Data integration and inference of gene regulation using single-cell temporal multimodal data with scTIE

Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mechanisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes. scTIE uses an autoencoder to embed cells from all time points into a common space by using iterative optimal transport, followed by extracting interpretable information to predict cell trajectories. Using a variety of synthetic and real temporal multimodal data sets, we show scTIE achieves effective data integration while preserving more biological signals than existing methods, particularly in the presence of batch effects and noise. Furthermore, on the exemplar multiome data set we generated from differentiating mouse embryonic stem cells over time, we show scTIE captures regulatory elements highly predictive of cell transition probabilities, providing new potentials to understand the regulatory landscape driving developmental processes.

Deterministic evolution and stringent selection during preneoplasia

The earliest events during human tumour initiation, although poorly characterized, may hold clues to malignancy detection and prevention1. Here we model occult preneoplasia by biallelic inactivation of TP53, a common early event in gastric cancer, in human gastric organoids. Causal relationships between this initiating genetic lesion and resulting phenotypes were established using experimental evolution in multiple clonally derived cultures over 2 years. TP53 loss elicited progressive aneuploidy, including copy number alterations and structural variants prevalent in gastric cancers, with evident preferred orders. Longitudinal single-cell sequencing of TP53-deficient gastric organoids similarly indicates progression towards malignant transcriptional programmes. Moreover, high-throughput lineage tracing with expressed cellular barcodes demonstrates reproducible dynamics whereby initially rare subclones with shared transcriptional programmes repeatedly attain clonal dominance. This powerful platform for experimental evolution exposes stringent selection, clonal interference and a marked degree of phenotypic convergence in premalignant epithelial organoids. These data imply predictability in the earliest stages of tumorigenesis and show evolutionary constraints and barriers to malignant transformation, with implications for earlier detection and interception of aggressive, genome-instable tumours.

scTIE: data integration and inference of gene regulation using single-cell temporal multimodal data

Single-cell technologies offer unprecedented opportunities to dissect gene regulatory mechanisms in context-specific ways. Although there are computational methods for extracting gene regulatory relationships from scRNA-seq and scATAC-seq data, the data integration problem, essential for accurate cell type identification, has been mostly treated as a standalone challenge. Here we present scTIE, a unified method that integrates temporal multimodal data and infers regulatory relationships predictive of cellular state changes. scTIE uses an autoencoder to embed cells from all time points into a common space using iterative optimal transport, followed by extracting interpretable information to predict cell trajectories. Using a variety of synthetic and real temporal multimodal datasets, we demonstrate scTIE achieves effective data integration while preserving more biological signals than existing methods, particularly in the presence of batch effects and noise. Furthermore, on the exemplar multiome dataset we generated from differentiating mouse embryonic stem cells over time, we demonstrate scTIE captures regulatory elements highly predictive of cell transition probabilities, providing new potentials to understand the regulatory landscape driving developmental processes.

The origins and functional effects of postzygotic mutations throughout the human life span

Postzygotic mutations (PZMs) begin to accrue in the human genome immediately after fertilization, but how and when PZMs affect development and lifetime health remain unclear. To study the origins and functional consequences of PZMs, we generated a multitissue atlas of PZMs spanning 54 tissue and cell types from 948 donors. Nearly half the variation in mutation burden among tissue samples can be explained by measured technical and biological effects, and 9% can be attributed to donor-specific effects. Through phylogenetic reconstruction of PZMs, we found that their type and predicted functional impact vary during prenatal development, across tissues, and through the germ cell life cycle. Thus, methods for interpreting effects across the body and the life span are needed to fully understand the consequences of genetic variants.

NeuronMotif: Deciphering cis-regulatory codes by layer-wise demixing of deep neural networks

Discovering DNA regulatory sequence motifs and their relative positions is vital to understanding the mechanisms of gene expression regulation. Although deep convolutional neural networks (CNNs) have achieved great success in predicting cis-regulatory elements, the discovery of motifs and their combinatorial patterns from these CNN models has remained difficult. We show that the main difficulty is due to the problem of multifaceted neurons which respond to multiple types of sequence patterns. Since existing interpretation methods were mainly designed to visualize the class of sequences that can activate the neuron, the resulting visualization will correspond to a mixture of patterns. Such a mixture is usually difficult to interpret without resolving the mixed patterns. We propose the NeuronMotif algorithm to interpret such neurons. Given any convolutional neuron (CN) in the network, NeuronMotif first generates a large sample of sequences capable of activating the CN, which typically consists of a mixture of patterns. Then, the sequences are "demixed" in a layer-wise manner by backward clustering of the feature maps of the involved convolutional layers. NeuronMotif can output the sequence motifs, and the syntax rules governing their combinations are depicted by position weight matrices organized in tree structures. Compared to existing methods, the motifs found by NeuronMotif have more matches to known motifs in the JASPAR database. The higher-order patterns uncovered for deep CNs are supported by the literature and ATAC-seq footprinting. Overall, NeuronMotif enables the deciphering of cis-regulatory codes from deep CNs and enhances the utility of CNN in genome interpretation.

