Jieming Chen

An integrated map of structural variation in 2,504 human genomes

Structural variants are implicated in numerous diseases and make up the majority of varying nucleotides among human genomes. Here we describe an integrated set of eight structural variant classes comprising both balanced and unbalanced variants, which we constructed using short-read DNA sequencing data and statistically phased onto haplotype blocks in 26 human populations. Analysing this set, we identify numerous gene-intersecting structural variants exhibiting population stratification and describe naturally occurring homozygous gene knockouts that suggest the dispensability of a variety of human genes. We demonstrate that structural variants are enriched on haplotypes identified by genome-wide association studies and exhibit enrichment for expression quantitative trait loci. Additionally, we uncover appreciable levels of structural variant complexity at different scales, including genic loci subject to clusters of repeated rearrangement and complex structural variants with multiple breakpoints likely to have formed through individual mutational events. Our catalogue will enhance future studies into structural variant demography, functional impact and disease association.

Molecular random access memory cell

Electronically programmable memory devices utilizing molecular self-assembled monolayers are reported. The devices exhibit electronically programmable and erasable memory bits compatible with conventional threshold levels and a memory cell applicable to a random access memory is demonstrated. Bit retention times >15 min have been observed.

Room-temperature negative differential resistance in nanoscale molecular junctions

Molecular devices are reported utilizing active self-assembled monolayers containing the nitroamine [2′-amino-4,4′-di(ethynylphenyl)-5′-nitro-1-benzenethiolate] or the nitro compound [4,4′-di(ethynylphenyl)-2′-nitro-1-benzenethiolate] as the active components. Both of these compounds have active redox centers. Current–voltage measurements of the devices exhibited negative differential resistance at room temperature and an on–off peak-to-valley ratio in excess of 1000:1 at low temperature.

Growth of a single freestanding multiwall carbon nanotube on each nanonickel dot

Patterned growth of freestanding carbon nanotube(s) on submicron nickel dot(s) on silicon has been achieved by plasma-enhanced-hot-filament-chemical-vapor deposition (PE-HF-CVD). A thin film nickel grid was fabricated on a silicon wafer by standard microlithographic techniques, and the PE-HF-CVD was done using acetylene (C2H2) gas as the carbon source and ammonia (NH3) as a catalyst and dilution gas. Well separated, single carbon nanotubes were observed to grow on the grid. The structures had rounded base diameters of approximately 150 nm, heights ranging from 0.1 to 5 μm, and sharp pointed tips. Transmission electron microscopy cross-sectional image clearly showed that the structures are indeed hollow nanotubes. The diameter and height depend on the nickel dot size and growth time, respectively. This nanotube growth process is compatible with silicon integrated circuit processing. Using this method, devices requiring freestanding vertical carbon nanotube(s) such as scanning probe microscopy, field emissi...

Integrative annotation of variants from 1092 humans: application to cancer genomics.

Introduction Plummeting sequencing costs have led to a great increase in the number of personal genomes. Interpreting the large number of variants in them, particularly in noncoding regions, is a current challenge. This is especially the case for somatic variants in cancer genomes, a large proportion of which are noncoding. Prioritization of candidate noncoding cancer drivers based on patterns of selection. (Step 1) Filter somatic variants to exclude 1000 Genomes polymorphisms; (2) retain variants in noncoding annotations; (3) retain those in “sensitive” regions; (4) prioritize those disrupting a transcription-factor binding motif and (5) residing near the center of a biological network; (6) prioritize ones in annotation blocks mutated in multiple cancer samples. Methods We investigated patterns of selection in DNA elements from the ENCODE project using the full spectrum of variants from 1092 individuals in the 1000 Genomes Project (Phase 1), including single-nucleotide variants (SNVs), short insertions and deletions (indels), and structural variants (SVs). Although we analyzed broad functional annotations, such as all transcription-factor binding sites, we focused more on highly specific categories such as distal binding sites of factor ZNF274. The greater statistical power of the Phase 1 data set compared with earlier ones allowed us to differentiate the selective constraints on these categories. We also used connectivity information between elements from protein-protein-interaction and regulatory networks. We integrated all the information on selection to develop a workflow (FunSeq) to prioritize personal-genome variants on the basis of their deleterious impact. As a proof of principle, we experimentally validated and characterized a few candidate variants. Results We identified a specific subgroup of noncoding categories with almost as much selective constraint as coding genes: “ultrasensitive” regions. We also uncovered a number of clear patterns of selection. Elements more consistently active across tissues and both maternal and paternal alleles (in terms of allele-specific activity) are under stronger selection. Variants disruptive because of mechanistic effects on transcription-factor binding (i.e. “motif-breakers”) are selected against. Higher network connectivity (i.e. for hubs) is associated with higher constraint. Additionally, many hub promoters and regulatory elements show evidence of recent positive selection. Overall, indels and SVs follow the same pattern as SNVs; however, there are notable exceptions. For instance, enhancers are enriched for SVs formed by nonallelic homologous recombination. We integrated these patterns of selection into the FunSeq prioritization workflow and applied it to cancer variants, because they present a strong contrast to inherited polymorphisms. In particular, application to ~90 cancer genomes (breast, prostate and medulloblastoma) reveals nearly a hundred candidate noncoding drivers. Discussion Our approach can be readily used to prioritize variants in cancer and is immediately applicable in a precision-medicine context. It can be further improved by incorporation of larger-scale population sequencing, better annotations, and expression data from large cohorts. Identifying Important Identifiers Each of us has millions of sequence variations in our genomes. Signatures of purifying or negative selection should help identify which of those variations is functionally important. Khurana et al. (1235587) used sequence polymorphisms from 1092 humans across 14 populations to identify patterns of selection, especially in noncoding regulatory regions. Noncoding regions under very strong negative selection included binding sites of some chromatin and general transcription factors (TFs) and core motifs of some important TF families. Positive selection in TF binding sites tended to occur in network hub promoters. Many recurrent somatic cancer variants occurred in noncoding regulatory regions and thus might indicate mutations that drive cancer. Regions under strong selection in the human genome identify noncoding regulatory elements with possible roles in disease. Interpreting variants, especially noncoding ones, in the increasing number of personal genomes is challenging. We used patterns of polymorphisms in functionally annotated regions in 1092 humans to identify deleterious variants; then we experimentally validated candidates. We analyzed both coding and noncoding regions, with the former corroborating the latter. We found regions particularly sensitive to mutations (“ultrasensitive”) and variants that are disruptive because of mechanistic effects on transcription-factor binding (that is, “motif-breakers”). We also found variants in regions with higher network centrality tend to be deleterious. Insertions and deletions followed a similar pattern to single-nucleotide variants, with some notable exceptions (e.g., certain deletions and enhancers). On the basis of these patterns, we developed a computational tool (FunSeq), whose application to ~90 cancer genomes reveals nearly a hundred candidate noncoding drivers.

