Open Access

While ionizing radiation (IR) is a powerful tool in medical diagnostics, nuclear medicine,and radiology, it also is a serious threat to the integrity of genetic material. Mutagenic effects ofIR to the human genome have long been the subject of research, yet still comparatively little isknown about the genome-wide effects of IR exposure on the DNA-sequence level. In this study,we employed high throughput sequencing technologies to investigate IR-induced DNA alterationsin human gingiva fibroblasts (HGF) that were acutely exposed to 0.5, 2, and 10 Gy of 240 kVX-radiation followed by repair times of 16 h or 7 days before whole-genome sequencing (WGS).Our analysis of the obtained WGS datasets revealed patterns of IR-induced variant (SNV and InDel)accumulation across the genome, within chromosomes as well as around the borders of topologicallyassociating domains (TADs). Chromosome 19 consistently accumulated the highest SNVs andInDels events. Translocations showed variable patterns but with recurrent chromosomes of origin(e.g., Chr7 andChr16). IR-induced InDels showed a relative increase in number relative to SNVs anda characteristic signature with respect to the frequency of triplet deletions in areas without repetitiveor microhomology features. Overall experimental conditions and datasets the majority of SNVs pergenome had no or little predicted functional impact with a maximum of 62, showing damagingpotential. A dose-dependent effect of IR was surprisingly not apparent. We also observed a significantreduction in transition/transversion (Ti/Tv) ratios for IR-dependent SNVs, which could point to acontribution of the mismatch repair (MMR) system that strongly favors the repair of transitions overtransversions, to the IR-induced DNA-damage response in human cells. Taken together, our resultsshow the presence of distinguishable characteristic patterns of IR-induced DNA-alterations on agenome-wide level and implicate DNA-repair mechanisms in the formation of these signatures

Influenza A Virus (IAV) infection followed by bacterial pneumonia often leads to hospitalization and death in individuals from high risk groups. Following infection, IAV triggers the process of viral RNA replication which in turn disrupts healthy gut microbial community, while the gut microbiota plays an instrumental role in protecting the host by evolving colonization resistance. Although the underlying mechanisms of IAV infection have been unraveled, the underlying complex mechanisms evolved by gut microbiota in order to induce host immune response following IAV infection remain evasive. In this work, we developed a novel Maximal-Clique based Community Detection algorithm for Weighted undirected Networks (MCCD-WN) and compared its performance with other existing algorithms using three sets of benchmark networks. Moreover, we applied our algorithm to gut microbiome data derived from fecal samples of both healthy and IAV-infected pigs over a sequence of time-points. The results we obtained from the real-life IAV dataset unveil the role of the microbial families Ruminococcaceae, Lachnospiraceae, Spirochaetaceae and Prevotellaceae in the gut microbiome of the IAV-infected cohort. Furthermore, the additional integration of metaproteomic data enabled not only the identification of microbial biomarkers, but also the elucidation of their functional roles in protecting the host following IAV infection. Our network analysis reveals a fast recovery of the infected cohort after the second IAV infection and provides insights into crucial roles of Desulfovibrionaceae and Lactobacillaceae families in combating Influenza A Virus infection. Source code of the community detection algorithm can be downloaded from https://github.com/AniBhar84/MCCD-WN.

Never in the past has the relevance of bioinformatic and predictive tools been more central in the field of virology as today. SARS-CoV-2 has brought along a huge health burden, but also a deeper awareness that scientific progress can no longer be effective without extensive systems for data storage, sharing and analysis, as well as computational tools dedicated to molecular epidemiology, NGS data analysis, prediction of drug targets, multi-OMIC data integration, and many other applications.

Background Approaching epidemiological data with flexible machine learning algorithms is of great value for understanding disease-specific association patterns. However, it can be difficult to correctly extract and understand those patterns due to the lack of model interpretability. Method We here propose a machine learning workflow that combines random forests with Bayesian network surrogate models to allow for a deeper level of interpretation of complex association patterns. We first evaluate the proposed workflow on synthetic data. We then apply it to data from the large population-based Study of Health in Pomerania (SHIP). Based on this combination, we discover and interpret broad patterns of individual serum TSH concentrations, an important marker of thyroid functionality. Results Evaluations using simulated data show that feature associations can be correctly recovered by combining random forests and Bayesian networks. The presented model achieves predictive accuracy that is similar to state-of-the-art models (root mean square error of 0.66, mean absolute error of 0.55, coefficient of determination of R2 = 0.15). We identify 62 relevant features from the final random forest model, ranging from general health variables over dietary and genetic factors to physiological, hematological and hemostasis parameters. The Bayesian network model is used to put these features into context and make the black-box random forest model more understandable. Conclusion We demonstrate that the combination of random forest and Bayesian network analysis is helpful to reveal and interpret broad association patterns of individual TSH concentrations. The discovered patterns are in line with state-of-the-art literature. They may be useful for future thyroid research and improved dosing of therapeutics.