Effects of primary granulocyte-colony stimulating factor prophylaxis on neutropenic toxicity and chemotherapy dose delivery in Chinese patients with breast cancer who received adjuvant docetaxel plus cyclophosphamide chemotherapy: a retrospective cohort study

INTRODUCTION

This study was performed to examine the effects of primary granulocyte-colony stimulating factor (G-CSF) prophylaxis on neutropenic toxicity, chemotherapy delivery, and hospitalisation among Chinese patients with breast cancer in Hong Kong.

METHODS

This retrospective study included patients with breast cancer who received adjuvant docetaxel plus cyclophosphamide chemotherapy from November 2007 to October 2013 at Princess Margaret Hospital. Data were collected regarding the usage of G-CSF prophylaxis; incidences of grade 3 or 4 neutropenia, febrile neutropenia, non-neutropenic fever, and infection; hospital admissions, and chemotherapy dose delivery. Patients who began to receive G-CSF prophylaxis during the first cycle of chemotherapy and continued such prophylaxis in subsequent cycles were regarded as the primary G-CSF prophylaxis group.

RESULTS

In total, 231 female Chinese patients with breast cancer were included in the analysis. Overall, 193 (83.5%) patients received primary G-CSF prophylaxis. The demographics and tumour characteristics were comparable between patients with and without primary G-CSF prophylaxis. Primary G-CSF prophylaxis significantly reduced febrile neutropenia incidence from 31.6% to 14.5% (relative risk=0.45, 95% confidence interval=0.25-0.81). Primary G-CSF prophylaxis also significantly reduced the incidence of grade 3 or 4 neutropenia from 57.9% to 24.7% (relative risk=0.43, 95% confidence interval=0.30-0.62) and the incidence of febrile neutropenia-related hospital admission from 31.6% to 12.4% (P=0.025). Finally, it enabled more patients to receive adequate chemotherapy dose delivery.

CONCLUSION

Primary G-CSF prophylaxis effectively reduced the incidences of grade 3 or 4 neutropenia and febrile neutropenia, while enabling adequate chemotherapy dose delivery and reducing hospital admissions among Chinese patients with breast cancer who received adjuvant docetaxel plus cyclophosphamide chemotherapy.

A scintillator attenuation spectrometer for intense gamma-rays

A new type of compact high-resolution high-sensitivity gamma-ray spectrometer for short-pulse intense gamma-rays (250 keV to 50 MeV) has been developed by combining the principles of scintillators and attenuation spectrometers. The first prototype of this scintillator attenuation spectrometer (SAS) was tested successfully in Trident laser experiments at LANL. Later versions have been used extensively in the Texas Petawatt laser experiments in Austin, TX, and more recently in OMEGA-EP laser experiments at LLE, Rochester, NY. The SAS is particularly useful for high-repetition-rate laser applications. Here, we give a concise description of the design principles, capabilities, and sample preliminary results of the SAS.

On the identifiability of the isoform deconvolution problem: application to select the proper fragment length in an RNA-seq library

MOTIVATION

Isoform deconvolution is an NP-hard problem. The accuracy of the proposed solutions is far from perfect. At present, it is not known if gene structure and isoform concentration can be uniquely inferred given paired-end reads, and there is no objective method to select the fragment length to improve the number of identifiable genes. Different pieces of evidence suggest that the optimal fragment length is gene-dependent, stressing the need for a method that selects the fragment length according to a reasonable trade-off across all the genes in the whole genome.

