however, whether the simple zebrafish heart can model different subtypes of cardiomyopathies, such as dilated cardiomyopathy (DCM), remains elusive. Here, we generated and characterized an inherited DCM model in adult zebrafish and used this model to search for therapeutic strategies. We employed transcription activator-like effector nuclease (TALEN) genome editing technology to generate frame-shift mutants for the zebrafish ortholog of human BCL2-associated athanogene 3 (BAG3), an established DCM-causative gene. As in mammals, the zebrafish bag3 homozygous mutant (bag3e2/e2) exhibited aberrant proteostasis, as indicated by impaired autophagy flux and elevated ubiquitinated protein aggregation. Through comprehensive phenotyping analysis of the mutant, we identified phenotypic traits that resembled DCM phenotypes in mammals, including cardiac chamber enlargement, reduced ejection fraction characterized by increased end-systolic volume/body weight (ESV/BW), and reduced contractile myofibril activation kinetics. Nonbiased transcriptome analysis identified the hyperactivation of the mechanistic target of rapamycin (mTOR) signaling in bag3e2/e2 mutant hearts. Further genetic studies showed that mtorxu015/+, an mTOR haploinsufficiency mutant, repaired abnormal proteostasis, improved cardiac function and rescued the survival of the bag3e2/e2 mutant. This study established the bag3e2/e2 mutant as a DCM model in adult zebrafish and suggested mtor as a candidate therapeutic target gene for BAG3 cardiomyopathy.

Objective Obesity is a risk factor for colorectal cancer (CRC), accounting for more than 14% of CRC incidence. Microbial dysbiosis and chronic inflammation are common characteristics in both obesity and CRC. Human and murine studies, together, demonstrate the significant impact of the microbiome in governing energy metabolism and CRC development; yet, little is understood about the contribution of the microbiome to development of obesity-associated CRC as compared to individuals who are not obese. Design In this study, we conducted a meta-analysis using five publicly available stool and tissue-based 16S rRNA and whole genome sequencing (WGS) data sets of CRC microbiome studies. High-resolution analysis was employed for 16S rRNA data, which allowed us to achieve species-level information to compare with WGS. Results Characterisation of the confounders between studies, 16S rRNA variable region and sequencing method did not reveal any significant effect on alpha diversity in CRC prediction. Both 16S rRNA and WGS were equally variable in their ability to predict CRC. Results from diversity analysis confirmed lower diversity in obese individuals without CRC; however, no universal differences were found in diversity between obese and non-obese individuals with CRC. When examining taxonomic differences, the probability of being classified as CRC did not change significantly in obese individuals for all taxa tested. However, random forest classification was able to distinguish CRC and non-CRC stool when body mass index was added to the model. Conclusion Overall, microbial dysbiosis was not a significant factor in explaining the higher risk of colon cancer among individuals with obesity.

Abstract Motivation The human microbiome is notoriously variable across individuals, with a wide range of ‘healthy’ microbiomes. Paired and longitudinal studies of the microbiome have become increasingly popular as a way to reduce unmeasured confounding and to increase statistical power by reducing large inter-subject variability. Statistical methods for analyzing such datasets are scarce. Results We introduce a paired UniFrac dissimilarity that summarizes within-individual (or within-pair) shifts in microbiome composition and then compares these compositional shifts across individuals (or pairs). This dissimilarity depends on a novel transformation of relative abundances, which we then extend to more than two time points and incorporate into several phylogenetic and non-phylogenetic dissimilarities. The data transformation and resulting dissimilarities may be used in a wide variety of downstream analyses, including ordination analysis and distance-based hypothesis testing. Simulations demonstrate that tests based on these dissimilarities retain appropriate type 1 error and high power. We apply the method in two real datasets. Availability and implementation The R package pldist is available on GitHub at https://github.com/aplantin/pldist. Supplementary information Supplementary data are available at Bioinformatics online.

ABSTRACT Evaluating the association of multiple genetic variants with a trait of interest by use of kernel‐based methods has made a significant impact on how genetic association analyses are conducted. An advantage of kernel methods is that they tend to be robust when the genetic variants have effects that are a mixture of positive and negative effects, as well as when there is a small fraction of causal variants. Another advantage is that kernel methods fit within the framework of mixed models, providing flexible ways to adjust for additional covariates that influence traits. Herein, we review the basic ideas behind the use of kernel methods for genetic association analysis as well as recent methodological advancements for different types of traits, multivariate traits, pedigree data, and longitudinal data. Finally, we discuss opportunities for future research.