High levels of donor-derived cell-free DNA in a case of graft-versus-host-disease following liver transplantation.

The diagnosis of graft-versus-host-disease (GVHD) after solid organ transplantation is made difficult by its variable clinical presentation and lack of sensitive and specific biomarkers to evaluate the immune state of transplant recipients. Emerging noninvasive diagnostic techniques like the quantification of donor-derived cell-free DNA (dd-cfDNA) for surveillance may improve the current standard-of-care. Herein, we report the use of this methodology in a patient with GVHD and corresponding levels of dd-cfDNA without any evidence of graft injury. Correlation of dd-cfDNA levels with the clinical course and its novel application here could lead to improvements in the rapid diagnosis of GVHD and in monitoring of response to treatment.

Detection and phasing of single base de novo mutations in biopsies from human in vitro fertilized embryos by advanced whole-genome sequencing.

Currently, the methods available for preimplantation genetic diagnosis (PGD) of in vitro fertilized (IVF) embryos do not detect de novo single-nucleotide and short indel mutations, which have been shown to cause a large fraction of genetic diseases. Detection of all these types of mutations requires whole-genome sequencing (WGS). In this study, advanced massively parallel WGS was performed on three 5- to 10-cell biopsies from two blastocyst-stage embryos. Both parents and paternal grandparents were also analyzed to allow for accurate measurements of false-positive and false-negative error rates. Overall, >95% of each genome was called. In the embryos, experimentally derived haplotypes and barcoded read data were used to detect and phase up to 82% of de novo single base mutations with a false-positive rate of about one error per Gb, resulting in fewer than 10 such errors per embryo. This represents a ∼ 100-fold lower error rate than previously published from 10 cells, and it is the first demonstration that advanced WGS can be used to accurately identify these de novo mutations in spite of the thousands of false-positive errors introduced by the extensive DNA amplification required for deep sequencing. Using haplotype information, we also demonstrate how small de novo deletions could be detected. These results suggest that phased WGS using barcoded DNA could be used in the future as part of the PGD process to maximize comprehensiveness in detecting disease-causing mutations and to reduce the incidence of genetic diseases.

Advanced Whole-Genome Sequencing and Analysis of Fetal Genomes from Amniotic Fluid.

BACKGROUND: Amniocentesis is a common procedure, the primary purpose of which is to collect cells from the fetus to allow testing for abnormal chromosomes, altered chromosomal copy number, or a small number of genes that have small single- to multibase defects. Here we demonstrate the feasibility of generating an accurate whole-genome sequence of a fetus from either the cellular or cell-free DNA (cfDNA) of an amniotic sample. METHODS: cfDNA and DNA isolated from the cell pellet of 31 amniocenteses were sequenced to approximately 50× genome coverage by use of the Complete Genomics nanoarray platform. In a subset of the samples, long fragment read libraries were generated from DNA isolated from cells and sequenced to approximately 100× genome coverage. RESULTS: Concordance of variant calls between the 2 DNA sources and with parental libraries was >96%. Two fetal genomes were found to harbor potentially detrimental variants in chromodomain helicase DNA binding protein 8 (CHD8) and LDL receptor-related protein 1 (LRP1), variations of which have been associated with autism spectrum disorder and keratosis pilaris atrophicans, respectively. We also discovered drug sensitivities and carrier information of fetuses for a variety of diseases. CONCLUSIONS: We were able to elucidate the complete genome sequence of 31 fetuses from amniotic fluid and demonstrate that the cfDNA or DNA from the cell pellet can be analyzed with little difference in quality. We believe that current technologies could analyze this material in a highly accurate and complete manner and that analyses like these should be considered for addition to current amniocentesis procedures.

Interpreting cancer genomes using systematic host network perturbations by tumour virus proteins.