RESULTS

A gene is considered to be identifiable if it is possible to get both the structure and concentration of its transcripts univocally. Here, we present a method to state the identifiability of this deconvolution problem. Assuming a given transcriptome and that the coverage is sufficient to interrogate all junction reads of the transcripts, this method states whether or not a gene is identifiable given the read length and fragment length distribution. Applying this method using different read and fragment length combinations, the optimal average fragment length for the human transcriptome is around 400-600 nt for coding genes and 150-200 nt for long non-coding RNAs. The optimal read length is the largest one that fits in the fragment length. It is also discussed the potential profit of combining several libraries to reconstruct the transcriptome. Combining two libraries of very different fragment lengths results in a significant improvement in gene identifiability.

AVAILABILITY AND IMPLEMENTATION

Code is available in GitHub (https://github.com/JFerrer-B/transcriptome-identifiability).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Breast cancer-derived GM-CSF regulates arginase 1 in myeloid cells to promote an immunosuppressive microenvironment

Tumor-infiltrating myeloid cells contribute to the development of the immunosuppressive tumor microenvironment. Myeloid cell expression of arginase 1 (ARG1) promotes a protumor phenotype by inhibiting T cell function and depleting extracellular l-arginine, but the mechanism underlying this expression, especially in breast cancer, is poorly understood. In breast cancer clinical samples and in our mouse models, we identified tumor-derived GM-CSF as the primary regulator of myeloid cell ARG1 expression and local immune suppression through a gene-KO screen of breast tumor cell-produced factors. The induction of myeloid cell ARG1 required GM-CSF and a low pH environment. GM-CSF signaling through STAT3 and p38 MAPK and acid signaling through cAMP were required to activate myeloid cell ARG1 expression in a STAT6-independent manner. Importantly, breast tumor cell-derived GM-CSF promoted tumor progression by inhibiting host antitumor immunity, driving a significant accumulation of ARG1-expressing myeloid cells compared with lung and melanoma tumors with minimal GM-CSF expression. Blockade of tumoral GM-CSF enhanced the efficacy of tumor-specific adoptive T cell therapy and immune checkpoint blockade. Taken together, we show that breast tumor cell-derived GM-CSF contributes to the development of the immunosuppressive breast cancer microenvironment by regulating myeloid cell ARG1 expression and can be targeted to enhance breast cancer immunotherapy.

Loss of KMT2C reprograms the epigenomic landscape in hPSCs resulting in NODAL overexpression and a failure of hemogenic endothelium specification

Germline or somatic variation in the family of KMT2 lysine methyltransferases have been associated with a variety of congenital disorders and cancers. Notably, KMT2A-fusions are prevalent in 70% of infant leukaemias but fail to phenocopy short latency leukaemogenesis in mammalian models, suggesting additional factors are necessary for transformation. Given the lack of additional somatic mutation, the role of epigenetic regulation in cell specification, and our prior results of germline KMT2C variation in infant leukaemia patients, we hypothesized that germline dysfunction of KMT2C altered haematopoietic specification. In isogenic KMT2C KO hPSCs, we found genome-wide differences in histone modifications at active and poised enhancers, leading to gene expression profiles akin to mesendoderm rather than mesoderm highlighted by a significant increase in NODAL expression and WNT inhibition, ultimately resulting in a lack of in vitro hemogenic endothelium specification. These unbiased multi-omic results provide new evidence for germline mechanisms increasing risk of early leukaemogenesis.

A Non-stop identity complex (NIC) supervises enterocyte identity and protects from premature aging

A hallmark of aging is loss of differentiated cell identity. Aged Drosophila midgut differentiated enterocytes (ECs) lose their identity, impairing tissue homeostasis. To discover identity regulators, we performed an RNAi screen targeting ubiquitin-related genes in ECs. Seventeen genes were identified, including the deubiquitinase Non-stop (CG4166). Lineage tracing established that acute loss of Non-stop in young ECs phenocopies aged ECs at cellular and tissue levels. Proteomic analysis unveiled that Non-stop maintains identity as part of a Non-stop identity complex (NIC) containing E(y)2, Sgf11, Cp190, (Mod) mdg4, and Nup98. Non-stop ensured chromatin accessibility, maintaining the EC-gene signature, and protected NIC subunit stability. Upon aging, the levels of Non-stop and NIC subunits declined, distorting the unique organization of the EC nucleus. Maintaining youthful levels of Non-stop in wildtype aged ECs safeguards NIC subunits, nuclear organization, and suppressed aging phenotypes. Thus, Non-stop and NIC, supervise EC identity and protects from premature aging.

