A Bayesian inference method to estimate transmission trees with multiple introductions; applied to SARS-CoV-2 in Dutch mink farms.

Knowledge of who infected whom during an outbreak of an infectious disease is important to determine risk factors for transmission and to design effective control measures. Both whole-genome sequencing of pathogens and epidemiological data provide useful information about the transmission events and underlying processes. Existing models to infer transmission trees usually assume that the pathogen is introduced only once from outside into the population of interest. However, this is not always true. For instance, SARS-CoV-2 is suggested to be introduced multiple times in mink farms in the Netherlands from the SARS-CoV-2 pandemic among humans. Here, we developed a Bayesian inference method combining whole-genome sequencing data and epidemiological data, allowing for multiple introductions of the pathogen in the population. Our method does not a priori split the outbreak into multiple phylogenetic clusters, nor does it break the dependency between the processes of mutation, within-host dynamics, transmission, and observation. We implemented our method as an additional feature in the R-package phybreak. On simulated data, our method correctly identifies the number of introductions, with an accuracy depending on the proportion of all observed cases that are introductions. Moreover, when a single introduction was simulated, our method produced similar estimates of parameters and transmission trees as the existing package. When applied to data from a SARS-CoV-2 outbreak in Dutch mink farms, the method provides strong evidence for independent introductions of the pathogen at 13 farms, infecting a total of 63 farms. Using the new feature of the phybreak package, transmission routes of a more complex class of infectious disease outbreaks can be inferred which will aid infection control in future outbreaks.

MutationalPatterns: the one stop shop for the analysis of mutational processes.

BACKGROUND: The collective of somatic mutations in a genome represents a record of mutational processes that have been operative in a cell. These processes can be investigated by extracting relevant mutational patterns from sequencing data. RESULTS: Here, we present the next version of MutationalPatterns, an R/Bioconductor package, which allows in-depth mutational analysis of catalogues of single and double base substitutions as well as small insertions and deletions. Major features of the package include the possibility to perform regional mutation spectra analyses and the possibility to detect strand asymmetry phenomena, such as lesion segregation. On top of this, the package also contains functions to determine how likely it is that a signature can cause damaging mutations (i.e., mutations that affect protein function). This updated package supports stricter signature refitting on known signatures in order to prevent overfitting. Using simulated mutation matrices containing varied signature contributions, we showed that reliable refitting can be achieved even when only 50 mutations are present per signature. Additionally, we incorporated bootstrapped signature refitting to assess the robustness of the signature analyses. Finally, we applied the package on genome mutation data of cell lines in which we deleted specific DNA repair processes and on large cancer datasets, to show how the package can be used to generate novel biological insights. CONCLUSIONS: This novel version of MutationalPatterns allows for more comprehensive analyses and visualization of mutational patterns in order to study the underlying processes. Ultimately, in-depth mutational analyses may contribute to improved biological insights in mechanisms of mutation accumulation as well as aid cancer diagnostics. MutationalPatterns is freely available at http://bioconductor.org/packages/MutationalPatterns .

Interventions to control nosocomial transmission of SARS-CoV-2: a modelling study.

BACKGROUND: Emergence of more transmissible SARS-CoV-2 variants requires more efficient control measures to limit nosocomial transmission and maintain healthcare capacities during pandemic waves. Yet the relative importance of different strategies is unknown. METHODS: We developed an agent-based model and compared the impact of personal protective equipment (PPE), screening of healthcare workers (HCWs), contact tracing of symptomatic HCWs and restricting HCWs from working in multiple units (HCW cohorting) on nosocomial SARS-CoV-2 transmission. The model was fit on hospital data from the first wave in the Netherlands (February until August 2020) and assumed that HCWs used 90% effective PPE in COVID-19 wards and self-isolated at home for 7 days immediately upon symptom onset. Intervention effects on the effective reproduction number (RE), HCW absenteeism and the proportion of infected individuals among tested individuals (positivity rate) were estimated for a more transmissible variant. RESULTS: Introduction of a variant with 56% higher transmissibility increased - all other variables kept constant - RE from 0.4 to 0.65 (+ 63%) and nosocomial transmissions by 303%, mainly because of more transmissions caused by pre-symptomatic patients and HCWs. Compared to baseline, PPE use in all hospital wards (assuming 90% effectiveness) reduced RE by 85% and absenteeism by 57%. Screening HCWs every 3 days with perfect test sensitivity reduced RE by 67%, yielding a maximum test positivity rate of 5%. Screening HCWs every 3 or 7 days assuming time-varying test sensitivities reduced RE by 9% and 3%, respectively. Contact tracing reduced RE by at least 32% and achieved higher test positivity rates than screening interventions. HCW cohorting reduced RE by 5%. Sensitivity analyses show that our findings do not change significantly for 70% PPE effectiveness. For low PPE effectiveness of 50%, PPE use in all wards is less effective than screening every 3 days with perfect sensitivity but still more effective than all other interventions. CONCLUSIONS: In response to the emergence of more transmissible SARS-CoV-2 variants, PPE use in all hospital wards might still be most effective in preventing nosocomial transmission. Regular screening and contact tracing of HCWs are also effective interventions but critically depend on the sensitivity of the diagnostic test used.

Full-length de novo viral quasispecies assembly through variation graph construction.

