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The COVID-19 pandemic caused by corona virus in 2019 has forced all the health and public organi-zations, across the globe, to take different measures for identification, prevention, and control of the disease. Early and accurate detection of the corona virus in human body being one of the im-portant measures to prevent the disease spread in population and successful eradication. However, available and known methods and technologies and project management systems in this context have not been used to the extent necessary to adequately control infection events (Lehrach, 2020; Glitscher, 2021).Today different testing methods are being used to check the COVID-19 infection for isolation of in-fected from uninfected population. The accuracy, time between sample collection to results declara-tion, testing cost, etc. depend on the type of testing method. In addition to the use of accurate testing method, it becomes necessary to ensure that the test centers are mobile and accessible to a maximum extent, so that the testing intensity can be regulated depending on the rate of spread, potential sudden outbreaks, areas of concern, etc. In this context, a new high throughput genome -based testing pipeline for SARS-CoV-2is proposed (Lehrach, 2022) which uses the combination of state-of-the art biotechnology and in-formatics. This proposed new method is claimed to deliver faster and cheaper test results at the population scale, without compromising the testing quality. This study is aimed to develop a project plan to design such a container-based COVID- 19 testing facility. Literature study shows a lack of frameworks to develop a design for mobile medical facility (Carl Heneghan et al. 2020; Bridges et al. 2014). On the other hand, at least in Germany, companies like centogene have deployed container-based mobile test stations. However, based on available experience, these are limited in terms of use and throughput of tests (Centogene 2020; Kyoug Ho Roh 2022).Thus, this study proposes a framework to design a mobile medical test facility, in addition to propos-ing a project plan to design a mobile container-based facility to implement the new COVID-19 test-ing method. The framework proposed is based on the combined adaptation of facility design meth-ods, namely - Systematic Layout Planning and project-based approach as per VDI 5200. The resulting outputs in the form of a project plan include - capacity plan, material flow plan and financial plan. It is clear from these plans that the industrialization of the new testing facility is feasi-ble in terms of testing throughput - approximately 450,000 tests per day (when the line is operated in all three shifts) - and cost per test being 3 Euros, spatial requirements as proposed in the draft layout, based on the inputs as published (Lehrach, 2022). The identified risks do not pose alarming threats to continue with the implementation, however contingency plans should be considered for every worst-case scenario.

PDF file - 175K, Table S1 Clinical data and sequencing statistics. Table S2: Oligonucleotides for library construction and quantification and BS-MS-primers used for validation of MeDIP-Seq data. Table S3 Top 100 differentially methylated regions between tumor and normal (a) and between FUS+ and FUS- samples (b). Table S4 Correlation of EZH2 expression to expression of homeobox (Holland et al. 2007) and target genes (Yu et al. 2007) and target promoter methylation (+-2kb)

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PDF file - 947K, Supplementary Fig. S1. Differential methylations in PCa validated by BS-MS experiments. Supplementary Fig. S2. DNA-Methylomes of prostate cancer differ significantly from normal tissues. Supplementary Fig. S3. Global differences in DNA methylation patterns between tumor, normal, FUS+ and FUS- samples. Supplementary Fig. S4 Patientwise patterns of promoter hypermethylation and corresponding gene expression of EZH2 target genes. Supplementary Fig. S5 Methylation of the miRNA-26a genomic region

The Covid-19 pandemic accelerated research and development not only in infectious diseases but also in digital technologies to improve monitoring, forecasting, and intervening on planetary and ecological risks. In the European Commission, the Destination Earth (DestinE) is a current major initiative to develop a digital model of the Earth (a "digital twin") with high precision. Moreover, omics systems science is undergoing digital transformation impacting nearly all dimensions of the field, including real-time phenotype capture to data analytics using machine learning and artificial intelligence, to name but a few emerging frontiers. We discuss the ways in which the current ongoing digital transformation in omics offers synergies with digital twins/DestinE. Importantly, we note here the rise of a new field of scholarship, planetary health genomics. We conclude that digital transformation in public and private sectors, digital twins/DestinE, and their convergence with omics systems science are poised to build robust capacities for pandemic preparedness and resilient societies in the 21st century.

Single molecule protein sequencing would tremendously impact in proteomics and human biology and it would promote the development of novel diagnostic and therapeutic approaches. However, its technological realization can only be envisioned, and huge challenges need to be overcome. Major difficulties are inherent to the structure of proteins, which are composed by several different amino-acids. Despite long standing efforts, only few complex techniques, such as Edman degradation, liquid chromatography and mass spectroscopy, make protein sequencing possible. Unfortunately, these techniques present significant limitations in terms of amount of sample required and dynamic range of measurement. It is known that proteins can distinguish closely similar molecules. Moreover, several proteins can work as biological nanopores in order to perform single molecule detection and sequencing. Unfortunately, while DNA sequencing by means of nanopores is demonstrated, very few examples of nanopores able to perform reliable protein-sequencing have been reported so far. Here, we investigate, by means of molecular dynamics simulations, how a re-engineered protein, acting as biological nanopore, can be used to recognize the sequence of a translocating peptide by sensing the “shape” of individual amino-acids. In our simulations we demonstrate that it is possible to discriminate with high fidelity, 9 different amino-acids in a short peptide translocating through the engineered construct. The method, here shown for fluorescence-based sequencing, does not require any labelling of the peptidic analyte. These results can pave the way for a new and highly sensitive method of sequencing.

Our lives (and deaths) have by now been dominated for two years by COVID-19, a pandemic that has caused hundreds of millions of disease cases, millions of deaths, trillions in economic costs, and major restrictions on our freedom. Here we suggest a novel tool for controlling the COVID-19 pandemic. The key element is a method for a population-scale PCR-based testing, applied on a systematic and repeated basis. For this we have developed a low cost, highly sensitive virus-genome-based test. Using Germany as an example, we demonstrate by using a mathematical model, how useful this strategy could have been in controlling the pandemic. We show using real-world examples how this might be implemented on a mass scale and discuss the feasibility of this approach.

