Search

There is early evidence of extraocular systemic signals effecting function and morphology in neovascular age-related macular degeneration (nAMD). The prospective, cross-sectional BIOMAC study is an explorative investigation of peripheral blood proteome profiles and matched clinical features to uncover systemic determinacy in nAMD under anti-vascular endothelial growth factor intravitreal therapy (anti-VEGF IVT). It includes 46 nAMD patients stratified by the level of disease control under ongoing anti-VEGF treatment. Proteomic profiles in peripheral blood samples of every patient were detected with LC-MS/MS mass spectrometry. The patients underwent extensive clinical examination with a focus on macular function and morphology. In silico analysis includes unbiased dimensionality reduction and clustering, a subsequent annotation of clinical features, and non-linear models for recognition of underlying patterns. The model assessment was performed using leave-one-out cross validation. The findings provide an exploratory demonstration of the link between systemic proteomic signals and macular disease pattern using and validating non-linear classification models. Three main results were obtained: (1) Proteome-based clustering identifies two distinct patient subclusters with the smaller one (n = 10) exhibiting a strong signature for oxidative stress response. Matching the relevant meta-features on the individual patient's level identifies pulmonary dysfunction as an underlying health condition in these patients. (2) We identify biomarkers for nAMD disease features with Aldolase C as a putative factor associated with superior disease control under ongoing anti-VEGF treatment. (3) Apart from this, isolated protein markers are only weakly correlated with nAMD disease expression. In contrast, applying a non-linear classification model identifies complex molecular patterns hidden in a high number of proteomic dimensions determining macular disease expression. In conclusion, so far unconsidered systemic signals in the peripheral blood proteome contribute to the clinically observed phenotype of nAMD, which should be examined in future translational research on AMD. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background The use of tumour xenografts is a well-established research tool in cancer genomics but has not yet been comprehensively evaluated for cancer epigenomics. Methods In this study, we assessed the suitability of patient-derived tumour xenografts (PDXs) for methylome analysis using Infinium 450 K Beadchips and MeDIP-seq. Results Controlled for confounding host (mouse) sequences, comparison of primary PDXs and matching patient tumours in a rare (osteosarcoma) and common (colon) cancer revealed that an average 2.7% of the assayed CpG sites undergo major (Δβ ≥ 0.51) methylation changes in a cancer-specific manner as a result of the xenografting procedure. No significant subsequent methylation changes were observed after a second round of xenografting between primary and secondary PDXs. Based on computational simulation using publically available methylation data, we additionally show that future studies comparing two groups of PDXs should use 15 or more samples in each group to minimise the impact of xenografting-associated changes in methylation on comparison results. Conclusions Our results from rare and common cancers indicate that PDXs are a suitable discovery tool for cancer epigenomics and we provide guidance on how to overcome the observed limitations. [ABSTRACT FROM AUTHOR]

Metabolic pathways must have coevolved with the corresponding enzyme gene sequences. However, the evolutionary dynamics ensuing from the interplay between metabolic networks and genomes is still poorly understood. Here, we present a computational model that generates putative evolutionary walks on the metabolic network using a parallel evolution of metabolic reactions and their catalyzing enzymes. Starting from an initial set of compounds and enzymes, we expand the metabolic network iteratively by adding new enzymes with a probability that depends on their sequence-based similarity to already present enzymes. Thus, we obtain simulated time courses of chemical evolution in which we can monitor the appearance of new metabolites, enzyme sequences, or even entire organisms. We observe that new enzymes do not appear gradually but rather in clusters which correspond to enzyme classes. A comparison with Brownian motion dynamics indicates that our system displays biased random walks similar to diffusion on the metabolic network with long-range correlations. This suggests that a quantitative molecular principle may underlie the appearance of punctuated equilibrium dynamics, whereby enzymes occur in bursts rather than by phyletic gradualism. Moreover, the simulated time courses lead to a putative time-order of enzyme and organism appearance. Among the patterns we detect in these evolutionary trends is a significant correlation between the time of appearance and their enzyme repertoire size. Hence, our approach to metabolic evolution may help understand the rise in complexity at the biochemical and genomic levels. [ABSTRACT FROM AUTHOR]

