Statistical tests for intra-tumour clonal co-occurrence and exclusivity.

Tumour progression is an evolutionary process in which different clones evolve over time, leading to intra-tumour heterogeneity. Interactions between clones can affect tumour evolution and hence disease progression and treatment outcome. Intra-tumoural pairs of mutations that are overrepresented in a co-occurring or clonally exclusive fashion over a cohort of patient samples may be suggestive of a synergistic effect between the different clones carrying these mutations. We therefore developed a novel statistical testing framework, called GeneAccord, to identify such gene pairs that are altered in distinct subclones of the same tumour. We analysed our framework for calibration and power. By comparing its performance to baseline methods, we demonstrate that to control type I errors, it is essential to account for the evolutionary dependencies among clones. In applying GeneAccord to the single-cell sequencing of a cohort of 123 acute myeloid leukaemia patients, we find 1 clonally co-occurring and 8 clonally exclusive gene pairs. The clonally exclusive pairs mostly involve genes of the key signalling pathways.

Bone quality analysis of jaw bones in individuals with type 2 diabetes mellitus-post mortem anatomical and microstructural evaluation.

OBJECTIVES: With the higher risk of dental implant failure with type 2 diabetes mellitus (T2DM), there is a need to characterize the jaw bones in those individuals. The aim of this post mortem study was to compare jaw bone quality of individuals with T2DM to healthy controls. MATERIAL AND METHODS: Bone cores from the edentulous lower first molar region and the region of mandibular angle were collected from male individuals with T2DM (n = 10, 70.6 ± 4.5 years) and healthy controls (n = 11, 71.5 ± 3.8 years) during autopsy. Within the T2DM, a subgroup treated with oral antidiabetics (OAD) and one on insulin were identified. Bone quality assessment encompassed evaluation of bone microstructure, matrix composition, and cellular activity, using microcomputed tomography (micro-CT), quantitative backscattered electron imaging (qBEI), Raman spectroscopy, and bone histomorphometry. RESULTS: In the mandibular angle, T2DM showed 51% lower porosity of the lingual cortex (p = 0.004) and 21% higher trabecular thickness (p = 0.008) compared to control. More highly mineralized bone packets were found in the buccal cortex of the mandibular angle in insulin-treated compared to OAD-treated T2DM group (p = 0.034). In the molar region, we found higher heterogeneity of trabecular calcium content in T2DM insulin compared to controls (p = 0.015) and T2DM OAD (p = 0.019). T2DM was associated with lower osteocyte lacunar size in the trabecular bone of the molar region (vs. control p = 0.03). CONCLUSIONS: Alterations in microstructure, mineralization, and osteocyte morphology were determined in jaw bone of individuals with T2DM compared to controls. CLINICAL RELEVANCE: Future studies will have to verify if the mild changes determined in this study will translate to potential contraindications for dental implant placements.

[Drug interactions in dermatological systemic treatment]. / Wechselwirkungen in der dermatologischen Systemtherapie.

Severe pharmacological side effects have an occurrence of 5-7% and represent a frequent reason for hospital admission. The prevalence of undesired pharmacological side effects during hospitalization is even higher with approximately 11.5%. The causes are often interactions between drugs due to the polypharmacy of multimorbid older patients. On average, a 65-year-old male patient will simultaneously be taking 5 medications. Due to the increasing use of systemic drugs in dermatology and the simultaneously increasing polypharmacy, knowledge of interactions between medications is essential for dermatologists in order to avoid severe side effects of drugs. This article provides assistance in order to identify patients and medications with a high risk for severe interactions and, therefore, to avoid the occurrence of undesired effects or the reduction of the therapeutic effects of active substances. We would like to point out that this article deals with individual aspects and does not mean that the testing of individual drug interactions with interaction programs can be omitted. It should also not be neglected that in addition to prescription-only drugs, foodstuffs, dietary supplements and herbs can also lead to interactions with medications.

Single-cell sequencing data reveal widespread recurrence and loss of mutational hits in the life histories of tumors.