Secondary infections contribute significantly to covid-19 mortality but host and microbial factors driving this sequel remain poorly understood. We performed an autopsy study of 20 covid-19 cases and 14 controls from the first pandemic wave. Autopsies combined with microbial cultivation and deep RNA sequencing (RNAseq) allowed us to define major organ pathologies and specify secondary infections. Lethal covid-19 segregated into two main death causes separating cases with either dominant diffuse alveolar damage (DAD) or secondary infections of lungs. Lung microbiome changes were profound in covid-19 showing a reduced biodiversity and increased presence of prototypical bacterial and fungal pathogens in cases with secondary infections. Deep RNAseq of lung tissues distinctly mirrored death causes and cellular deconvolution stratified DAD cases into subgroups with different cellular compositions. Myeloid cells, including macrophages, and complement C1q activation were found to be strong stratifying factors suggesting a pathophysiological link possibly leading to tolerance in DAD subgroups. Moreover, several signs of immune-impairment were evident in covid-19 lungs including strong induction of inhibitory immune-checkpoints. Thus, our study highlights profound alterations of the local immunity in covid-19, wherein immune-impairment leads to reduced antimicrobial defense favoring the development of secondary infections on top of SARS-CoV-2 infection.

All people are unique and so are their diseases. Our genomes, disease histories, behavior, and lifestyles are all different; therefore it is not too surprising that people often respond differently when administered the same drugs. Cancer, in particular, is a complex and heterogeneous disease, originating in patients with different genomes, in cells with the different epigenomes, formed and evolving on the basis of random processes, with the response to therapy not only depending on the individual cancer cell but also on many features of the patient. Selection of an optimal therapy will therefore require a deep molecular analysis comprising both the patient and their tumor (e.g., comprehensive molecular tumor analysis [CMTA]), and much better personalized prediction of response to possible therapies. Currently, we are at an inflection point in which advances in technology, decreases in the costs of sequencing and other molecular analyses, and increases in computing advances are converging, forming the foundation to build a data-driven approach to personalized oncology. In this article we discuss the deep molecular characterization of individual tumors and patients as the basis of not only current precision oncology but also of computational models (‘digital twins’), the foundation for a truly personalized therapy selection of the future. We are all very different, with different genomes, different disease histories, different behavior and molecularly different diseases. It is therefore not surprising that we often react differently to drugs we receive. To overcome this in oncology, we require much deeper data on individual tumors and patients (e.g., comprehensive molecular tumor analysis, CMTA), and much better personalized prediction of the effects of possible therapies, initially through precision medicine, but increasingly through digital models of individual tumors and patients, our “digital twins”.

A nonlinear model consisting of a system of coupled ordinary differential equations (ODE), describing a biological process linked with cancer development, is linearized using Taylor series and tested against different magnitudes of input perturbations, in order to investigate the extent to which the linearization is accurate. The canonical wingless/integrated (WNT) signaling pathway is considered. The linearization procedure is described, and special considerations for linearization validity are analyzed. The analytical properties of nonlinear and linearized systems are studied, including aspects such as existence of steady state and initial value sensitivity. Linearization is a useful tool for speeding up drug response computations or for providing analytical answers to problems such as required drug concentrations. A Monte Carlo-based error testing workflow is employed to study the errors introduced by the linearization for different input conditions and parameter vectors. The deviations between the nonlinear and the linearized system were found to increase in a polynomial fashion w.r.t. the magnitude of tested perturbations. The linearized system closely followed the original one for perturbations of magnitude within 10% of the base input vector which yielded the state-space fixed point used for the linearization.

Our lives (and deaths) have been dominated for more than a year by COVID-19, a pandemic that has caused hundreds of millions of disease cases, millions of deaths, trillions in economic costs, and major restrictions on our freedom. We argue that much of this could have been avoided by repeated and systematic population-scale PCR-based testing and targeted quarantine. We describe key elements of the current implementations of such a system and demonstrate (with Germany as an example), that this strategy could have suppressed the pandemic within weeks, eliminating the vast majority of its overall impact in terms of deaths, economic costs and restrictions. It can, however, still play a major role in further reducing the worldwide impact of the current phase of the pandemic, and remain as a key protection against similar dangers in the future.

Our lives (and deaths) have been dominated for more than a year by COVID-19, a pandemic that has caused hundreds of millions of disease cases, millions of deaths, trillions in economic costs, and major restrictions on our freedom. We argue that much of this could have been avoided by repeated and systematic population-scale PCR-based testing and targeted quarantine. We describe key elements of the current implementations of such a system and demonstrate (with Germany as an example), that this strategy could have suppressed the pandemic within weeks, eliminating the vast majority of its overall impact in terms of deaths, economic costs and restrictions. It can, however, still play a major role in further reducing the worldwide impact of the current phase of the pandemic, and remain as a key protection against similar dangers in the future.

The development of the technology required to generate (molecular) digital twins provides the technological basis for in silico clinical trials, as well as major virtualization of many other steps in the drug development process, offering the potential of orders of magnitude reductions in the cost of bringing new drugs to the market, and a corresponding increase in new drugs in many disease areas, including those for which risks have been considered too high to justify commercial development. The same basic technology will, however, also be used to truly personalize therapy choice and preventive and wellness measures, further increasing health and well-being worldwide.

Persistent activation of hedgehog (HH)/GLI signaling accounts for the development of basal cell carcinoma (BCC), a very frequent nonmelanoma skin cancer with rising incidence. Targeting HH/GLI signaling by approved pathway inhibitors can provide significant therapeutic benefit to BCC patients. However, limited response rates, development of drug resistance, and severe side effects of HH pathway inhibitors call for improved treatment strategies such as rational combination therapies simultaneously inhibiting HH/GLI and cooperative signals promoting the oncogenic activity of HH/GLI. In this study, we identified the interleukin-6 (IL6) pathway as a novel synergistic signal promoting oncogenic HH/GLI via STAT3 activation. Mechanistically, we provide evidence that signal integration of IL6 and HH/GLI occurs at the level of cis-regulatory sequences by co-binding of GLI and STAT3 to common HH-IL6 target gene promoters. Genetic inactivation of Il6 signaling in a mouse model of BCC significantly reduced in vivo tumor growth by interfering with HH/GLI-driven BCC proliferation. Our genetic and pharmacologic data suggest that combinatorial HH-IL6 pathway blockade is a promising approach to efficiently arrest cancer growth in BCC patients.