A vital quest in biology is comprehensible visualization and interpretation of correlation relationships on a genome scale. Such relationships may be represented in the form of networks, which usually require disassembly into smaller manageable units, or clusters, to facilitate interpretation. Several graph-clustering algorithms that may be used to visualize biological networks are available. However, only some of these support weighted edges, and none provides good control of cluster sizes, which is crucial for comprehensible visualization of large networks. We constructed an interactive coexpression network for the Arabidopsis (Arabidopsis thaliana) genome using a novel Heuristic Cluster Chiseling Algorithm (HCCA) that supports weighted edges and that may control average cluster sizes. Comparative clustering analyses demonstrated that the HCCA performed as well as, or better than, the commonly used Markov, MCODE, and k-means clustering algorithms. We mapped MapMan ontology terms onto coexpressed node vicinities of the network, which revealed transcriptional organization of previously unrelated cellular processes. We further explored the predictive power of this network through mutant analyses and identified six new genes that are essential to plant growth. We show that the HCCA-partitioned network constitutes an ideal "cartographic" platform for visualization of correlation networks. This approach rapidly provides network partitions with relative uniform cluster sizes on a genome-scale level and may thus be used for correlation network layouts also for other species. [ABSTRACT FROM AUTHOR]

Simple Summary: This experimental preclinical study developed a strategy to identify signatures for the personalized treatment of colon cancer focusing on target-specific drug combinations. Tumor growth inhibition was analyzed in a preclinical phase II study using 25 patient-derived xenograft models (PDX) treated with drug combinations blocking alternatively activated oncogenic pathways. Results reveal an improved response by combinatorial treatment in some defined molecular subgroups and potential alternative treatment options in KRAS- and BRAF-mutated colon cancer. The current standard therapies for advanced, recurrent or metastatic colon cancer are the 5-fluorouracil and oxaliplatin or irinotecan schedules (FOxFI) +/− targeted drugs cetuximab or bevacizumab. Treatment with the FOxFI cytotoxic chemotherapy regimens causes significant toxicity and might induce secondary cancers. The overall low efficacy of the targeted drugs seen in colon cancer patients still is hindering the substitution of the chemotherapy. The ONCOTRACK project developed a strategy to identify predictive biomarkers based on a systems biology approach, using omics technologies to identify signatures for personalized treatment based on single drug response data. Here, we describe a follow-up project focusing on target-specific drug combinations. Background for this experimental preclinical study was that, by analyzing the tumor growth inhibition in the PDX models by cetuximab treatment, a broad heterogenic response from complete regression to tumor growth stimulation was observed. To provide confirmation of the hypothesis that drug combinations blocking alternatively activated oncogenic pathways may improve therapy outcomes, 25 models out of the well-characterized ONCOTRACK PDX panel were subjected to treatment with a drug combination scheme using four approved, targeted cancer drugs. [ABSTRACT FROM AUTHOR]

Surface-enhanced Raman spectroscopy (SERS) sensing of DNA bases by plasmonic nanopores could pave a way to novel methods for DNA analyses and new generation single-molecule sequencing platforms. The SERS discrimination of single DNA bases depends critically on the time that a DNA strand resides within the plasmonic hot spot. In fact, DNA molecules flow through the nanopores so rapidly that the SERS signals collected are not sufficient for single-molecule analysis. Here, we report an approach to control the residence time of molecules in the hot spot by an electro-plasmonic trapping effect. By directly adsorbing molecules onto a gold nanoparticle and then trapping the single nanoparticle in a plasmonic nanohole up to several minutes, we demonstrate single-molecule SERS detection of all four DNA bases as well as discrimination of single nucleobases in a single oligonucleotide. Our method can be extended easily to label-free sensing of single-molecule amino acids and proteins. Sensing DNA bases by surface-enhanced Raman spectroscopy (SERS) in plasmonic nanopores has suffered from rapid flow through of molecules. Here, the authors attach DNA molecules to gold nanoparticles which, due to electro-plasmonic trapping, allow for controlled residence times and discrimination of single nucleotides. [ABSTRACT FROM AUTHOR]

