Multi-omics data integration reveals novel drug targets in hepatocellular carcinoma.

BACKGROUND: Genetic aberrations in hepatocellular carcinoma (HCC) are well known, but the functional consequences of such aberrations remain poorly understood. RESULTS: Here, we explored the effect of defined genetic changes on the transcriptome, proteome and phosphoproteome in twelve tumors from an mTOR-driven hepatocellular carcinoma mouse model. Using Network-based Integration of multi-omiCS data (NetICS), we detected 74 'mediators' that relay via molecular interactions the effects of genetic and miRNA expression changes. The detected mediators account for the effects of oncogenic mTOR signaling on the transcriptome, proteome and phosphoproteome. We confirmed the dysregulation of the mediators YAP1, GRB2, SIRT1, HDAC4 and LIS1 in human HCC. CONCLUSIONS: This study suggests that targeting pathways such as YAP1 or GRB2 signaling and pathways regulating global histone acetylation could be beneficial in treating HCC with hyperactive mTOR signaling.

Network-based integration of multi-omics data for prioritizing cancer genes.

Motivation: Several molecular events are known to be cancer-related, including genomic aberrations, hypermethylation of gene promoter regions and differential expression of microRNAs. These aberration events are very heterogeneous across tumors and it is poorly understood how they affect the molecular makeup of the cell, including the transcriptome and proteome. Protein interaction networks can help decode the functional relationship between aberration events and changes in gene and protein expression. Results: We developed NetICS (Network-based Integration of Multi-omics Data), a new graph diffusion-based method for prioritizing cancer genes by integrating diverse molecular data types on a directed functional interaction network. NetICS prioritizes genes by their mediator effect, defined as the proximity of the gene to upstream aberration events and to downstream differentially expressed genes and proteins in an interaction network. Genes are prioritized for individual samples separately and integrated using a robust rank aggregation technique. NetICS provides a comprehensive computational framework that can aid in explaining the heterogeneity of aberration events by their functional convergence to common differentially expressed genes and proteins. We demonstrate NetICS' competitive performance in predicting known cancer genes and in generating robust gene lists using TCGA data from five cancer types. Availability and implementation: NetICS is available at https://github.com/cbg-ethz/netics. Supplementary information: Supplementary data are available at Bioinformatics online.

Scale-space measures for graph topology link protein network architecture to function.

MOTIVATION: The network architecture of physical protein interactions is an important determinant for the molecular functions that are carried out within each cell. To study this relation, the network architecture can be characterized by graph topological characteristics such as shortest paths and network hubs. These characteristics have an important shortcoming: they do not take into account that interactions occur across different scales. This is important because some cellular functions may involve a single direct protein interaction (small scale), whereas others require more and/or indirect interactions, such as protein complexes (medium scale) and interactions between large modules of proteins (large scale). RESULTS: In this work, we derive generalized scale-aware versions of known graph topological measures based on diffusion kernels. We apply these to characterize the topology of networks across all scales simultaneously, generating a so-called graph topological scale-space. The comprehensive physical interaction network in yeast is used to show that scale-space based measures consistently give superior performance when distinguishing protein functional categories and three major types of functional interactions-genetic interaction, co-expression and perturbation interactions. Moreover, we demonstrate that graph topological scale spaces capture biologically meaningful features that provide new insights into the link between function and protein network architecture. AVAILABILITY AND IMPLEMENTATION: Matlab(TM) code to calculate the scale-aware topological measures (STMs) is available at http://bioinformatics.tudelft.nl/TSSA

Thermal transport into graphene through nanoscopic contacts.

Superior thermal conductivity of graphene is frequently reported and used to justify its technical relevance for ultimately scaled devices. However, this extraordinary property is size dependent, and understanding of graphene's thermal properties in the quasiballistic thermal transport regime is lacking. To overcome this limitation, we directly probe local heat transfer into graphene by high-resolution scanning thermal microscopy on amorphous silicon oxide (SiO2) and crystalline silicon carbide (SiC). We quantify thickness-dependent thermal resistance modulations at sub-10-nm lateral resolution and thermal sensitivity for the individual atomic layers. On SiO2, we observe a decrease of thermal resistance with increasing number of graphene layers. We attribute this trend to the spreading of heat using the thickness dependence of graphene's thermal conductivity. On SiC, the heated tip-sample contact is scaled below the phonon mean free path of both the graphene and its supporting substrate. Consistently, we find the thermal interface resistances of the graphene top and bottom contacts dominating thermal transport.

State-of-the-art graphene high-frequency electronics.