Electronic transport of molecular systems

We report stable and reproducible switching and memory effects in self-assembled monolayers (SAMs). We demonstrate realization of negative differential resistance (NDR) and charge storage in electronic devices that utilize single redox-center-contained SAM as the active component; and compare the effects of various redox centers to switching and storage behavior. The devices exhibit electronically programmable and erasable memory bits with bit retention times greater than 15 min at room temperature.

Protein–protein interactions: General trends in the relationship between binding affinity and interfacial buried surface area

Protein–protein interactions play key roles in many cellular processes and their affinities and specificities are finely tuned to the functions they perform. Here, we present a study on the relationship between binding affinity and the size and chemical nature of protein–protein interfaces. Our analysis focuses on heterodimers and includes curated structural and thermodynamic data for 113 complexes. We observe a direct correlation between binding affinity and the amount of surface area buried at the interface. For a given amount of surface area buried, the binding affinity spans four orders of magnitude in terms of the dissociation constant (Kd). Across the entire dataset, we observe no obvious relationship between binding affinity and the chemical composition of the interface. We also calculate the free energy per unit surface area buried, or “surface energy density,” of each heterodimer. For interfacial surface areas between 500 and 2000 Å2, the surface energy density decreases as the buried surface area increases. As the buried surface area increases beyond about 2000 Å2, the surface energy density levels off to a constant value. We believe that these analyses and data will be useful for researchers with an interest in understanding, designing or inhibiting protein–protein interfaces.

Interpretation of Genomic Variants Using a Unified Biological Network Approach

The decreasing cost of sequencing is leading to a growing repertoire of personal genomes. However, we are lagging behind in understanding the functional consequences of the millions of variants obtained from sequencing. Global system-wide effects of variants in coding genes are particularly poorly understood. It is known that while variants in some genes can lead to diseases, complete disruption of other genes, called ‘loss-of-function tolerant’, is possible with no obvious effect. Here, we build a systems-based classifier to quantitatively estimate the global perturbation caused by deleterious mutations in each gene. We first survey the degree to which gene centrality in various individual networks and a unified ‘Multinet’ correlates with the tolerance to loss-of-function mutations and evolutionary conservation. We find that functionally significant and highly conserved genes tend to be more central in physical protein-protein and regulatory networks. However, this is not the case for metabolic pathways, where the highly central genes have more duplicated copies and are more tolerant to loss-of-function mutations. Integration of three-dimensional protein structures reveals that the correlation with centrality in the protein-protein interaction network is also seen in terms of the number of interaction interfaces used. Finally, combining all the network and evolutionary properties allows us to build a classifier distinguishing functionally essential and loss-of-function tolerant genes with higher accuracy (AUC = 0.91) than any individual property. Application of the classifier to the whole genome shows its strong potential for interpretation of variants involved in Mendelian diseases and in complex disorders probed by genome-wide association studies.

Growth and characterization of aligned carbon nanotubes from patterned nickel nanodots and uniform thin films

Microstructures of well-aligned multiwall carbon nanotubes grown on patterned nickel nanodots and uniform thin films by plasma-enhanced chemical vapor deposition have been studied by electron microscopy. It was found that growth of carbon nanotubes on patterned nickel nanodots and uniform thin films is different. During growth of carbon nanotubes, a nickel particle sits at the tip of each nanotube, and its [220] is preferentially oriented along the plasma direction, which can be explained by a channeling effect of ions coming into nickel particles in plasma. The alignment of nanotubes is induced by the electrical field direction relative to substrate surface.

Molecular wires, switches, and memories.

Abstract: Design and measurements of molecular wires, switches, and memories offer an increased device capability with reduced elements. We report: Measurements on through‐bond electronic transport properties of nanoscale metal‐1,4‐phenylene diisocyanide‐metal junctions are reported, where nonohmic thermionic emission is the dominant process, with isocyanide‐Pd showing the lowest thermionic barrier of 0.22 eV; robust and large reversible switching behavior in an electronic device that utilizes molecules containing redox centers as the active component, exhibiting negative differential resistance (NDR) and large on‐off peak‐to‐valley ratio (PVR) are realized; erasable storage of higher conductivity states in these redox‐center‐containing molecular devices are observed; and a two‐terminal electronically programmable and erasable molecular memory cell with long bit retention time is demonstrated.