Genotypic differences greatly influence susceptibility and resistance to disease. Understanding genotype-phenotype relationships requires that phenotypes be viewed as manifestations of network properties, rather than simply as the result of individual genomic variations. Genome sequencing efforts have identified numerous germline mutations, and large numbers of somatic genomic alterations, associated with a predisposition to cancer. However, it remains difficult to distinguish background, or 'passenger', cancer mutations from causal, or 'driver', mutations in these data sets. Human viruses intrinsically depend on their host cell during the course of infection and can elicit pathological phenotypes similar to those arising from mutations. Here we test the hypothesis that genomic variations and tumour viruses may cause cancer through related mechanisms, by systematically examining host interactome and transcriptome network perturbations caused by DNA tumour virus proteins. The resulting integrated viral perturbation data reflects rewiring of the host cell networks, and highlights pathways, such as Notch signalling and apoptosis, that go awry in cancer. We show that systematic analyses of host targets of viral proteins can identify cancer genes with a success rate on a par with their identification through functional genomics and large-scale cataloguing of tumour mutations. Together, these complementary approaches increase the specificity of cancer gene identification. Combining systems-level studies of pathogen-encoded gene products with genomic approaches will facilitate the prioritization of cancer-causing driver genes to advance the understanding of the genetic basis of human cancer.

A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma.

Recent advances in genetics have spurred rapid progress towards the systematic identification of genes involved in complex diseases. Still, the detailed understanding of the molecular and physiological mechanisms through which these genes affect disease phenotypes remains a major challenge. Here, we identify the asthma disease module, i.e. the local neighborhood of the interactome whose perturbation is associated with asthma, and validate it for functional and pathophysiological relevance, using both computational and experimental approaches. We find that the asthma disease module is enriched with modest GWAS P-values against the background of random variation, and with differentially expressed genes from normal and asthmatic fibroblast cells treated with an asthma-specific drug. The asthma module also contains immune response mechanisms that are shared with other immune-related disease modules. Further, using diverse omics (genomics, gene-expression, drug response) data, we identify the GAB1 signaling pathway as an important novel modulator in asthma. The wiring diagram of the uncovered asthma module suggests a relatively close link between GAB1 and glucocorticoids (GCs), which we experimentally validate, observing an increase in the level of GAB1 after GC treatment in BEAS-2B bronchial epithelial cells. The siRNA knockdown of GAB1 in the BEAS-2B cell line resulted in a decrease in the NFkB level, suggesting a novel regulatory path of the pro-inflammatory factor NFkB by GAB1 in asthma.

Network medicine: a network-based approach to human disease.

Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.

Global landscape of HIV-human protein complexes.

Human immunodeficiency virus (HIV) has a small genome and therefore relies heavily on the host cellular machinery to replicate. Identifying which host proteins and complexes come into physical contact with the viral proteins is crucial for a comprehensive understanding of how HIV rewires the host's cellular machinery during the course of infection. Here we report the use of affinity tagging and purification mass spectrometry to determine systematically the physical interactions of all 18 HIV-1 proteins and polyproteins with host proteins in two different human cell lines (HEK293 and Jurkat). Using a quantitative scoring system that we call MiST, we identified with high confidence 497 HIV-human protein-protein interactions involving 435 individual human proteins, with ∼40% of the interactions being identified in both cell types. We found that the host proteins hijacked by HIV, especially those found interacting in both cell types, are highly conserved across primates. We uncovered a number of host complexes targeted by viral proteins, including the finding that HIV protease cleaves eIF3d, a subunit of eukaryotic translation initiation factor 3. This host protein is one of eleven identified in this analysis that act to inhibit HIV replication. This data set facilitates a more comprehensive and detailed understanding of how the host machinery is manipulated during the course of HIV infection.

Network-based analysis of genome wide association data provides novel candidate genes for lipid and lipoprotein traits.

Genome wide association studies (GWAS) identify susceptibility loci for complex traits, but do not identify particular genes of interest. Integration of functional and network information may help in overcoming this limitation and identifying new susceptibility loci. Using GWAS and comorbidity data, we present a network-based approach to predict candidate genes for lipid and lipoprotein traits. We apply a prediction pipeline incorporating interactome, co-expression, and comorbidity data to Global Lipids Genetics Consortium (GLGC) GWAS for four traits of interest, identifying phenotypically coherent modules. These modules provide insights regarding gene involvement in complex phenotypes with multiple susceptibility alleles and low effect sizes. To experimentally test our predictions, we selected four candidate genes and genotyped representative SNPs in the Malmö Diet and Cancer Cardiovascular Cohort. We found significant associations with LDL-C and total-cholesterol levels for a synonymous SNP (rs234706) in the cystathionine beta-synthase (CBS) gene (p = 1 × 10(-5) and adjusted-p = 0.013, respectively). Further, liver samples taken from 206 patients revealed that patients with the minor allele of rs234706 had significant dysregulation of CBS (p = 0.04). Despite the known biological role of CBS in lipid metabolism, SNPs within the locus have not yet been identified in GWAS of lipoprotein traits. Thus, the GWAS-based Comorbidity Module (GCM) approach identifies candidate genes missed by GWAS studies, serving as a broadly applicable tool for the investigation of other complex disease phenotypes.