Intra-tumor heterogeneity poses substantial challenges for cancer treatment. A tumor's composition can be deduced by reconstructing its mutational history. Central to current approaches is the infinite sites assumption that every genomic position can only mutate once over the lifetime of a tumor. The validity of this assumption has never been quantitatively assessed. We developed a rigorous statistical framework to test the infinite sites assumption with single-cell sequencing data. Our framework accounts for the high noise and contamination present in such data. We found strong evidence for the same genomic position being mutationally affected multiple times in individual tumors for 11 of 12 single-cell sequencing data sets from a variety of human cancers. Seven cases involved the loss of earlier mutations, five of which occurred at sites unaffected by large-scale genomic deletions. Four cases exhibited a parallel mutation, potentially indicating convergent evolution at the base pair level. Our results refute the general validity of the infinite sites assumption and indicate that more complex models are needed to adequately quantify intra-tumor heterogeneity for more effective cancer treatment.

Computational Analysis of DNA and RNA Sequencing Data Obtained from Liquid Biopsies.

Next-generation sequencing of DNA and RNA obtained from liquid biopsies of cancer patients may reveal important insights into disease progression and metastasis formation, and it holds the promise to enable new methods for noninvasive screening and clinical decision support. However, implementing liquid biopsy sequencing protocols is challenged by capturing circulating tumor cells or cell-free tumor DNA from blood samples, by amplifying genomic DNA and RNA in a reliable and unbiased manner, and by extracting biologically meaningful signals from the noisy sequencing data. In this chapter, we discuss computational methods for the analysis of DNA and RNA sequencing data obtained from liquid biopsies, addressing these challenges.

Advances in understanding tumour evolution through single-cell sequencing.

The mutational heterogeneity observed within tumours poses additional challenges to the development of effective cancer treatments. A thorough understanding of a tumour's subclonal composition and its mutational history is essential to open up the design of treatments tailored to individual patients. Comparative studies on a large number of tumours permit the identification of mutational patterns which may refine forecasts of cancer progression, response to treatment and metastatic potential. The composition of tumours is shaped by evolutionary processes. Recent advances in next-generation sequencing offer the possibility to analyse the evolutionary history and accompanying heterogeneity of tumours at an unprecedented resolution, by sequencing single cells. New computational challenges arise when moving from bulk to single-cell sequencing data, leading to the development of novel modelling frameworks. In this review, we present the state of the art methods for understanding the phylogeny encoded in bulk or single-cell sequencing data, and highlight future directions for developing more comprehensive and informative pictures of tumour evolution. This article is part of a Special Issue entitled: Evolutionary principles - heterogeneity in cancer?, edited by Dr. Robert A. Gatenby.

Perturbed bone composition and integrity with disorganized osteoblast function in zinc receptor/Gpr39-deficient mice.

Changes in bone matrix composition are frequently found with bone diseases and may be associated with increased fracture risk. Bone is rich in the trace element zinc. Zinc was established to play a significant role in the growth, development, and maintenance of healthy bones; however, the mechanisms underlying zinc effects on the integrity of the skeleton are poorly understood. Here, we show that the zinc receptor (ZnR)/Gpr39 is required for normal bone matrix deposition by osteoblasts. Initial analysis showed that Gpr39-deficient ( Gpr39-/-) mice had weaker bones as a result of altered bone composition. Fourier transform infrared spectroscopy analysis showed high mineral-to-matrix ratios in the bones of Gpr39-/- mice. Histologic analysis showed abnormally high numbers of active osteoblasts but normal osteoclast numbers on the surfaces of bones from Gpr39-/- mice. Furthermore, Gpr39-/- osteoblasts had disorganized matrix deposition in vitro with cultures exhibiting abnormally low collagen and high mineral contents, findings that demonstrate a cell-intrinsic role for ZnR/Gpr39 in these cells. We show that both collagen synthesis and deposition by Gpr39-/- osteoblasts are perturbed. Finally, the expression of the zinc transporter Zip13 and a disintegrin and metalloproteinase with thrombospondin motifs family of zinc-dependent metalloproteases that regulate collagen processing was downregulated in Gpr39-/- osteoblasts. Altogether, our results suggest that zinc sensing by ZnR/Gpr39 affects the expression levels of zinc-dependent enzymes in osteoblasts and regulates collagen processing and deposition.-Jovanovic, M., Schmidt, F. N., Guterman-Ram, G., Khayyeri, H., Hiram-Bab, S., Orenbuch, A., Katchkovsky, S., Aflalo, A., Isaksson, H., Busse, B., Jähn, K., Levaot, N. Perturbed bone composition and integrity with disorganized osteoblast function in zinc receptor/Gpr39-deficient mice.