High-performance graphene transistors for radio frequency applications have received much attention and significant progress has been achieved. However, devices based on large-area synthetic graphene, which have direct technological relevance, are still typically outperformed by those based on mechanically exfoliated graphene. Here, we report devices with intrinsic cutoff frequency above 300 GHz, based on both wafer-scale CVD grown graphene and epitaxial graphene on SiC, thus surpassing previous records on any graphene material. We also demonstrate devices with optimized architecture exhibiting voltage and power gains reaching 20 dB and a wafer-scale integrated graphene amplifier circuit with voltage amplification.

Dimensional approach to delusions in psychotic depression in the elderly: factor structure and clinical correlates.

OBJECTIVE: The present study attempted to investigate the clinically important broader dimensions of clinical characteristics of delusions, through multivariate analysis, in a pure sample of elderly unipolar delusional depressives as well as to test their external validity against a set of demographic, anamnestic and psychopathological validators. METHODS: Fifty inpatients suffering from psychotic major depression (PMD) in the context of major depressive disorder, 60 years old or older, were assessed on the basis of SCID IV, HRSD, MMSE and by three-point ordinal scales of 12 clinical, intrinsic or relational characteristics of delusions tested for their inter-rater reliability. RESULTS: Principal Component Analysis resulted in the extraction of five factors, jointly accounting for 69.7% of the total variance. The five factors were interpreted as representing the dimensions of delusional strength, acute upsetting, delusional organization, incomprehensibility and incitation to actions. Most of the factors were differentially associated with patients' demographic, anamnestic and clinical variables. CONCLUSION: Our results overlap in part with those of another similar study in delusional depressives of all age-ranges, differing, however, in respects possibly attributable to peculiarities of elderly depressives. Overall, the findings of the present study contribute to the further elucidation of major clinical dimensions of delusions in PMD in the elderly and the testing of their external validity.

Identification and Validation of a Biomarker Signature in Patients With Resectable Pancreatic Cancer via Genome-Wide Screening for Functional Genetic Variants.

Importance: Surgery currently offers the only chance for a cure in pancreatic ductal adenocarcinoma (PDAC), but it carries a significant morbidity and mortality risk and results in varying oncologic outcomes. At present, to our knowledge, there are no tests available before surgical resection to identify tumors with an aggressive biological phenotype that could guide personalized treatment strategies. Objective: Identification of noninvasive genetic biomarkers that could direct therapy in patients whose cases are amenable to pancreatic cancer resection. Design, Setting, and Participants: This multicenter study combined a prospective European cohort of patients with PDAC who underwent pancreatic resection (from University Hospital of Zurich, Zurich, Switzerland; Cantonal Hospital of Winterthur, Winterthur, Switzerland; and University Clinic of Ulm, Ulm, Germany) with data from the Cancer Genome Atlas database in the United States, which includes prospectively registered patients with PDAC. A genome-wide screening for functional single-nucleotide polymorphisms (SNPs) that affect PDAC survival was conducted using the European cohort for identification and the Cancer Genome Atlas cohort for validation. We used Cox proportional hazards models to screen for high-frequency polymorphic variants that are associated with allelic differences in tumor-associated survival and either result in an altered protein structure and function or reside in known regulatory noncoding genomic regions. The false-discovery rate method was applied for multiple hypothesis-testing corrections. Data analysis occurred from November 2017 to May 2018. Exposures: Pancreatic resection. Main Outcomes and Measures: Tumor-associated survival. Results: A total of 195 patients in the European cohort were included, as well as 136 patients in the Cancer Genome Atlas cohort (overall median [range] age, 66 [19-87] years; 156 [47.1%] were women, and 175 [52.9%] were men). Two SNPs in noncoding, functional regions of genes that regulate cancer progression, invasion, and metastasis were identified (CHI3L2 SNP rs684559 and CD44 SNP rs353630). These were associated with survival after PDAC resection; patients who carry the risk alleles at 1 of both SNP loci had a 2.63-fold increased risk for tumor-associated death compared with those with protective genotypes (hazard ratio for survival, 0.38 [95% CI, 0.27-0.53]; P = 1.0 × 10-8). Conclusions and Relevance: The identified polymorphisms may serve as a noninvasive biomarker signature of prospective survival after pancreatic resection that is readily available at the time of PDAC diagnosis. This signature can be used to identify a subset of high-risk patients with PDAC with very low survival probability who might be eligible for inclusion in clinical trials of new therapeutic strategies, including neoadjuvant chemotherapy protocols. In addition, the biological knowledge about these SNPs could help guide the development of individualized genomic strategies for PDAC therapies.