"-Omics" research is in transition with the recent rise of multi-omics technology platforms. Integration of "-omics" and multi-omics research is of high priority in sepsis, a heterogeneous syndrome that is widely recognized as a global health burden and a priority biomedical funding field. We report here an original study on bibliometric trends in the use of "-omics" technologies, and multi-omics approaches in particular, in sepsis research in three (supra)national settings, the United States, the European Union 28 Member States (EU-28), and China. Using a 5-year longitudinal bibliometric study design from 2011 to 2015, we analyzed the sepsis-related research articles in English language that included at least one or multi-omics technologies in publicly available form in Medline (free full texts). We found that the United States has had the lead (almost one-third of publications) in the inclusion of an "-omics" or multi-omics technology in sepsis within the study period. However, both China and the EU-28 displayed a significant increase in the number of publications that employed one or more types of "-omics" research (p

Summary: Colon cancer is a heterogeneous tumor driven by a subpopulation of cancer stem cells (CSCs). To study CSCs in colon cancer, we used limiting dilution spheroid and serial xenotransplantation assays to functionally define the frequency of CSCs in a panel of patient-derived cancer organoids. These studies demonstrated cancer organoids to be enriched for CSCs, which varied in frequency between tumors. Whole-transcriptome analysis identified WNT and Hedgehog signaling components to be enhanced in CSC-enriched tumors and in aldehyde dehydrogenase (ALDH)-positive CSCs. Canonical GLI-dependent Hedgehog signaling is a negative regulator of WNT signaling in normal intestine and intestinal tumors. Here, we show that Hedgehog signaling in colon CSCs is autocrine SHH-dependent, non-canonical PTCH1 dependent, and GLI independent. In addition, using small-molecule inhibitors and RNAi against SHH-palmitoylating Hedgehog acyltransferase (HHAT), we demonstrate that non-canonical Hedgehog signaling is a positive regulator of WNT signaling and required for colon CSC survival. : Colon cancer is a heterogeneous tumor driven by a subpopulation(s) of therapy-resistant cancer stem cells (CSCs). Regan et al. use 3D culture models to demonstrate that CSC survival is regulated by non-canonical, SHH-dependent, PTCH1-dependent Hedgehog signaling, which acts as a positive regulator of WNT signaling to block CSC differentiation. Keywords: WNT pathway, non-canonical Hedgehog signaling, cancer stem cell, colon cancer, cancer organoid, PTCH1, HHAT, SHH

Triosephosphate isomerase (TPI) deficiency is a fatal genetic disorder characterized by hemolytic anemia and neurological dysfunction. Although the enzyme defect in TPI was discovered in the 1960s, the exact etiology of the disease is still debated. Some aspects indicate the disease could be caused by insufficient enzyme activity, whereas other observations indicate it could be a protein misfolding disease with tissue‐specific differences in TPI activity. We generated a mouse model in which exchange of a conserved catalytic amino acid residue (isoleucine to valine, Ile170Val) reduces TPI specific activity without affecting the stability of the protein dimer. TPIIle170Val/Ile170Val mice exhibit an approximately 85% reduction in TPI activity consistently across all examined tissues, which is a stronger average, but more consistent, activity decline than observed in patients or symptomatic mouse models that carry structural defect mutant alleles. While monitoring protein expression levels revealed no evidence for protein instability, metabolite quantification indicated that glycolysis is affected by the active site mutation. TPIIle170Val/Ile170Val mice develop normally and show none of the disease symptoms associated with TPI deficiency. Therefore, without the stability defect that affects TPI activity in a tissue‐specific manner, a strong decline in TPI catalytic activity is not sufficient to explain the pathological onset of TPI deficiency., A new mouse model shows that reducing enzyme activity, without affecting the overall structure of triosephopshate isomerase (TPI), does not induce TPI deficiency‐like symptoms in mice.

Deletions at the C-terminus of the proto-oncogene protein c-Src kinase are found in the viral oncogene protein v-Src as well as in some advanced human colon cancers. They are associated with increased kinase activity and cellular invasiveness. Here, we analyzed the mRNA expression signature of a constitutively active C-terminal mutant of c-Src, c-Src(mt), in comparison with its wild-type protein, c-Src(wt), in the human non-transformed breast epithelial cell line MCF-10A. We demonstrated previously that the mutant altered migratory and metastatic properties. Genome-wide transcriptome analysis revealed that c-Src(mt) de-regulated the expression levels of approximately 430 mRNAs whose gene products are mainly involved in the cellular processes of migration and adhesion, apoptosis and protein synthesis. 82.9% of these genes have previously been linked to cellular migration, while the others play roles in RNA transport and splicing processes, for instance. Consistent with the transcriptome data, cells expressing c-Src(mt), but not those expressing c-Src(wt), showed the capacity to metastasize into the lungs of mice in vivo. The mRNA expression profile of c-Src(mt)-expressing cells shows significant overlap with that of various primary human tumor samples, possibly reflecting elevated Src activity in some cancerous cells. Expression of c-Src(mt) led to elevated migratory potential. We used this model system to analyze the transcriptional changes associated with an invasive cellular phenotype. These genes and pathways de-regulated by c-Src(mt) may provide suitable biomarkers or targets of therapeutic approaches for metastatic cells. DATABASE: This project was submitted to the National Center for Biotechnology Information BioProject under ID PRJNA288540. The Illumina RNA-Seq reads are available in the National Center for Biotechnology Information Sequence Read Archive under study ID SRP060008 with accession numbers SRS977414 for MCF-10A cells, SRS977717 for mock cells, SRS978053 for c-Src(wt) cells and SRS978046 for c-Src(mt) cells.