MOTIVATION: Viruses populate their hosts as a viral quasispecies: a collection of genetically related mutant strains. Viral quasispecies assembly is the reconstruction of strain-specific haplotypes from read data, and predicting their relative abundances within the mix of strains is an important step for various treatment-related reasons. Reference genome independent ('de novo') approaches have yielded benefits over reference-guided approaches, because reference-induced biases can become overwhelming when dealing with divergent strains. While being very accurate, extant de novo methods only yield rather short contigs. The remaining challenge is to reconstruct full-length haplotypes together with their abundances from such contigs. RESULTS: We present Virus-VG as a de novo approach to viral haplotype reconstruction from preassembled contigs. Our method constructs a variation graph from the short input contigs without making use of a reference genome. Then, to obtain paths through the variation graph that reflect the original haplotypes, we solve a minimization problem that yields a selection of maximal-length paths that is, optimal in terms of being compatible with the read coverages computed for the nodes of the variation graph. We output the resulting selection of maximal length paths as the haplotypes, together with their abundances. Benchmarking experiments on challenging simulated and real datasets show significant improvements in assembly contiguity compared to the input contigs, while preserving low error rates compared to the state-of-the-art viral quasispecies assemblers. AVAILABILITY AND IMPLEMENTATION: Virus-VG is freely available at https://bitbucket.org/jbaaijens/virus-vg. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Precancerous liver diseases do not cause increased mutagenesis in liver stem cells.

Inflammatory liver disease increases the risk of developing primary liver cancer. The mechanism through which liver disease induces tumorigenesis remains unclear, but is thought to occur via increased mutagenesis. Here, we performed whole-genome sequencing on clonally expanded single liver stem cells cultured as intrahepatic cholangiocyte organoids (ICOs) from patients with alcoholic cirrhosis, non-alcoholic steatohepatitis (NASH), and primary sclerosing cholangitis (PSC). Surprisingly, we find that these precancerous liver disease conditions do not result in a detectable increased accumulation of mutations, nor altered mutation types in individual liver stem cells. This finding contrasts with the mutational load and typical mutational signatures reported for liver tumors, and argues against the hypothesis that liver disease drives tumorigenesis via a direct mechanism of induced mutagenesis. Disease conditions in the liver may thus act through indirect mechanisms to drive the transition from healthy to cancerous cells, such as changes to the microenvironment that favor the outgrowth of precancerous cells.

Reconstructing single-cell karyotype alterations in colorectal cancer identifies punctuated and gradual diversification patterns.

Central to tumor evolution is the generation of genetic diversity. However, the extent and patterns by which de novo karyotype alterations emerge and propagate within human tumors are not well understood, especially at single-cell resolution. Here, we present 3D Live-Seq-a protocol that integrates live-cell imaging of tumor organoid outgrowth and whole-genome sequencing of each imaged cell to reconstruct evolving tumor cell karyotypes across consecutive cell generations. Using patient-derived colorectal cancer organoids and fresh tumor biopsies, we demonstrate that karyotype alterations of varying complexity are prevalent and can arise within a few cell generations. Sub-chromosomal acentric fragments were prone to replication and collective missegregation across consecutive cell divisions. In contrast, gross genome-wide karyotype alterations were generated in a single erroneous cell division, providing support that aneuploid tumor genomes can evolve via punctuated evolution. Mapping the temporal dynamics and patterns of karyotype diversification in cancer enables reconstructions of evolutionary paths to malignant fitness.

Publisher Correction: The mutational impact of culturing human pluripotent and adult stem cells.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The mutational impact of culturing human pluripotent and adult stem cells.

Genetic changes acquired during in vitro culture pose a risk for the successful application of stem cells in regenerative medicine. To assess the genetic risks induced by culturing, we determined all mutations in individual human stem cells by whole genome sequencing. Individual pluripotent, intestinal, and liver stem cells accumulate 3.5 ± 0.5, 7.2 ± 1.1 and 8.3 ± 3.6 base substitutions per population doubling, respectively. The annual in vitro mutation accumulation rate of adult stem cells is nearly 40-fold higher than the in vivo mutation accumulation rate. Mutational signature analysis reveals that in vitro induced mutations are caused by oxidative stress. Reducing oxygen tension in culture lowers the mutational load. We use the mutation rates, spectra, and genomic distribution to model the accumulation of oncogenic mutations during typical in vitro expansion, manipulation or screening experiments using human stem cells. Our study provides empirically defined parameters to assess the mutational risk of stem cell based therapies.

5-Fluorouracil treatment induces characteristic T>G mutations in human cancer.

5-Fluorouracil (5-FU) is a chemotherapeutic drug commonly used for the treatment of solid cancers. It is proposed that 5-FU interferes with nucleotide synthesis and incorporates into DNA, which may have a mutational impact on both surviving tumor and healthy cells. Here, we treat intestinal organoids with 5-FU and find a highly characteristic mutational pattern that is dominated by T>G substitutions in a CTT context. Tumor whole genome sequencing data confirms that this signature is also identified in vivo in colorectal and breast cancer patients who have received 5-FU treatment. Taken together, our results demonstrate that 5-FU is mutagenic and may drive tumor evolution and increase the risk of secondary malignancies. Furthermore, the identified signature shows a strong resemblance to COSMIC signature 17, the hallmark signature of treatment-naive esophageal and gastric tumors, which indicates that distinct endogenous and exogenous triggers can converge onto highly similar mutational signatures.