Physiological and pathological osteocytic osteolysis.

Osteocytes, the most abundant bone cell in the adult skeleton, can function as mechanosensors directing osteoblast and osteoclast function in order to maintain optimal load bearing bone in addition to functioning as endocrine cells regulating phosphate metabolism. A controversial function, previously overlooked or denied, has been osteocytes as regulators of calcium metabolism. Early histologists upon observing enlarged osteocyte lacunae in bone sections proposed that mature osteocytes could remove their perilacunar matrix, a term called "osteocytic osteolysis". New insights into this process have occurred during the last decade using novel technology thereby providing a means to identify molecular mechanisms responsible for osteocytic osteolysis. As release of calcium from a mineralized matrix requires a more acidic pH and specialized enzymes, it was proposed that osteocytes may utilize similar molecular mechanisms as osteoclasts to remove mineral. The idea that a cell descended from mesenchymal progenitors (the osteocyte) could function similarly to a cell descended from hematopoietic progenitors (the osteoclast) was challenged as being improbable. Here we review the molecular mechanisms behind this osteocyte function, the role of osteocytic osteolysis in health and disease, and the capacity of the osteocyte to reverse the osteolytic process by replacing the removed matrix, a revived osteoblast function.

Finding approximate gene clusters with Gecko 3.

Gene-order-based comparison of multiple genomes provides signals for functional analysis of genes and the evolutionary process of genome organization. Gene clusters are regions of co-localized genes on genomes of different species. The rapid increase in sequenced genomes necessitates bioinformatics tools for finding gene clusters in hundreds of genomes. Existing tools are often restricted to few (in many cases, only two) genomes, and often make restrictive assumptions such as short perfect conservation, conserved gene order or monophyletic gene clusters. We present Gecko 3, an open-source software for finding gene clusters in hundreds of bacterial genomes, that comes with an easy-to-use graphical user interface. The underlying gene cluster model is intuitive, can cope with low degrees of conservation as well as misannotations and is complemented by a sound statistical evaluation. To evaluate the biological benefit of Gecko 3 and to exemplify our method, we search for gene clusters in a dataset of 678 bacterial genomes using Synechocystis sp. PCC 6803 as a reference. We confirm detected gene clusters reviewing the literature and comparing them to a database of operons; we detect two novel clusters, which were confirmed by publicly available experimental RNA-Seq data. The computational analysis is carried out on a laptop computer in <40 min.

Phenotype and Viability of MLO-Y4 Cells Is Maintained by TGFß3 in a Serum-Dependent Manner within a 3D-Co-Culture with MG-63 Cells.

The osteocyte network inside the bone matrix is of functional importance and osteocyte cell death is a characteristic feature of pathological bone diseases. Osteocytes have emerged as key regulators of bone tissue maintenance, yet maintaining their phenotype during in vitro culture remains challenging. A 3D co-culture system for osteocytes with osteoblasts was recently presented, enabling the determination of more physiological effects of growth factors on cells in vitro. MLO-Y4 cells were embedded within a type I collagen gel and cultured in the presence of surface MG-63 cells. Co-culture was performed in the presence or absence of TGFß3. Gene expression by quantitative PCR, protein expression by fluorescent immunohistochemistry and cell viability tests were performed. The 3D co-culture induced cell differentiation of MG-63 cells seen by increased type I collagen and osteocalcin mRNA expression. TGFβ3 maintained osteocyte differentiation of MLO-Y4 cells during co-culture as determined by stable E11 and osteocalcin mRNA expression till day 4. Interestingly, most of the effects of TGFß3 on co-cultured cells were serum-dependent. Also, TGFß3 reduced cell death of 3D co-cultured MLO-Y4 cells in a serum-dependent manner. This study shows that 3D co-culture upregulates differentiation of MG-63 cells to a more mature osteoblast-like phenotype; while the addition of TGFß3 maintained the characteristic MLO-Y4 osteocyte-like phenotype and viability in a serum-dependent manner.