Medulloblastoma is a highly malignant paediatric brain tumour, often inflicting devastating consequences on the developing child. Genomic studies have revealed four distinct molecular subgroups with divergent biology and clinical behaviour. An understanding of the regulatory circuitry governing the transcriptional landscapes of medulloblastoma subgroups, and how this relates to their respective developmental origins, is lacking. Here, using H3K27ac and BRD4 chromatin immunoprecipitation followed by sequencing (ChIP-seq) coupled with tissue-matched DNA methylation and transcriptome data, we describe the active cis-regulatory landscape across 28 primary medulloblastoma specimens. Analysis of differentially regulated enhancers and super-enhancers reinforced inter-subgroup heterogeneity and revealed novel, clinically relevant insights into medulloblastoma biology. Computational reconstruction of core regulatory circuitry identified a master set of transcription factors, validated by ChIP-seq, that is responsible for subgroup divergence, and implicates candidate cells of origin for Group 4. Our integrated analysis of enhancer elements in a large series of primary tumour samples reveals insights into cis-regulatory architecture, unrecognized dependencies, and cellular origins.

A substantial fraction of phenotypic differences between closely related species are likely caused by differences in gene regulation. While this has already been postulated over 30 years ago, only few examples of evolutionary changes in gene regulation have been verified. Here, we identified and investigated binding sites of the transcription factor GA-binding protein alpha (GABPa) aiming to discover cis-regulatory adaptations on the human lineage. By performing chromatin immunoprecipitation-sequencing experiments in a human cell line, we found 11,619 putative GABPa binding sites. Through sequence comparisons of the human GABPa binding regions with orthologous sequences from 34 mammals, we identified substitutions that have resulted in 224 putative human-specific GABPa binding sites. To experimentally assess the transcriptional impact of those substitutions, we selected four promoters for promoter-reporter gene assays using human and African green monkey cells. We compared the activities of wild-type promoters to mutated forms, where we have introduced one or more substitutions to mimic the ancestral state devoid of the GABPa consensus binding sequence. Similarly, we introduced the human-specific substitutions into chimpanzee and macaque promoter backgrounds. Our results demonstrate that the identified substitutions are functional, both in human and nonhuman promoters. In addition, we performed GABPa knock-down experiments and found 1,215 genes as strong candidates for primary targets. Further analyses of our data sets link GABPa to cognitive disorders, diabetes, KRAB zinc finger (KRAB-ZNF), and human-specific genes. Thus, we propose that differences in GABPa binding sites played important roles in the evolution of human-specific phenotypes.

The biological processes that keep us healthy or cause disease, as well as the mechanisms of action of possible drugs are inherently complex. In the face of this complexity, attempts at discovering new drugs to treat diseases have alternated between trial-and-error (typically on experimental systems) and grand simplification, usually based on much too little information. We now have the chance to combine these strategies through establishment of 'virtual patient' models, centred on a detailed molecular characterisation of thousands or even, in the future, millions of patients. In doing so, we lay the foundations for truly personalised therapy, as well as a far-reaching virtualisation of drug discovery and development in oncology and other areas of medicine.

Combined transcriptome and whole genome sequencing of the same ultra-low input sample down to single cells is a rapidly evolving approach for the analysis of rare cells. Besides stem cells, rare cells originating from tissues like tumor or biopsies, circulating tumor cells and cells from early embryonic development are under investigation. Herein we describe a universal method applicable for the analysis of minute amounts of sample material (150 to 200 cells) derived from sub-colony structures from human embryonic stem cells. The protocol comprises the combined isolation and separate amplification of poly(A) mRNA and whole genome DNA followed by next generation sequencing. Here we present a detailed description of the method developed and an overview of the results obtained for RNA and whole genome sequencing of human embryonic stem cells, sequencing data is available in the Gene Expression Omnibus (GEO) database under accession number GSE69471.

[Background] Non-small cell lung cancer (NSCLC) is the most common cause of cancer-related deaths worldwide and is primarily treated with radiation, surgery, and platinum-based drugs like cisplatin and carboplatin. The major challenge in the treatment of NSCLC patients is intrinsic or acquired resistance to chemotherapy. Molecular markers predicting the outcome of the patients are urgently needed., [Methods] Here, we employed patient-derived xenografts (PDXs) to detect predictive methylation biomarkers for platin-based therapies. We used MeDIP-Seq to generate genome-wide DNA methylation profiles of 22 PDXs, their parental primary NSCLC, and their corresponding normal tissues and complemented the data with gene expression analyses of the same tissues. Candidate biomarkers were validated with quantitative methylation-specific PCRs (qMSP) in an independent cohort., [Results] Comprehensive analyses revealed that differential methylation patterns are highly similar, enriched in PDXs and lung tumor-specific when comparing differences in methylation between PDXs versus primary NSCLC. We identified a set of 40 candidate regions with methylation correlated to carboplatin response and corresponding inverse gene expression pattern even before therapy. This analysis led to the identification of a promoter CpG island methylation of LDL receptor-related protein 12 (LRP12) associated with increased resistance to carboplatin. Validation in an independent patient cohort (n = 35) confirmed that LRP12 methylation status is predictive for therapeutic response of NSCLC patients to platin therapy with a sensitivity of 80% and a specificity of 84% (p, [Conclusions] Using an epigenome-wide sequencing approach, we find differential methylation patterns from primary lung cancer and PDX-derived cancers to be very similar, albeit with a lower degree of differential methylation in primary tumors. We identify LRP12 DNA methylation as a powerful predictive marker for carboplatin resistance. These findings outline a platform for the identification of epigenetic therapy resistance biomarkers based on PDX NSCLC models., This work was supported by the Volkswagenstiftung (Lichtenberg program to MRS), the German Federal Ministry of Education and Research (0316190A (EPITREAT), the Center for Molecular Medicine Cologne (CMMC), the state of North Rhine-Westphalia (EFRE-0800397), and the German Research Foundation (DFG KFO286).

Summary Mechanistic models are essential to deepen the understanding of complex diseases at the molecular level. Nowadays, high-throughput molecular and phenotypic characterizations are possible, but the integration of such data with prior knowledge on signaling pathways is limited by the availability of scalable computational methods. Here, we present a computational framework for the parameterization of large-scale mechanistic models and its application to the prediction of drug response of cancer cell lines from exome and transcriptome sequencing data. This framework is over 104 times faster than state-of-the-art methods, which enables modeling at previously infeasible scales. By applying the framework to a model describing major cancer-associated pathways (>1,200 species and >2,600 reactions), we could predict the effect of drug combinations from single drug data. This is the first integration of high-throughput datasets using large-scale mechanistic models. We anticipate this to be the starting point for development of more comprehensive models allowing a deeper mechanistic insight.