Nuances in the interpretation and utility of donor-derived cell-free DNA in lung transplantation following allogeneic hematopoietic stem cell transplantation - Case report.

Respiratory complications following allogeneic HSCT can lead to severe morbidity and mortality. Lung transplantation (LT) is a potential treatment for select patients with late-onset non-infectious pulmonary complications post-HSCT. Donor-derived cell-free DNA (dd-cfDNA) is a noninvasive biomarker for monitoring the health of allografts following LT. However, its utility in a multi-genome setting of LT after HSCT has not yet been clinically validated. Here we describe a case of a 75-year-old, male patient who underwent single-lung transplantation for BOS related to chronic GVHD and presented with persistently elevated dd-cfDNA levels. In a surveillance biopsy, the patient was diagnosed with mild acute cellular rejection at three months. The patient's lung function remained stable, and the reported dd-cfDNA levels decreased after the rejection episode but remained elevated above levels that would be considered quiescent for LT alone. In this unique setting, as 3 different genomes contributed to the dd-cfDNA% reported value, valuable insight was obtained by performing further analysis to separate the specific SNPs to identify the contribution of recipient, lung-donor, and HSCT-donor cfDNA. This study highlights the potential utility of dd-cfDNA in the multi-genome setting of lung transplant post-HSCT, nuances that need to be considered while interpreting the results, and its value in monitoring lung rejection.

MicroRNA-21 integrates pathogenic signaling to control pulmonary hypertension: results of a network bioinformatics approach.

BACKGROUND: Pulmonary hypertension (PH) is driven by diverse pathogenic etiologies. Owing to their pleiotropic actions, microRNA molecules are potential candidates for coordinated regulation of these disease stimuli. METHODS AND RESULTS: Using a network biology approach, we identify microRNA associated with multiple pathogenic pathways central to PH. Specifically, microRNA-21 (miR-21) is predicted as a PH-modifying microRNA, regulating targets integral to bone morphogenetic protein (BMP) and Rho/Rho-kinase signaling as well as functional pathways associated with hypoxia, inflammation, and genetic haploinsufficiency of BMP receptor type 2. To validate these predictions, we have found that hypoxia and BMP receptor type 2 signaling independently upregulate miR-21 in cultured pulmonary arterial endothelial cells. In a reciprocal feedback loop, miR-21 downregulates BMP receptor type 2 expression. Furthermore, miR-21 directly represses RhoB expression and Rho-kinase activity, inducing molecular changes consistent with decreased angiogenesis and vasodilation. In vivo, miR-21 is upregulated in pulmonary tissue from several rodent models of PH and in humans with PH. On induction of disease in miR-21-null mice, RhoB expression and Rho-kinase activity are increased, accompanied by exaggerated manifestations of PH. CONCLUSIONS: A network-based bioinformatic approach coupled with confirmatory in vivo data delineates a central regulatory role for miR-21 in PH. Furthermore, this study highlights the unique utility of network biology for identifying disease-modifying microRNA in PH.

Comparison of an expanded ataxia interactome with patient medical records reveals a relationship between macular degeneration and ataxia.

Spinocerebellar ataxias 6 and 7 (SCA6 and SCA7) are neurodegenerative disorders caused by expansion of CAG repeats encoding polyglutamine (polyQ) tracts in CACNA1A, the alpha1A subunit of the P/Q-type calcium channel, and ataxin-7 (ATXN7), a component of a chromatin-remodeling complex, respectively. We hypothesized that finding new protein partners for ATXN7 and CACNA1A would provide insight into the biology of their respective diseases and their relationship to other ataxia-causing proteins. We identified 118 protein interactions for CACNA1A and ATXN7 linking them to other ataxia-causing proteins and the ataxia network. To begin to understand the biological relevance of these protein interactions within the ataxia network, we used OMIM to identify diseases associated with the expanded ataxia network. We then used Medicare patient records to determine if any of these diseases co-occur with hereditary ataxia. We found that patients with ataxia are at 3.03-fold greater risk of these diseases than Medicare patients overall. One of the diseases comorbid with ataxia is macular degeneration (MD). The ataxia network is significantly (P= 7.37 × 10(-5)) enriched for proteins that interact with known MD-causing proteins, forming a MD subnetwork. We found that at least two of the proteins in the MD subnetwork have altered expression in the retina of Ataxin-7(266Q/+) mice suggesting an in vivo functional relationship with ATXN7. Together these data reveal novel protein interactions and suggest potential pathways that can contribute to the pathophysiology of ataxia, MD, and diseases comorbid with ataxia.