Identifying gene clusters by discovering common intervals in indeterminate strings.

BACKGROUND: Comparative analyses of chromosomal gene orders are successfully used to predict gene clusters in bacterial and fungal genomes. Present models for detecting sets of co-localized genes in chromosomal sequences require prior knowledge of gene family assignments of genes in the dataset of interest. These families are often computationally predicted on the basis of sequence similarity or higher order features of gene products. Errors introduced in this process amplify in subsequent gene order analyses and thus may deteriorate gene cluster prediction. RESULTS: In this work, we present a new dynamic model and efficient computational approaches for gene cluster prediction suitable in scenarios ranging from traditional gene family-based gene cluster prediction, via multiple conflicting gene family annotations, to gene family-free analysis, in which gene clusters are predicted solely on the basis of a pairwise similarity measure of the genes of different genomes. We evaluate our gene family-free model against a gene family-based model on a dataset of 93 bacterial genomes. CONCLUSIONS: Our model is able to detect gene clusters that would be also detected with well-established gene family-based approaches. Moreover, we show that it is able to detect conserved regions which are missed by gene family-based methods due to wrong or deficient gene family assignments.

Mutation order in acute myeloid leukemia identifies uncommon patterns of evolution and illuminates phenotypic heterogeneity.

Acute myeloid leukemia (AML) has a poor prognosis and a heterogeneous mutation landscape. Although common mutations are well-studied, little research has characterized how the sequence of mutations relates to clinical features. Using published, single-cell DNA sequencing data from three institutions, we compared clonal evolution patterns in AML to patient characteristics, disease phenotype, and outcomes. Mutation trees, which represent the order of select mutations, were created for 207 patients from targeted panel sequencing data using 1 639 162 cells, 823 mutations, and 275 samples. In 224 distinct orderings of mutated genes, mutations related to DNA methylation typically preceded those related to cell signaling, but signaling-first cases did occur, and had higher peripheral cell counts, increased signaling mutation homozygosity, and younger patient age. Serial sample analysis suggested that NPM1 and DNA methylation mutations provide an advantage to signaling mutations in AML. Interestingly, WT1 mutation evolution shared features with signaling mutations, such as WT1-early being proliferative and occurring in younger individuals, trends that remained in multivariable regression. Some mutation orderings had a worse prognosis, but this was mediated by unfavorable mutations, not mutation order. These findings add a dimension to the mutation landscape of AML, identifying uncommon patterns of leukemogenesis and shedding light on heterogeneous phenotypes.

Statistics for approximate gene clusters.

BACKGROUND: Genes occurring co-localized in multiple genomes can be strong indicators for either functional constraints on the genome organization or remnant ancestral gene order. The computational detection of these patterns, which are usually referred to as gene clusters, has become increasingly sensitive over the past decade. The most powerful approaches allow for various types of imperfect cluster conservation: Cluster locations may be internally rearranged. The individual cluster locations may contain only a subset of the cluster genes and may be disrupted by uninvolved genes. Moreover cluster locations may not at all occur in some or even most of the studied genomes. The detection of such low quality clusters increases the risk of mistaking faint patterns that occur merely by chance for genuine findings. Therefore, it is crucial to estimate the significance of computational gene cluster predictions and discriminate between true conservation and coincidental clustering. RESULTS: In this paper, we present an efficient and accurate approach to estimate the significance of gene cluster predictions under the approximate common intervals model. Given a single gene cluster prediction, we calculate the probability to observe it with the same or a higher degree of conservation under the null hypothesis of random gene order, and add a correction factor to account for multiple testing. Our approach considers all parameters that define the quality of gene cluster conservation: the number of genomes in which the cluster occurs, the number of involved genes, the degree of conservation in the different genomes, as well as the frequency of the clustered genes within each genome. We apply our approach to evaluate gene cluster predictions in a large set of well annotated genomes.