Every patient is different - his/her genomes, environment, disease history and exposure to drugs. Tumours, in particular, are often heterogeneous in their genetic make-up and their response to drugs, both within and between samples. Classic clinical trials basically ignore this complexity or, as in stratified medicine, attempt to reduce it to an analysis of a small number of still enormously heterogeneous patient groups. Medicine, however, is not the only area in which we are faced with such complex ‘n = 1' (every individual case is different) situations. The weather we experience today, characterised by tens of terabytes of measurement data, has never occurred before and will never occur again. Similar to the situation in medicine, we cannot predict the development of today's weather by looking for identical weather conditions in the past, and we cannot, in real life, test drugs for every individual patient in clinical trials with large numbers of biologically identical patient replicas. We can, however, do it with the help of models duplicating the ‘n = 1' situation on the computer, an approach which will also have to be used in both patient treatment and prevention as well as drug development in the future if we do not want to continue to make dangerous and expensive mistakes in real life.

Persistent activation of hedgehog (HH)/GLI signaling accounts for the development of basal cell carcinoma (BCC), a very frequent nonmelanoma skin cancer with rising incidence. Targeting HH/GLI signaling by approved pathway inhibitors can provide significant therapeutic benefit to BCC patients. However, limited response rates, development of drug resistance, and severe side effects of HH pathway inhibitors call for improved treatment strategies such as rational combination therapies simultaneously inhibiting HH/GLI and cooperative signals promoting the oncogenic activity of HH/GLI. In this study, we identified the interleukin‐6 (IL6) pathway as a novel synergistic signal promoting oncogenic HH/GLI via STAT3 activation. Mechanistically, we provide evidence that signal integration of IL6 and HH/GLI occurs at the level of cis‐regulatory sequences by co‐binding of GLI and STAT3 to common HH‐IL6 target gene promoters. Genetic inactivation of Il6 signaling in a mouse model of BCC significantly reduced in vivo tumor growth by interfering with HH/GLI‐driven BCC proliferation. Our genetic and pharmacologic data suggest that combinatorial HH‐IL6 pathway blockade is a promising approach to efficiently arrest cancer growth in BCC patients., What's new? Persistent activation of hedgehog (HH)/GLI signaling represents the main driver signal for the development of basal cell carcinoma (BCC), a common non‐melanoma skin cancer with rising incidence. Small molecule hedgehog pathway inhibitors are successfully used for the treatment of hedgehog‐driven BCC, but frequent drug resistance calls for improved strategies. Here, the authors identified the interleukin‐6 pathway as a novel synergistic signal promoting oncogenic HH/GLI via STAT3 activation. The synergistic interaction was required for the in vivo growth of hedgehog‐driven BCC. The study thus provides a rationale for effective combination treatments simultaneously targeting oncogenic hedgehog and interleukin‐6 signaling in BCC patients.

Sepsis, with its often devastating consequences for patients and their families, remains a major public health concern that poses an increasing financial burden. Early resuscitation together with the elucidation of the biological pathways and pathophysiological mechanisms with the use of "-omics" technologies have started changing the clinical and research landscape in sepsis. Metabolomics (i. e., the study of the metabolome), an "-omics" technology further down in the "-omics" cascade between the genome and the phenome, could be particularly fruitful in sepsis research with the potential to alter the clinical practice. Apart from its benefit for the individual patient, metabolomics has an impact on public health that extends beyond its applications in medicine. In this review, we present recent developments in metabolomics research in sepsis, with a focus on pneumonia, and we discuss the impact of metabolomics on public health, with a focus on free/libre open source software. (c) 2018 S. Karger AG, Basel

Every tumour is different. They arise in patients with different genomes, from cells with different epigenetic modifications, and by random processes affecting the genome and/or epigenome of a somatic cell, allowing it to escape the usual controls on its growth. Tumours and patients therefore often respond very differently to the drugs they receive. Cancer precision medicine aims to characterise the tumour (and often also the patient) to be able to predict, with high accuracy, its response to different treatments, with options ranging from the selective characterisation of a few genomic variants considered particularly important to predict the response of the tumour to specific drugs, to deep genome analysis of both tumour and patient, combined with deep transcriptome analysis of the tumour. Here, we compare the expected results of carrying out such analyses at different levels, from different size panels to a comprehensive analysis incorporating both patient and tumour at the DNA and RNA levels. In doing so, we illustrate the additional power gained by this unusually deep analysis strategy, a potential basis for a future precision medicine first strategy in cancer drug therapy. However, this is only a step along the way of increasingly detailed molecular characterisation, which in our view will, in the future, introduce additional molecular characterisation techniques, including systematic analysis of proteins and protein modification states and different types of metabolites in the tumour, systematic analysis of circulating tumour cells and nucleic acids, the use of spatially resolved analysis techniques to address the problem of tumour heterogeneity as well as the deep analyses of the immune system of the patient to, e.g., predict the response of the patient to different types of immunotherapy. Such analyses will generate data sets of even greater complexity, requiring mechanistic modelling approaches to capture enough of the complex situation in the real patient to be able to accurately predict his/her responses to all available therapies.

Epigenetic alterations, that is, disruption of DNA methylation and chromatin architecture, are now acknowledged as a universal feature of tumorigenesis. Medulloblastoma, a clinically challenging, malignant childhood brain tumour, is no exception. Despite much progress from recent genomics studies, with recurrent changes identified in each of the four distinct tumour subgroups (WNT-pathway-activated, SHH-pathway-activated, and the less-well-characterized Group 3 and Group 4), many cases still lack an obvious genetic driver. Here we present whole-genome bisulphite-sequencing data from thirty-four human and five murine tumours plus eight human and three murine normal controls, augmented with matched whole-genome, RNA and chromatin immunoprecipitation sequencing data. This comprehensive data set allowed us to decipher several features underlying the interplay between the genome, epigenome and transcriptome, and its effects on medulloblastoma pathophysiology. Most notable were highly prevalent regions of hypomethylation correlating with increased gene expression, extending tens of kilobases downstream of transcription start sites. Focal regions of low methylation linked to transcription-factor-binding sites shed light on differential transcriptional networks between subgroups, whereas increased methylation due to re-normalization of repressed chromatin in DNA methylation valleys was positively correlated with gene expression. Large, partially methylated domains affecting up to one-third of the genome showed increased mutation rates and gene silencing in a subgroup-specific fashion. Epigenetic alterations also affected novel medulloblastoma candidate genes (for example, LIN28B), resulting in alternative promoter usage and/or differential messenger RNA/microRNA expression. Analysis of mouse medulloblastoma and precursor-cell methylation demonstrated a somatic origin for many alterations. Our data provide insights into the epigenetic regulation of transcription and genome organization in medulloblastoma pathogenesis, which are probably also of importance in a wider developmental and disease context.