Targeted therapies for metastatic uveal melanoma have shown limited benefit in biomarker-unselected populations. The Treat20 Plus study prospectively evaluated the feasibility of a precision oncology strategy in routine clinical practice. Fresh biopsies were analyzed by high-throughput genomics (whole-genome, whole-exome, and RNA sequencing). A multidisciplinary molecular and immunologic tumor board (MiTB) made individualized treatment recommendations based on identified molecular aberrations, patient situation, drug, and clinical trial availability. Therapy selection was at the discretion of the treating physician. The primary endpoint was the feasibility of the precision oncology clinical program. Molecular analyses were available for 39/45 patients (87%). The MiTB provided treatment recommendations for 40/45 patients (89%), of whom 27/45 (60%) received ≥1 matched therapy. First-line matched therapies included MEK inhibitors (n = 15), MET inhibitors (n = 10), sorafenib (n = 1), and nivolumab (n = 1). The best response to first-line matched therapy was partial response in one patient (nivolumab based on tumor mutational burden), mixed response in two patients, and stable disease in 12 patients for a clinical benefit of 56%. The matched therapy population had a median progression-free survival and overall survival of 3.3 and 13.9 months, respectively. The growth modulation index with matched therapy was >1.33 in 6/17 patients (35%) with prior systemic therapy, suggesting clinical benefit. A precision oncology approach was feasible for patients with metastatic uveal melanoma, with 60% receiving a therapy matched to identify molecular aberrations. The clinical benefit after checkpoint inhibitors highlights the value of tumor mutational burden testing. • Demonstration of the feasibility of precision oncology for advanced uveal melanoma. • Complete whole-genome and RNA sequencing data were generated for 87% of patients. • Following treatment recommendations, 60% of patients received ≥1 matched therapy. • First-line therapies included inhibitors of MEK, MET, sorafenib, and nivolumab. • Clinical benefit rate in 56% of patients with one partial response under nivolumab. [ABSTRACT FROM AUTHOR]

Reliable and reproducible interpretation of molecular aberrations constitutes a bottleneck of precision medicine. Evidence-based decision management systems may improve rational therapy recommendations. To cope with an increasing amount of complex molecular data in the clinical care of patients with cancer, we established a workflow for the interpretation of molecular analyses. A specialized physician screened results from molecular analyses for potential biomarkers, irrespective of the diagnostic modality. Best available evidence was retrieved and categorized through establishment of an in-house database and interrogation of publicly available databases. Annotated biomarkers were ranked using predefined evidence levels and subsequently discussed at a molecular tumour board (MTB), which generated treatment recommendations. Subsequent translation into patient treatment and clinical outcomes were followed up. One hundred patients were discussed in the MTB between January 2016 and May 2017. Molecular data were obtained for 70 of 100 patients (50 whole exome/RNA sequencing, 18 panel sequencing, 2 immunohistochemistry (IHC)/microsatellite instability analysis). The MTB generated a median of two treatment recommendations each for 63 patients. Thirty-nine patients were treated: 6 partial responses and 12 stable diseases were achieved as best responses. Genetic counselling for germline events was recommended for seven patients. The development of an evidence-based workflow allowed for the clinical interpretation of complex molecular data and facilitated the translation of personalized treatment strategies into routine clinical care. The high number of treatment recommendations in patients with comprehensive genomic data and promising responses in patients treated with combination therapy warrant larger clinical studies. • Evidence-based, flexible workflow for the clinical interpretation of molecular data. • Application to complex data including whole-exome sequencing and RNA-Sequencing in clinical routine. • Initiation of targeted treatment in 39% of patients. • Promising responses in a subset of patients. • Standardisation of precision oncology workflows could help improve prospective trials. [ABSTRACT FROM AUTHOR]