COMPASS: joint copy number and mutation phylogeny reconstruction from amplicon single-cell sequencing data.

Reconstructing the history of somatic DNA alterations can help understand the evolution of a tumor and predict its resistance to treatment. Single-cell DNA sequencing (scDNAseq) can be used to investigate clonal heterogeneity and to inform phylogeny reconstruction. However, most existing phylogenetic methods for scDNAseq data are designed either for single nucleotide variants (SNVs) or for large copy number alterations (CNAs), or are not applicable to targeted sequencing. Here, we develop COMPASS, a computational method for inferring the joint phylogeny of SNVs and CNAs from targeted scDNAseq data. We evaluate COMPASS on simulated data and apply it to several datasets including a cohort of 123 patients with acute myeloid leukemia. COMPASS detected clonal CNAs that could be orthogonally validated with bulk data, in addition to subclonal ones that require single-cell resolution, some of which point toward convergent evolution.

To be or not to be stressed: Designing autonomy to reduce stress at work.

BACKGROUND: Many organizations are undertaking efforts to reduce the stress of (oftentimes overworked) employees. Information Technology (IT) (e.g., smartphones) has the potential to be a key instrument for reducing stress. One design-relevant factor considered to reduce stress is the concept of autonomy. Unfortunately, little research exists using autonomy as a characteristic of technology design. OBJECTIVE: Against this background, this study aimed to investigate specific autonomy-related design options with the potential to prevent stress. METHODS: In a factorial survey, this experimental study tested three design options in an overwork scenario: 1) autonomy (no intervention by design), 2) nudge ("nudging" by design), and 3) enforcement (hard stop by design). 51 participants (mean age 38 years, 50% women, mean work experience 18 years) from the Netherlands, United Kingdom, United States of America, and Germany participated in the experiment for 330 seconds on average. To test our hypothesis, we used a two-step approach. First, a multiple linear regression was applied. Second, we carried out a one-way ANCOVA comparing the effects of our design options. RESULTS: Our results indicate that autonomy can be manipulated through technology design and is negatively correlated with stress. Additionally, the design options autonomy and nudge were associated with lower levels of perceived stress than was enforcement. CONCLUSION: The study proposes a careful use of IT and policies that limit the perceived autonomy of employees. Overall, this study offers a set of design recommendations arguing that organizations should implement technology that helps employees prevent overwork and maintain their autonomy.

Mutation order in acute myeloid leukemia identifies uncommon patterns of evolution and illuminates phenotypic heterogeneity.

Acute myeloid leukemia (AML) has a poor prognosis and a heterogeneous mutation landscape. Although common mutations are well-studied, little research has characterized how the sequence of mutations relates to clinical features. Using published, single-cell DNA sequencing data from three institutions, we compared clonal evolution patterns in AML to patient characteristics, disease phenotype, and outcomes. Mutation trees, which represent the order of select mutations, were created for 207 patients from targeted panel sequencing data using 1 639 162 cells, 823 mutations, and 275 samples. In 224 distinct orderings of mutated genes, mutations related to DNA methylation typically preceded those related to cell signaling, but signaling-first cases did occur, and had higher peripheral cell counts, increased signaling mutation homozygosity, and younger patient age. Serial sample analysis suggested that NPM1 and DNA methylation mutations provide an advantage to signaling mutations in AML. Interestingly, WT1 mutation evolution shared features with signaling mutations, such as WT1-early being proliferative and occurring in younger individuals, trends that remained in multivariable regression. Some mutation orderings had a worse prognosis, but this was mediated by unfavorable mutations, not mutation order. These findings add a dimension to the mutation landscape of AML, identifying uncommon patterns of leukemogenesis and shedding light on heterogenous phenotypes.