Viral perturbations of host networks reflect disease etiology.

Many human diseases, arising from mutations of disease susceptibility genes (genetic diseases), are also associated with viral infections (virally implicated diseases), either in a directly causal manner or by indirect associations. Here we examine whether viral perturbations of host interactome may underlie such virally implicated disease relationships. Using as models two different human viruses, Epstein-Barr virus (EBV) and human papillomavirus (HPV), we find that host targets of viral proteins reside in network proximity to products of disease susceptibility genes. Expression changes in virally implicated disease tissues and comorbidity patterns cluster significantly in the network vicinity of viral targets. The topological proximity found between cellular targets of viral proteins and disease genes was exploited to uncover a novel pathway linking HPV to Fanconi anemia.

Emergent criticality through adaptive information processing in boolean networks.

We study information processing in populations of boolean networks with evolving connectivity and systematically explore the interplay between the learning capability, robustness, the network topology, and the task complexity. We solve a long-standing open question and find computationally that, for large system sizes N, adaptive information processing drives the networks to a critical connectivity K(c)=2. For finite size networks, the connectivity approaches the critical value with a power law of the system size N. We show that network learning and generalization are optimized near criticality, given that the task complexity and the amount of information provided surpass threshold values. Both random and evolved networks exhibit maximal topological diversity near K(c). We hypothesize that this diversity supports efficient exploration and robustness of solutions. Also reflected in our observation is that the variance of the fitness values is maximal in critical network populations. Finally, we discuss implications of our results for determining the optimal topology of adaptive dynamical networks that solve computational tasks.

Purification and characterization of HIV-human protein complexes.

To fully understand how pathogens infect their host and hijack key biological processes, systematic mapping of intra-pathogenic and pathogen-host protein-protein interactions (PPIs) is crucial. Due to the relatively small size of viral genomes (usually around 10-100 proteins), generation of comprehensive host-virus PPI maps using different experimental platforms, including affinity tag purification-mass spectrometry (AP-MS) and yeast two-hybrid (Y2H) approaches, can be achieved. Global maps such as these provide unbiased insight into the molecular mechanisms of viral entry, replication and assembly. However, to date, only two-hybrid methodology has been used in a systematic fashion to characterize viral-host protein-protein interactions, although a deluge of data exists in databases that manually curate from the literature individual host-pathogen PPIs. We will summarize this work and also describe an AP-MS platform that can be used to characterize viral-human protein complexes and discuss its application for the HIV genome.

The art of community detection.

Networks in nature possess a remarkable amount of structure. Via a series of data-driven discoveries, the cutting edge of network science has recently progressed from positing that the random graphs of mathematical graph theory might accurately describe real networks to the current viewpoint that networks in nature are highly complex and structured entities. The identification of high order structures in networks unveils insights into their functional organization. Recently, Clauset, Moore, and Newman, introduced a new algorithm that identifies such heterogeneities in complex networks by utilizing the hierarchy that necessarily organizes the many levels of structure. Here, we anchor their algorithm in a general community detection framework and discuss the future of community detection.

Predicting synthetic rescues in metabolic networks.

An important goal of medical research is to develop methods to recover the loss of cellular function due to mutations and other defects. Many approaches based on gene therapy aim to repair the defective gene or to insert genes with compensatory function. Here, we propose an alternative, network-based strategy that aims to restore biological function by forcing the cell to either bypass the functions affected by the defective gene, or to compensate for the lost function. Focusing on the metabolism of single-cell organisms, we computationally study mutants that lack an essential enzyme, and thus are unable to grow or have a significantly reduced growth rate. We show that several of these mutants can be turned into viable organisms through additional gene deletions that restore their growth rate. In a rather counterintuitive fashion, this is achieved via additional damage to the metabolic network. Using flux balance-based approaches, we identify a number of synthetically viable gene pairs, in which the removal of one enzyme-encoding gene results in a non-viable phenotype, while the deletion of a second enzyme-encoding gene rescues the organism. The systematic network-based identification of compensatory rescue effects may open new avenues for genetic interventions.