Computational approaches for the identification of cancer genes and pathways.

High-throughput DNA sequencing techniques enable large-scale measurement of somatic mutations in tumors. Cancer genomics research aims at identifying all cancer-related genes and solid interpretation of their contribution to cancer initiation and development. However, this venture is characterized by various challenges, such as the high number of neutral passenger mutations and the complexity of the biological networks affected by driver mutations. Based on biological pathway and network information, sophisticated computational methods have been developed to facilitate the detection of cancer driver mutations and pathways. They can be categorized into (1) methods using known pathways from public databases, (2) network-based methods, and (3) methods learning cancer pathways de novo. Methods in the first two categories use and integrate different types of data, such as biological pathways, protein interaction networks, and gene expression measurements. The third category consists of de novo methods that detect combinatorial patterns of somatic mutations across tumor samples, such as mutual exclusivity and co-occurrence. In this review, we discuss recent advances, current limitations, and future challenges of these approaches for detecting cancer genes and pathways. We also discuss the most important current resources of cancer-related genes. WIREs Syst Biol Med 2017, 9:e1364. doi: 10.1002/wsbm.1364 For further resources related to this article, please visit the WIREs website.

Predicting overlapping protein complexes from weighted protein interaction graphs by gradually expanding dense neighborhoods.

OBJECTIVE: Proteins are vital biological molecules driving many fundamental cellular processes. They rarely act alone, but form interacting groups called protein complexes. The study of protein complexes is a key goal in systems biology. Recently, large protein-protein interaction (PPI) datasets have been published and a plethora of computational methods that provide new ideas for the prediction of protein complexes have been implemented. However, most of the methods suffer from two major limitations: First, they do not account for proteins participating in multiple functions and second, they are unable to handle weighted PPI graphs. Moreover, the problem remains open as existing algorithms and tools are insufficient in terms of predictive metrics. METHOD: In the present paper, we propose gradually expanding neighborhoods with adjustment (GENA), a new algorithm that gradually expands neighborhoods in a graph starting from highly informative "seed" nodes. GENA considers proteins as multifunctional molecules allowing them to participate in more than one protein complex. In addition, GENA accepts weighted PPI graphs by using a weighted evaluation function for each cluster. RESULTS: In experiments with datasets from Saccharomyces cerevisiae and human, GENA outperformed Markov clustering, restricted neighborhood search and clustering with overlapping neighborhood expansion, three state-of-the-art methods for computationally predicting protein complexes. Seven PPI networks and seven evaluation datasets were used in total. GENA outperformed existing methods in 16 out of 18 experiments achieving an average improvement of 5.5% when the maximum matching ratio metric was used. Our method was able to discover functionally homogeneous protein clusters and uncover important network modules in a Parkinson expression dataset. When used on the human networks, around 47% of the detected clusters were enriched in gene ontology (GO) terms with depth higher than five in the GO hierarchy. CONCLUSIONS: In the present manuscript, we introduce a new method for the computational prediction of protein complexes by making the realistic assumption that proteins participate in multiple protein complexes and cellular functions. Our method can detect accurate and functionally homogeneous clusters.

A robust molecular probe for Ångstrom-scale analytics in liquids.

Traditionally, nanomaterial profiling using a single-molecule-terminated scanning probe is performed at the vacuum-solid interface often at a few Kelvin, but is not a notion immediately associated with liquid-solid interface at room temperature. Here, using a scanning tunnelling probe functionalized with a single C60 molecule stabilized in a high-density liquid, we resolve low-dimensional surface defects, atomic interfaces and capture Ångstrom-level bond-length variations in single-layer graphene and MoS2. Atom-by-atom controllable imaging contrast is demonstrated at room temperature and the electronic structure of the C60-metal probe complex within the encompassing liquid molecules is clarified using density functional theory. Our findings demonstrates that operating a robust single-molecular probe is not restricted to ultra-high vacuum and cryogenic settings. Hence the scope of high-precision analytics can be extended towards resolving sub-molecular features of organic elements and gauging ambient compatibility of emerging layered materials with atomic-scale sensitivity under experimentally less stringent conditions.

Graphene-based microfluidics for serial crystallography.