Bone morphogenetic proteins (BMPs) are known to induce diverse differentiation fates in human embryonic stem cells (hESCs). In the present study, we compared the potency at which BMP5, BMP10 and BMP13, which are members of distinct BMP subgroups due to differences in sequential and structural homology, induce differentiation in hESCs and human induced pluripotent stem cells (hiPSCs). We observed, in agreement with previous BMP4 model studies, that all ligands induced differentiation to the trophoblast lineage in the absence of bFGF. However, distinct BMPs exerted differences in the kinetics of induced differentiation, with BMP10 being the most potent. hiPSCs and hESCs shared comparable expression patterns of BMP type-I and -II receptor subtypes, which might explain conserved properties with respect to ligand potency and activation of SMAD-dependent (via SMAD1/5/8) and -independent (via MAPK p38) signal transduction pathways. The tested BMPs had distinct and also conserved target genes such as CDX2, DLX3, DLX5, GATA2, GATA3, HAND1, ID2, MSX2 and TFAP2A, known to be associated with the emergence of trophoblast cells. hESCs induced expression of the BMP antagonist NOGGIN as a protection mechanism to constrict extensive BMP action. Unlike BMP4, BMP10 has been shown to be resistant to NOGGIN-induced inhibition which in part might explain its potency. BMPs, in particular BMP4, are commonly used cytokines in differentiation protocols to generate diverse mesoderm- and endoderm-derivates from human pluripotent stem cells. Our study has identified BMP10, a cardiac-specific protein, as a superior alternative to BMP4 for inducing trophoblast differentiation in human pluripotent stem cells.

Colorectal cancer (CRC) is the third most common cause of cancer related death worldwide. Furthermore, with more than 1.2 million cases registered per year, it constitutes the third most frequent diagnosed cancer entity worldwide. Deregulation of protein synthesis has received considerable attention as a major step in cancer development and progression. Eukaryotic translation initiation factors (eIFs) are involved in the regulation of protein synthesis and are functionally linked to the phosphatidylinositol-3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) signaling pathway. The identification of factors accounting for colorectal carcinoma (CRC) development is a major gap in the field. Besides the importance of eIF3 subunits and the eIF4 complex, eIF1, eIF5 and eIF6 were found to be altered in primary and metastatic CRC. We observed significant difference in the expression profile between low and high grade CRC. eIF1, eIF5 and eIF6 are involved in translational control in CRC. Our findings also indicate a probable clinical impact when separating them into low and high grade colon and rectum carcinoma. eIF and mTOR expression were analysed on protein and mRNA level in primary low and high grade colon carcinoma (CC) and rectum carcinoma (RC) samples in comparison to non-neoplastic tissue without any disease-related pathology. To assess the therapeutic potential of targeting eIF1, eIF5 and eIF6 siRNA knockdown in HCT116 and HT29 cells was performed. We evaluated the eIF knockdown efficacy on protein and mRNA level and investigated proliferation, apoptosis, invasion, as well as colony forming and polysome associated fractions. These results indicate that eIFs, in particular eIF1, eIF5 and eIF6 play a major role in translational control in colon and rectum cancer.

Here, we describe an electrophoresis free assay for characterizing Tn5 transposomes fragmentation efficiency in a tagmentation reaction, in which double-stranded DNA is fragmented and tagged with adapter sequences. The assay uses plasmid DNA as a reference tagmentation substrate. Fragmentation efficiency is analyzed by comparative qPCR which measures the difference (ΔCt) in amplification of a specific plasmid region before and after tagmentation: more efficient fragmentation is characterized by a larger number of cleavage events within the amplified region, a delayed increase in the amplification curve and as a result, a larger ΔCt. Tagmentation reactions characterized with the same ΔCt exhibit the same fragment size distribution on an agarose gel. The ΔCt values measured can be used to quantitatively determine the relative performance of Tn5 transposome assemblies in optimization experiments and to standardize between batch variations in transposomes for use in next-generation sequencing library preparation. Moreover, the use of a reference tagmentation template added during next-generation sequencing library preparation enabled monitoring of the input DNA fragmentation. The presented qPCR-based assay is quick, contamination-safe, high-throughput and cost-efficient.

A combination of decades of cancer research and trillions of dollars has helped to exponentially increase our understanding of the molecular processes involved in cancer pathogenesis [1, 2] and to develop new cancer drugs. However, despite progress in diagnosing and treating cancer, these diseases remain one of the leading causes of morbidity and mortality worldwide, responsible for millions of deaths each year [3]. Moreover, we are still faced, on average, with low drug response rates, very serious treatment side effects and questionable survival benefits. In Europe alone, cancer kills around 4000 people every day [4] and costs billions per year [5]. Worldwide, the number of new cancer cases is increasing every year [4], at least partly due to rapidly ageing populations [6], with the number of new cancer cases projected to reach 25 million per year by 2030, and cure rates for many common forms of cancer stagnating [4]. Due to the high number of nonresponders to existing drugs and spiralling costs of new cancer drugs—costs have almost doubled in the last decade, resulting in a dramatic decrease in the number of new drugs—an individualised approach to the diagnosis and treatment of cancer patients is desperately required.