MMP-9 is a predictor of atherosclerotic plaque instability and adverse cardiovascular events, but longitudinal data on the association between MMP9 and coronary disease progression are lacking. This study is aimed at investigating whether MMP9 is associated with atherosclerotic plaque progression and the related molecular basis in stable patients with chronic coronary syndrome (CCS). MMP9 serum levels were measured in 157 CCS patients (58 ± 8 years of age; 66% male) undergoing coronary computed tomography angiography at baseline and after a follow up period of 6.5 ± 1.1 years to assess progression of Total, Fibrous, Fibro-fatty, Necrotic Core, and Dense Calcium plaque volumes (PV). Gene expression analysis was evaluated in whole blood using a transcriptomic approach by RNA-seq. At multivariate analysis, serum MMP9 was associated with annual change of Total and Necrotic Core PV (Coefficient 3.205, SE 1.321, P = 0.017; 1.449, SE 0.690, P = 0.038, respectively), while MMP9 gene expression with Necrotic Core PV (Coefficient 70.559, SE 32.629, P = 0.034), independently from traditional cardiovascular risk factors, medications, and presence of obstructive CAD. After transcriptomic analysis, MMP9 expression was linked to expression of genes involved in the innate immunity. Among CCS patients, MMP9 is an independent predictive marker of progression of adverse coronary plaques, possibly reflecting the activity of inflammatory pathways conditioning adverse plaque phenotypes. Thus, blood MMP9 might be used for the identification of patients with residual risk even with optimal management of classical cardiovascular risk factors who may derive the greatest benefit from targeted anti-inflammatory drugs. [Display omitted] • Serum MMP9 was associated with adverse plaque features progression in CCS patients • MMP9 mRNA expression was linked to expression of genes involved in the innate immunity • MMP9 expression may reflect inflammatory pathways conditioning adverse plaque phenotypes • Blood MMP9 might be identify patients who may benefit from targeted anti-inflammatory drugs [ABSTRACT FROM AUTHOR]

Purpose: We aimed to determine the impact of artificial sweeteners (AS), especially saccharin, on the progression and treatment efficacy of patients with neovascular age-related macular degeneration (nAMD) under anti-vascular endothelial growth factor (anti-VEGF-A) treatment., Methods: In a cross-sectional study involving 46 patients with nAMD undergoing intravitreal anti-VEGF therapy, 6 AS metabolites were detected in peripheral blood using liquid chromatography - tandem mass spectrometry (LC-MS/MS). Disease features were statistically tested against these metabolite levels. Additionally, a murine choroidal neovascularization (CNV) model, induced by laser, was used to evaluate the effects of orally administered saccharin, assessing both imaging outcomes and gene expression patterns. Polymerase chain reaction (PCR) methods were used to evaluate functional expression of sweet taste receptors in a retinal pigment epithelium (RPE) cell line., Results: Saccharin levels in blood were significantly higher in patients with well-controlled CNV activity (P = 0.004) and those without subretinal hyper-reflective material (P = 0.015). In the murine model, saccharin-treated mice exhibited fewer leaking laser scars, lesser occurrence of bleeding, smaller fibrotic areas (P < 0.05), and a 40% decrease in mononuclear phagocyte accumulation (P = 0.06). Gene analysis indicated downregulation of inflammatory and VEGFR-1 response genes in the treated animals. Human RPE cells expressed taste receptor type 1 member 3 (TAS1R3) mRNA and reacted to saccharin stimulation with changes in mRNA expression., Conclusions: Saccharin appears to play a protective role in patients with nAMD undergoing intravitreal anti-VEGF treatment, aiding in better pathological lesion control and scar reduction. The murine study supports this observation, proposing saccharin's potential in mitigating pathological VEGFR-1-induced immune responses potentially via the RPE sensing saccharin in the blood stream.