Microfluidic strategies to enable the growth and subsequent serial crystallographic analysis of micro-crystals have the potential to facilitate both structural characterization and dynamic structural studies of protein targets that have been resistant to single-crystal strategies. However, adapting microfluidic crystallization platforms for micro-crystallography requires a dramatic decrease in the overall device thickness. We report a robust strategy for the straightforward incorporation of single-layer graphene into ultra-thin microfluidic devices. This architecture allows for a total material thickness of only ∼1 µm, facilitating on-chip X-ray diffraction analysis while creating a sample environment that is stable against significant water loss over several weeks. We demonstrate excellent signal-to-noise in our X-ray diffraction measurements using a 1.5 µs polychromatic X-ray exposure, and validate our approach via on-chip structure determination using hen egg white lysozyme (HEWL) as a model system. Although this work is focused on the use of graphene for protein crystallography, we anticipate that this technology should find utility in a wide range of both X-ray and other lab on a chip applications.

Fast Production of High-Quality Graphene via Sequential Liquid Exfoliation.

A novel and practical approach of exfoliating graphite into graphene uses a sequence of flow and sonication on graphite suspensions. Graphite sediment after intense mixing is found to be altered, graphite having curled-up edges, which increases its sensitivity to ultrasound. Quadrupled graphene yield is achieved through introducing flow pretreatment.

Predicting protein complexes from weighted protein-protein interaction graphs with a novel unsupervised methodology: Evolutionary enhanced Markov clustering.

OBJECTIVE: Proteins are considered to be the most important individual components of biological systems and they combine to form physical protein complexes which are responsible for certain molecular functions. Despite the large availability of protein-protein interaction (PPI) information, not much information is available about protein complexes. Experimental methods are limited in terms of time, efficiency, cost and performance constraints. Existing computational methods have provided encouraging preliminary results, but they phase certain disadvantages as they require parameter tuning, some of them cannot handle weighted PPI data and others do not allow a protein to participate in more than one protein complex. In the present paper, we propose a new fully unsupervised methodology for predicting protein complexes from weighted PPI graphs. METHODS AND MATERIALS: The proposed methodology is called evolutionary enhanced Markov clustering (EE-MC) and it is a hybrid combination of an adaptive evolutionary algorithm and a state-of-the-art clustering algorithm named enhanced Markov clustering. EE-MC was compared with state-of-the-art methodologies when applied to datasets from the human and the yeast Saccharomyces cerevisiae organisms. RESULTS: Using public available datasets, EE-MC outperformed existing methodologies (in some datasets the separation metric was increased by 10-20%). Moreover, when applied to new human datasets its performance was encouraging in the prediction of protein complexes which consist of proteins with high functional similarity. In specific, 5737 protein complexes were predicted and 72.58% of them are enriched for at least one gene ontology (GO) function term. CONCLUSIONS: EE-MC is by design able to overcome intrinsic limitations of existing methodologies such as their inability to handle weighted PPI networks, their constraint to assign every protein in exactly one cluster and the difficulties they face concerning the parameter tuning. This fact was experimentally validated and moreover, new potentially true human protein complexes were suggested as candidates for further validation using experimental techniques.

Ectopic expression of Msx2 in mammalian myotubes recapitulates aspects of amphibian muscle dedifferentiation.

In contrast to urodele amphibians and teleost fish, mammals lack the regenerative responses to replace large body parts. Amphibian and fish regeneration uses dedifferentiation, i.e., reversal of differentiated state, as a means to produce progenitor cells to eventually replace damaged tissues. Therefore, induced activation of dedifferentiation responses in mammalian tissues holds an immense promise for regenerative medicine. Here we demonstrate that ectopic expression of Msx2 in cultured mouse myotubes recapitulates several aspects of amphibian muscle dedifferentiation. We found that MSX2, but not MSX1, leads to cellularization of myotubes and downregulates the expression of myotube markers, such as MHC, MRF4 and myogenin. RNA sequencing of myotubes ectopically expressing Msx2 showed downregulation of over 500 myotube-enriched transcripts and upregulation of over 300 myoblast-enriched transcripts. MSX2 selectively downregulated expression of Ptgs2 and Ptger4, two members of the prostaglandin pathway with important roles in myoblast fusion during muscle differentiation. Ectopic expression of Msx2, as well as Msx1, induced partial cell cycle re-entry of myotubes by upregulating CyclinD1 expression but failed to initiate S-phase. Finally, MSX2-induced dedifferentiation in mouse myotubes could be recapitulated by a pharmacological treatment with trichostatin A (TSA), bone morphogenetic protein 4 (BMP4) and fibroblast growth factor 1 (FGF1). Together, these observations indicate that MSX2 is a major driver of dedifferentiation in mammalian muscle cells.

Principle of direct van der Waals epitaxy of single-crystalline films on epitaxial graphene.