SummaryEarly-onset prostate cancer (EO-PCA) represents the earliest clinical manifestation of prostate cancer. To compare the genomic alteration landscapes of EO-PCA with “classical” (elderly-onset) PCA, we performed deep sequencing-based genomics analyses in 11 tumors diagnosed at young age, and pursued comparative assessments with seven elderly-onset PCA genomes. Remarkable age-related differences in structural rearrangement (SR) formation became evident, suggesting distinct disease pathomechanisms. Whereas EO-PCAs harbored a prevalence of balanced SRs, with a specific abundance of androgen-regulated ETS gene fusions including TMPRSS2:ERG, elderly-onset PCAs displayed primarily non-androgen-associated SRs. Data from a validation cohort of > 10,000 patients showed age-dependent androgen receptor levels and a prevalence of SRs affecting androgen-regulated genes, further substantiating the activity of a characteristic “androgen-type” pathomechanism in EO-PCA.

Colorectal cancer is one of the most common cancer types worldwide and accounts for approximately 600,000 deaths annually. Work over the last decades has uncovered a number of tumor-suppressor and oncogenes which are frequently mutated and might thus be responsible for the malignant transformation. However, only with the development of new high-throughput technologies systematic analyses of the genome and epigenomes became feasible. While data generation has increased exponential, we are now faced with new challenges to transform these data into useful models that help predicting the outcome of genomic aberrations and to develop novel diagnostic and therapeutic strategies. As a basis for the modeling it is essential to understand and integrate current knowledge. We review previous and current ideas in colorectal cancer development and focus on a pathway oriented view. We show that colorectal cancer is a multilayer complex disease affecting the genome as well as the epigenome with direct consequences on the gene and microRNA (miRNA) expression signatures. The goal is to illustrate the current principles of colorectal cancer pathogenesis and to illustrate the need for elaborate computer modeling systems. WIREs Syst Biol Med 2013, 5:205–219. doi: 10.1002/wsbm.1206 For further resources related to this article, please visit the WIREs website.

Background Human endogenous retroviruses (HERVs) constitute 8% of the human genome and contribute substantially to the transcriptome. HERVs have been shown to generate RNAs that modulate host gene expression. However, experimental evidence for an impact of these regulatory transcripts on the cellular phenotype has been lacking. Results We characterized the previously little described HERV-K(HML-10) endogenous retrovirus family on a genome-wide scale. HML-10 invaded the ancestral genome of Old World monkeys about 35 Million years ago and is enriched within introns of human genes when compared to other HERV families. We show that long terminal repeats (LTRs) of HML-10 exhibit variable promoter activity in human cancer cell lines. One identified HML-10 LTR-primed RNA was in opposite orientation to the pro-apoptotic Death-associated protein 3 (DAP3). In HeLa cells, experimental inactivation of HML-10 LTR-primed transcripts induced DAP3 expression levels, which led to apoptosis. Conclusions Its enrichment within introns suggests that HML-10 may have been evolutionary co-opted for gene regulation more than other HERV families. We demonstrated such a regulatory activity for an HML-10 RNA that suppressed DAP3-mediated apoptosis in HeLa cells. Since HML-10 RNA appears to be upregulated in various tumor cell lines and primary tumor samples, it may contribute to evasion of apoptosis in malignant cells. However, the overall weak expression of HML-10 transcripts described here raises the question whether our result described for HeLa represent a rare event in cancer. A possible function in other cells or tissues requires further investigation. Electronic supplementary material The online version of this article (doi:10.1186/s13100-016-0081-9) contains supplementary material, which is available to authorized users.

Personalised medicine promises prediction, prevention and treatment of illness that is targeted to individuals’ needs. New technologies for detailed biological profiling of individuals at the molecular level have been crucial in initiating the move to personalised medicine; further novel technologies will be necessary if the vision is to become a reality. We will need to develop new technologies to collect and analyse data in a way that is not just linear but integrated (understanding system level functioning) and dynamic (understanding system in flux). Key factors in the development of technologies for personalised medicine are standardisation, integration and harmonisation. For example, the tools and processes for data collection and analysis must be standardised across research sites. Research activity at different sites must be integrated to maximise synergies, and scientific research must be integrated with healthcare to ensure effective translation. There must also be harmonisation between scientific practices in different research sites, between science and healthcare and between science, healthcare and wider society, including the ethical and regulatory frameworks, the prevailing political and cultural ethos and the expectations of patients/citizens.

The blood–brain barrier (BBB) and the environment of the central nervous system (CNS) guard the nervous tissue from peripheral immune cells. In the autoimmune disease multiple sclerosis, myelin-reactive T-cell blasts are thought to transgress the BBB1, 2 and create a pro-inflammatory environment in the CNS, thereby making possible a second autoimmune attack that starts from the leptomeningeal vessels and progresses into the parenchyma3, 4, 5, 6. Using a Lewis rat model of experimental autoimmune encephalomyelitis, we show here that contrary to the expectations of this concept, T-cell blasts do not efficiently enter the CNS and are not required to prepare the BBB for immune-cell recruitment. Instead, intravenously transferred T-cell blasts gain the capacity to enter the CNS after residing transiently within the lung tissues. Inside the lung tissues, they move along and within the airways to bronchus-associated lymphoid tissues and lung-draining mediastinal lymph nodes before they enter the blood circulation from where they reach the CNS. Effector T cells transferred directly into the airways showed a similar migratory pattern and retained their full pathogenicity. On their way the T cells fundamentally reprogrammed their gene-expression profile, characterized by downregulation of their activation program and upregulation of cellular locomotion molecules together with chemokine and adhesion receptors. The adhesion receptors include ninjurin 1, which participates in T-cell intravascular crawling on cerebral blood vessels. We detected that the lung constitutes a niche not only for activated T cells but also for resting myelin-reactive memory T cells. After local stimulation in the lung, these cells strongly proliferate and, after assuming migratory properties, enter the CNS and induce paralytic disease. The lung could therefore contribute to the activation of potentially autoaggressive T cells and their transition to a migratory mode as a prerequisite to entering their target tissues and inducing autoimmune disease.