Spontaneous reaction silencing in metabolic optimization.

Metabolic reactions of single-cell organisms are routinely observed to become dispensable or even incapable of carrying activity under certain circumstances. Yet, the mechanisms as well as the range of conditions and phenotypes associated with this behavior remain very poorly understood. Here we predict computationally and analytically that any organism evolving to maximize growth rate, ATP production, or any other linear function of metabolic fluxes tends to significantly reduce the number of active metabolic reactions compared to typical nonoptimal states. The reduced number appears to be constant across the microbial species studied and just slightly larger than the minimum number required for the organism to grow at all. We show that this massive spontaneous reaction silencing is triggered by the irreversibility of a large fraction of the metabolic reactions and propagates through the network as a cascade of inactivity. Our results help explain existing experimental data on intracellular flux measurements and the usage of latent pathways, shedding new light on microbial evolution, robustness, and versatility for the execution of specific biochemical tasks. In particular, the identification of optimal reaction activity provides rigorous ground for an intriguing knockout-based method recently proposed for the synthetic recovery of metabolic function.

Structure of fluctuations near mean-field critical points and spinodals and its implication for physical processes.

We analyze the structure of fluctuations near critical points and spinodals in mean-field and near-mean-field systems. Unlike systems that are non-mean-field, for which a fluctuation can be represented by a single cluster in a properly chosen percolation model, a fluctuation in mean-field and near-mean-field systems consists of a large number of clusters, which we term fundamental clusters. The structure of the latter and the way that they form fluctuations has important physical consequences for phenomena as diverse as nucleation in supercooled liquids, spinodal decomposition and continuous ordering, and the statistical distribution of earthquakes. The effects due to the fundamental clusters implies that they are physical objects and not only mathematical constructs.

Quantitative Whole Genome Sequencing of Circulating Tumor Cells Enables Personalized Combination Therapy of Metastatic Cancer.

Much effort has been dedicated to developing circulating tumor cells (CTC) as a noninvasive cancer biopsy, but with limited success as yet. In this study, we combine a method for isolation of highly pure CTCs using immunomagnetic enrichment/fluorescence-activated cell sorting with advanced whole genome sequencing (WGS), based on long fragment read technology, to illustrate the utility of an accurate, comprehensive, phased, and quantitative genomic analysis platform for CTCs. Whole genomes of 34 CTCs from a patient with metastatic breast cancer were analyzed as 3,072 barcoded subgenomic compartments of long DNA. WGS resulted in a read coverage of 23× per cell and an ensemble call rate of >95%. These barcoded reads enabled accurate detection of somatic mutations present in as few as 12% of CTCs. We found in CTCs a total of 2,766 somatic single-nucleotide variants and 543 indels and multi-base substitutions, 23 of which altered amino acid sequences. Another 16,961 somatic single nucleotide variant and 8,408 indels and multi-base substitutions, 77 of which were nonsynonymous, were detected with varying degrees of prevalence across the 34 CTCs. On the basis of our whole genome data of mutations found in all CTCs, we identified driver mutations and the tissue of origin of these cells, suggesting personalized combination therapies beyond the scope of most gene panels. Taken together, our results show how advanced WGS of CTCs can lead to high-resolution analyses of cancers that can reliably guide personalized therapy. Cancer Res; 77(16); 4530-41. ©2017 AACR.

Statistical mechanics of histories: a cluster Monte Carlo algorithm.

We present an efficient computational approach to sample the histories of nonlinear stochastic processes. This framework builds upon recent work on casting a d-dimensional stochastic dynamical system into a (d+1)-dimensional equilibrium system using the path-integral approach. We introduce a cluster algorithm that efficiently samples histories and discuss how to include measurements that are available into the estimate of the histories. This allows our approach to be applicable to the simulation of rare events and to optimal state and parameter estimation. We demonstrate the utility of this approach for Phi4 Langevin dynamics in two spatial dimensions where our algorithm improves sampling efficiency up to an order of magnitude.