Multiple genome comparison based on overlap regions of pairwise local alignments.

BACKGROUND: Mancheron, Uricaru and Rivals (Nucleic Acids Res. 39:e101, 2011) recently introduced a new approach in the context of multiple genome comparison that allows to detect regions of strong overlaps in a set of pairwise local alignments between several reference genomes and one target genome. Such overlap regions are an important source of information in genome annotation. RESULTS: In this paper we introduce a series of algorithms that improve over the approach of Mancheron et al., both in terms of computational complexity and in practical runtime. We also extend the problem definition such that overlaps to different reference genomes can be rated differently and regions overlapping only a subset of the reference genomes are detected.

A consolidation algorithm for genomes fractionated after higher order polyploidization.

BACKGROUND: It has recently been shown that fractionation, the random loss of excess gene copies after a whole genome duplication event, is a major cause of gene order disruption. When estimating evolutionary distances between genomes based on chromosomal rearrangement, fractionation inevitably leads to significant overestimation of classic rearrangement distances. This bias can be largely avoided when genomes are preprocessed by "consolidation", a procedure that identifies and accounts for regions of fractionation. RESULTS: In this paper, we present a new consolidation algorithm that extends and improves previous work in several directions. We extend the notion of the fractionation region to use information provided by regions where this process is still ongoing. The new algorithm can optionally work with this new definition of fractionation region and is able to process not only tetraploids but also genomes that have undergone hexaploidization and polyploidization events of higher order. Finally, this algorithm reduces the asymptotic time complexity of consolidation from quadratic to linear dependence on the genome size. The new algorithm is applied both to plant genomes and to simulated data to study the effect of fractionation in ancient hexaploids.

Early detection and surveillance of SARS-CoV-2 genomic variants in wastewater using COJAC.

The continuing emergence of SARS-CoV-2 variants of concern and variants of interest emphasizes the need for early detection and epidemiological surveillance of novel variants. We used genomic sequencing of 122 wastewater samples from three locations in Switzerland to monitor the local spread of B.1.1.7 (Alpha), B.1.351 (Beta) and P.1 (Gamma) variants of SARS-CoV-2 at a population level. We devised a bioinformatics method named COJAC (Co-Occurrence adJusted Analysis and Calling) that uses read pairs carrying multiple variant-specific signature mutations as a robust indicator of low-frequency variants. Application of COJAC revealed that a local outbreak of the Alpha variant in two Swiss cities was observable in wastewater up to 13 d before being first reported in clinical samples. We further confirmed the ability of COJAC to detect emerging variants early for the Delta variant by analysing an additional 1,339 wastewater samples. While sequencing data of single wastewater samples provide limited precision for the quantification of relative prevalence of a variant, we show that replicate and close-meshed longitudinal sequencing allow for robust estimation not only of the local prevalence but also of the transmission fitness advantage of any variant. We conclude that genomic sequencing and our computational analysis can provide population-level estimates of prevalence and fitness of emerging variants from wastewater samples earlier and on the basis of substantially fewer samples than from clinical samples. Our framework is being routinely used in large national projects in Switzerland and the UK.

Swiftly computing center strings.

BACKGROUND: The center string (or closest string) problem is a classic computer science problem with important applications in computational biology. Given k input strings and a distance threshold d, we search for a string within Hamming distance at most d to each input string. This problem is NP complete. RESULTS: In this paper, we focus on exact methods for the problem that are also swift in application. We first introduce data reduction techniques that allow us to infer that certain instances have no solution, or that a center string must satisfy certain conditions. We describe how to use this information to speed up two previously published search tree algorithms. Then, we describe a novel iterative search strategy that is efficient in practice, where some of our reduction techniques can also be applied. Finally, we present results of an evaluation study for two different data sets from a biological application. CONCLUSIONS: We find that the running time for computing the optimal center string is dominated by the subroutine calls for d = dopt -1 and d = dopt. Our data reduction is very effective for both, either rejecting unsolvable instances or solving trivial positions. We find that this speeds up computations considerably.