There are numerous studies on the growth of planar films on sp(2)-bonded two-dimensional (2D) layered materials. However, it has been challenging to grow single-crystalline films on 2D materials due to the extremely low surface energy. Recently, buffer-assisted growth of crystalline films on 2D layered materials has been introduced, but the crystalline quality is not comparable with the films grown on sp(3)-bonded three-dimensional materials. Here we demonstrate direct van der Waals epitaxy of high-quality single-crystalline GaN films on epitaxial graphene with low defectivity and surface roughness comparable with that grown on conventional SiC or sapphire substrates. The GaN film is released and transferred onto arbitrary substrates. The post-released graphene/SiC substrate is reused for multiple growth and transfer cycles of GaN films. We demonstrate fully functional blue light-emitting diodes (LEDs) by growing LED stacks on reused graphene/SiC substrates followed by transfer onto plastic tapes.

Reducing contact resistance in graphene devices through contact area patterning.

Performance of graphene electronics is limited by contact resistance associated with the metal-graphene (M-G) interface, where unique transport challenges arise as carriers are injected from a 3D metal into a 2D-graphene sheet. In this work, enhanced carrier injection is experimentally achieved in graphene devices by forming cuts in the graphene within the contact regions. These cuts are oriented normal to the channel and facilitate bonding between the contact metal and carbon atoms at the graphene cut edges, reproducibly maximizing "edge-contacted" injection. Despite the reduction in M-G contact area caused by these cuts, we find that a 32% reduction in contact resistance results in Cu-contacted, two-terminal devices, while a 22% reduction is achieved for top-gated graphene transistors with Pd contacts as compared to conventionally fabricated devices. The crucial role of contact annealing to facilitate this improvement is also elucidated. This simple approach provides a reliable and reproducible means of lowering contact resistance in graphene devices to bolster performance. Importantly, this enhancement requires no additional processing steps.

Layer-resolved graphene transfer via engineered strain layers.

The performance of optimized graphene devices is ultimately determined by the quality of the graphene itself. Graphene grown on copper foils is often wrinkled, and the orientation of the graphene cannot be controlled. Graphene grown on SiC(0001) via the decomposition of the surface has a single orientation, but its thickness cannot be easily limited to one layer. We describe a method in which a graphene film of one or two monolayers grown on SiC is exfoliated via the stress induced with a Ni film and transferred to another substrate. The excess graphene is selectively removed with a second exfoliation process with a Au film, resulting in a monolayer graphene film that is continuous and single-oriented.

Three-terminal graphene negative differential resistance devices.

A new mechanism for negative differential resistance (NDR) is discovered in three-terminal graphene devices based on a field-effect transistor configuration. This NDR effect is a universal phenomenon for graphene and is demonstrated in devices fabricated with different types of graphene materials and gate dielectrics. Operation of conventional NDR devices is usually based on quantum tunneling or intervalley carrier transfer, whereas the NDR behavior observed here is unique to the ambipolar behavior of zero-bandgap graphene and is associated with the competition between electron and hole conduction as the drain bias increases. These three terminal graphene NDR devices offer more operation flexibility than conventional two-terminal devices based on tunnel diodes, Gunn diodes, or molecular devices, and open up new opportunities for graphene in microwave to terahertz applications.

Epitaxial graphene nanoribbon array fabrication using BCP-assisted nanolithography.

A process for fabricating dense graphene nanoribbon arrays using self-assembled patterns of block copolymers on graphene grown epitaxially on SiC on the wafer scale has been developed. Etching masks comprising long and straight nanoribbon array structures with linewidths as narrow as 10 nm were fabricated, and the patterns were transferred to graphene. Our process combines both top-down and self-assembly steps to fabricate long graphene nanoribbon arrays with low defect counts. These are the narrowest nanoribbon arrays of epitaxial graphene on SiC fabricated to date.

Infrared spectroscopy of wafer-scale graphene.

We report spectroscopy results from the mid- to far-infrared on wafer-scale graphene, grown either epitaxially on silicon carbide or by chemical vapor deposition. The free carrier absorption (Drude peak) is simultaneously obtained with the universal optical conductivity (due to interband transitions) and the wavelength at which Pauli blocking occurs due to band filling. From these, the graphene layer number, doping level, sheet resistivity, carrier mobility, and scattering rate can be inferred. The mid-IR absorption of epitaxial two-layer graphene shows a less pronounced peak at 0.37 ± 0.02 eV compared to that in exfoliated bilayer graphene. In heavily chemically doped single-layer graphene, a record high transmission reduction due to free carriers approaching 40% at 250 µm (40 cm(-1)) is measured in this atomically thin material, supporting the great potential of graphene in far-infrared and terahertz optoelectronics.