Secondary infections contribute significantly to covid-19 mortality but driving factors remain poorly understood. Autopsies of 20 covid-19 cases and 14 controls from the first pandemic wave complemented with microbial cultivation and RNA-seq from lung tissues enabled description of major organ pathologies and specification of secondary infections. Lethal covid-19 segregated into two main death causes with either dominant diffuse alveolar damage (DAD) or secondary pneumonias. The lung microbiome in covid-19 showed a reduced biodiversity and increased prototypical bacterial and fungal pathogens in cases of secondary pneumonias. RNA-seq distinctly mirrored death causes and stratified DAD cases into subgroups with differing cellular compositions identifying myeloid cells, macrophages and complement C1q as strong separating factors suggesting a pathophysiological link. Together with a prominent induction of inhibitory immune-checkpoints our study highlights profound alterations of the lung immunity in covid-19 wherein a reduced antimicrobial defense likely drives development of secondary infections on top of SARS-CoV-2 infection., Competing Interests: H.L. is founder of Alacris Theranostics GmbH and M.S. and L.O. are employees of Alacris Theranostics GmbH. K. Z. is CEO and founder of Zatloukal Innovations GmbH. All other authors declare no conflicts of interest., (© 2022 The Authors.)

Chromophobe renal cell carcinoma (chRCC) accounts for approximately 5% of all renal cancers and around 30% of chRCC cases have mutations in TP53 . chRCC is poorly supported by microvessels and has markably lower glucose uptake than clear cell RCC and papillary RCC. Currently, the metabolic status and mechanisms by which this tumor adapts to nutrient-poor microenvironments remain to be investigated. In this study, we performed proteome and metabolome profiling of chRCC tumors and adjacent kidney tissues and identified major metabolic alterations in chRCC tumors, including the classical Warburg effect, the downregulation of gluconeogenesis and amino acid metabolism, and the upregulation of protein degradation and endocytosis. chRCC cells depended on extracellular macromolecules as an amino acid source by activating endocytosis to sustain cell proliferation and survival. Inhibition of the phospholipase C gamma 2 (PLCG2)/inositol 1,4,5-trisphosphate (IP3)/Ca 2+ /protein kinase C (PKC) pathway significantly impaired the activation of endocytosis for amino acid uptakes into chRCC cells. In chRCC, whole-exome sequencing revealed that TP53 mutations were not related to expression of PLCG2 and activation of endocytosis. Our study provides novel perspectives on metabolic rewiring in chRCC and identifies the PLCG2/IP3/Ca 2+ /PKC axis as a potential therapeutic target in patients with chRCC. SIGNIFICANCE: This study reveals macropinocytosis as an important process utilized by chRCC to gain extracellular nutrients in a p53-independent manner., (©2020 American Association for Cancer Research.)

Most relapses of acute lymphoblastic leukemia (ALL) occur in patients with a medium risk (MR) for relapse on the Associazione Italiana di Ematologia e Oncologia Pediatrica and Berlin-Frankfurt-Münster (AIEOP-BFM) ALL protocol, based on persistence of minimal residual disease (MRD). New insights into biological features that are associated with MRD are needed. Here, we identify the glycosylphosphatidylinositol-anchored cell surface protein vanin-2 (VNN2; GPI-80) by charting the cell surface proteome of MRD very high-risk (HR) B-cell precursor (BCP) ALL using a chemoproteomics strategy. The correlation between VNN2 transcript and surface protein expression enabled a retrospective analysis (ALL-BFM 2000; N = 770 cases) using quantitative polymerase chain reaction to confirm the association of VNN2 with MRD and independent prediction of worse outcome. Using flow cytometry, we detected VNN2 expression in 2 waves, in human adult bone marrow stem and progenitor cells and in the mature myeloid compartment, in line with proposed roles for fetal hematopoietic stem cells and inflammation. Prospective validation by flow cytometry in the ongoing clinical trial (AIEOP-BFM 2009) identified 10% (103/1069) of VNN2+ BCP ALL patients at first diagnosis, primarily in the MRD MR (48/103, 47%) and HR (37/103, 36%) groups, across various cytogenetic subtypes. We also detected frequent mutations in epigenetic regulators in VNN2+ ALLs, including histone H3 methyltransferases MLL2, SETD2, and EZH2 and demethylase KDM6A. Inactivation of the VNN2 gene did not impair leukemia repopulation capacity in xenografts. Taken together, VNN2 marks a cellular state of increased resistance to chemotherapy that warrants further investigations. Therefore, this marker should be included in diagnostic flow cytometry panels., (© 2020 by The American Society of Hematology.)