Delivery of reprogramming factor-encoding mRNAs by means of lipofection in somatic cells is a desirable method for deriving integration-free iPSCs. However, the lack of reproducibility implies there are major hurdles to overcome before this protocol becomes universally accepted. This study demonstrates the functionality of our in-house synthesized mRNAs expressing the reprogramming factors (OCT4, SOX2, KLF4, c-MYC) within the nucleus of human fibroblasts. However, upon repeated transfections, the mRNAs induced severe loss of cell viability as demonstrated by MTT cytotoxicity assays. Microarray-derived transcriptome data revealed that the poor cell survival was mainly due to the innate immune response triggered by the exogenous mRNAs. We validated the influence of mRNA transfection on key immune response-associated transcript levels, including IFNB1, RIG-I, PKR, IL12A, IRF7 and CCL5, by quantitative real-time PCR and directly compared these with the levels induced by other methods previously published to mediate reprogramming in somatic cells. Finally, we evaluated chemical compounds (B18R, chloroquine, TSA, Pepinh-TRIF, Pepinh-MYD), known for their ability to suppress cellular innate immune responses. However, none of these had the desired effect. The data presented here should provide the basis for further investigations into other immunosuppressing strategies that might facilitate efficient mRNA-mediated cellular reprogramming in human cells.

Sugar beet (Beta vulgaris) is an important crop plant that accounts for 30% of the world's sugar production annually. The genus Beta is a distant relative of currently sequenced taxa within the core eudicotyledons; the genomic characterization of sugar beet is essential to make its genome accessible to molecular dissection. Here, we present comprehensive genomic information in genetic and physical maps that cover all nine chromosomes. Based on this information we identified the proposed ancestral linkage groups of rosids and asterids within the sugar beet genome. We generated an extended genetic map that comprises 1127 single nucleotide polymorphism markers prepared from expressed sequence tags and bacterial artificial chromosome (BAC) end sequences. To construct a genome-wide physical map, we hybridized gene-derived oligomer probes against two BAC libraries with 9.5-fold cumulative coverage of the 758 Mbp genome. More than 2500 probes and clones were integrated both in genetic maps and the physical data. The final physical map encompasses 535 chromosomally anchored contigs that contains 8361 probes and 22 815 BAC clones. By using the gene order established with the physical map, we detected regions of synteny between sugar beet (order Caryophyllales) and rosid species that involves 1400-2700 genes in the sequenced genomes of Arabidopsis, poplar, grapevine, and cacao. The data suggest that Caryophyllales share the palaeohexaploid ancestor proposed for rosids and asterids. Taken together, we here provide extensive molecular resources for sugar beet and enable future high-resolution trait mapping, gene identification, and cross-referencing to regions sequenced in other plant species.

Inhibition of Hedgehog (HH)/GLI signalling in cancer is a promising therapeutic approach. Interactions between HH/GLI and other oncogenic pathways affect the strength and tumourigenicity of HH/GLI. Cooperation of HH/GLI with epidermal growth factor receptor (EGFR) signalling promotes transformation and cancer cell proliferation in vitro. However, the in vivo relevance of HH-EGFR signal integration and the critical downstream mediators are largely undefined. In this report we show that genetic and pharmacologic inhibition of EGFR signalling reduces tumour growth in mouse models of HH/GLI driven basal cell carcinoma (BCC). We describe HH-EGFR cooperation response genes including SOX2, SOX9, JUN, CXCR4 and FGF19 that are synergistically activated by HH-EGFR signal integration and required for in vivo growth of BCC cells and tumour-initiating pancreatic cancer cells. The data validate EGFR signalling as drug target in HH/GLI driven cancers and shed light on the molecular processes controlled by HH-EGFR signal cooperation, providing new therapeutic strategies based on combined targeting of HH-EGFR signalling and selected downstream target genes.

Cancer is a complex disease involving many different genomic and epigenomic changes, mutations, copy number changes, loss of heterozygosity etc. These differences cause tumors with the same pathological classification to respond very differently to the drugs, making therapy decisions difficult. As a result of intensive research in the field of oncology much information is available on molecular interactions and pathways involved in cancer onset and progression. In addition recent increases in the capacity of next-generation sequencing systems will provide huge amounts of genome, epigenome and transcriptome data, making it feasible to apply deep sequencing in the clinic to characterize tumor/patient samples. Both, the complexity of disturbances in interaction networks of biological processes in cancer and the new molecular information generated by this sequence analysis urgently require the development of systems that are able to derive clinically relevant predictions from all available data. The “Virtual Patient” modeling system combines general information available about cancer relevant pathways with the individual (genome, transcriptome) information available on the individual tumor/patient to generate models able to predict the effects and side effects of individual drugs or drug combinations. This opens the way to experiment with the response of the model of the individual patient to different therapy options in the computer, offering new routes to improve oncological practice, reduce health costs but also to accelerate the development and the approval process for new drugs in this area.

In this review, we discuss the latest targeted enrichment methods and aspects of their utilization along with second-generation sequencing for complex genome analysis. In doing so, we provide an overview of issues involved in detecting genetic variation, for which targeted enrichment has become a powerful tool. We explain how targeted enrichment for next-generation sequencing has made great progress in terms of methodology, ease of use and applicability, but emphasize the remaining challenges such as the lack of even coverage across targeted regions. Costs are also considered versus the alternative of whole-genome sequencing which is becoming ever more affordable. We conclude that targeted enrichment is likely to be the most economical option for many years to come in a range of settings.

BackgroundHost genetic characteristics and environmental factors may correlate with risk for cervical cancer development. Here we describe a retrospective screening study for single nucleotide polymorphisms (SNPs) in genetic markersTP53, MTHFR, CYP1A1,andCYP2E1in 749 patients.MethodsA multiplex ligation-dependent polymerase chain reaction approach was applied. We used archived material from human papillomavirus tests and correlated SNP genotypes to the corresponding clinical data. Semantic integration was used to identify and evaluate the clinical status from electronic health records.ResultsAn association with cervical cancer and high-grade dysplasia was found for the rare homozygous CC genotype (rs4646903) inCYP1A1(odds ratio [OR], 8.862). Odds ratios were also significantly elevated for heterozygousMTHFRCT genotype (rs1801133; OR, 1.457). No significant association was found inTP53(rs1042522) andCYP2E1(rs3813867). In addition, we found smokers at higher risk (OR, 2.688) and identified pregnancies as a significant risk factor (OR, 1.54).ConclusionsOur protocol enables a feasible way for further retrospective large sample size evaluation of potential genetic markers. This study revealed genetic associations of a rare SNP genotype with cervical dysplasia in one of the largest patient sample to date that warrants further investigation.