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 10 4 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., (Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.)

Hypofertility is a risk factor for the development of testicular germ cell tumors (TGCT), but the initiating event linking these pathologies is unknown. We hypothesized that excessive planar division of undifferentiated germ cells promotes their self-renewal and TGCT development. However, our results obtained from mouse models and seminoma patients demonstrated the opposite. Defective planar divisions of undifferentiated germ cells caused their premature exit from the seminiferous tubule niche, resulting in germ cell depletion, hypofertility, intratubular germ cell neoplasias, and seminoma development. Oriented divisions of germ cells, which determine their fate, were regulated by spindle-associated RHAMM-a function we found to be abolished in 96% of human seminomas. Mechanistically, RHAMM expression is regulated by the testis-specific polyadenylation protein CFIm25, which is downregulated in the human seminomas. These results suggested that spindle misorientation is oncogenic, not by promoting self-renewing germ cell divisions within the niche, but by prematurely displacing proliferating cells from their normal epithelial milieu. Furthermore, they suggested RHAMM loss-of-function and spindle misorientation as an initiating event underlying both hypofertility and TGCT initiation. These findings identify spindle-associated RHAMM as an intrinsic regulator of male germ cell fate and as a gatekeeper preventing initiation of TGCTs. Cancer Res; 76(21); 6382-95. ©2016 AACR., (©2016 American Association for Cancer Research.)

The set of chemicals producible and usable by metabolic pathways must have evolved in parallel with the enzymes that catalyze them. One implication of this common historical path should be a correspondence between the innovation steps that gradually added new metabolic reactions to the biosphere-level biochemical toolkit, and the gradual sequence changes that must have slowly shaped the corresponding enzyme structures. However, global signatures of a long-term co-evolution have not been identified. Here we search for such signatures by computing correlations between inter-reaction distances on a metabolic network, and sequence distances of the corresponding enzyme proteins. We perform our calculations using the set of all known metabolic reactions, available from the KEGG database. Reaction-reaction distance on the metabolic network is computed as the length of the shortest path on a projection of the metabolic network, in which nodes are reactions and edges indicate whether two reactions share a common metabolite, after removal of cofactors. Estimating the distance between enzyme sequences in a meaningful way requires some special care: for each enzyme commission (EC) number, we select from KEGG a consensus set of protein sequences using the cluster of orthologous groups of proteins (COG) database. We define the evolutionary distance between protein sequences as an asymmetric transition probability between two enzymes, derived from the corresponding pair-wise BLAST scores. By comparing the distances between sequences to the minimal distances on the metabolic reaction graph, we find a small but statistically significant correlation between the two measures. This suggests that the evolutionary walk in enzyme sequence space has locally mirrored, to some extent, the gradual expansion of metabolism.

Coexpressed genes are tentatively translated into proteins that are involved in similar biological functions. Here, we constructed gene coexpression networks from collected microarray data of the organisms Arabidopsis thaliana, Saccharomyces cerevisiae, and Escherichia coli. Their degree distributions show the common property of an overrepresentation of highly connected nodes followed by a sudden truncation. In order to analyze this behavior, we present an evolutionary model simulating the genetic evolution. This model assumes that new genes emerge by duplication from a small initial set of primordial genes. Our model does not include the removal of unused genes but selective pressure is indirectly taken into account by preferentially duplicating the old genes. Thus, gene duplication represents the emergence of a new gene and its successful establishment. After a duplication event, all genes are slightly but iteratively mutated, thus altering their expression patterns. Our model is capable of reproducing global properties of the investigated coexpression networks. We show that our model reflects the mean inter-node distances and especially the characteristic humps in the degree distribution that, in the biological examples, result from functionally related genes.