Battling COVID-19: critical care and peri-operative healthcare resource management strategies in a tertiary academic medical centre in Singapore

In December 2019, a cluster of atypical pneumonia cases were reported in Wuhan, China, and a novel coronavirus elucidated as the aetiologic agent. Although most initial cases occurred in China, the disease, termed coronavirus disease 2019, has become a pandemic and continues to spread rapidly with human-to-human transmission in many countries. This is the third novel coronavirus outbreak in the last two decades and presents an ensuing healthcare resource burden that threatens to overwhelm available healthcare resources. A study of the initial Chinese response has shown that there is a significant positive association between coronavirus disease 2019 mortality and healthcare resource burden. Based on the Chinese experience, some 19% of coronavirus disease 2019 cases develop severe or critical disease. This results in a need for adequate preparation and mobilisation of critical care resources to anticipate and adapt to a surge in coronavirus disease 2019 case-load in order to mitigate morbidity and mortality. In this article, we discuss some of the peri-operative and critical care resource planning considerations and management strategies employed in a tertiary academic medical centre in Singapore in response to the coronavirus disease 2019 outbreak.

The evolutionary dynamics and fitness landscape of clonal hematopoiesis

Somatic mutations acquired in healthy tissues as we age are major determinants of cancer risk. Whether variants confer a fitness advantage or rise to detectable frequencies by chance remains largely unknown. Blood sequencing data from ~50,000 individuals reveal how mutation, genetic drift, and fitness shape the genetic diversity of healthy blood (clonal hematopoiesis). We show that positive selection, not drift, is the major force shaping clonal hematopoiesis, provide bounds on the number of hematopoietic stem cells, and quantify the fitness advantages of key pathogenic variants, at single-nucleotide resolution, as well as the distribution of fitness effects (fitness landscape) within commonly mutated driver genes. These data are consistent with clonal hematopoiesis being driven by a continuing risk of mutations and clonal expansions that become increasingly detectable with age.

Haplotype-resolved and integrated genome analysis of the cancer cell line HepG2

HepG2 is one of the most widely used human cancer cell lines in biomedical research and one of the main cell lines of ENCODE. Although the functional genomic and epigenomic characteristics of HepG2 are extensively studied, its genome sequence has never been comprehensively analyzed and higher order genomic structural features are largely unknown. The high degree of aneuploidy in HepG2 renders traditional genome variant analysis methods challenging and partially ineffective. Correct and complete interpretation of the extensive functional genomics data from HepG2 requires an understanding of the cell line’s genome sequence and genome structure. Using a variety of sequencing and analysis methods, we identified a wide spectrum of genome characteristics in HepG2: copy numbers of chromosomal segments at high resolution, SNVs and Indels (corrected for aneuploidy), regions with loss of heterozygosity, phased haplotypes extending to entire chromosome arms, retrotransposon insertions and structural variants (SVs) including complex and somatic genomic rearrangements. A large number of SVs were phased, sequence assembled and experimentally validated. We re-analyzed published HepG2 datasets for allele-specific expression and DNA methylation and assembled an allele-specific CRISPR/Cas9 targeting map. We demonstrate how deeper insights into genomic regulatory complexity are gained by adopting a genome-integrated framework.

Endometriosis - A rare cause of primary spontaneous pneumothorax

Catamenial pneumothorax is a rare condition. We report a case of a 36-year-old female who presented with dyspnoea every time before she had her regular menses. Further investigation confirmed that she had catamenial pneumothorax. With this case we wish to highlight this rare diagnostic entity that every clinician should keep in mind.

Comprehensive, integrated, and phased whole-genome analysis of the primary ENCODE cell line K562

K562 is widely used in biomedical research. It is one of three tier-one cell lines of ENCODE and also most commonly used for large-scale CRISPR/Cas9 screens. Although its functional genomic and epigenomic characteristics have been extensively studied, its genome sequence and genomic structural features have never been comprehensively analyzed. Such information is essential for the correct interpretation and understanding of the vast troves of existing functional genomics and epigenomics data for K562. We performed and integrated deep-coverage whole-genome (short-insert), mate-pair, and linked-read sequencing as well as karyotyping and array CGH analysis to identify a wide spectrum of genome characteristics in K562: copy numbers (CN) of aneuploid chromosome segments at high-resolution, SNVs and indels (both corrected for CN in aneuploid regions), loss of heterozygosity, megabase-scale phased haplotypes often spanning entire chromosome arms, structural variants (SVs), including small and large-scale complex SVs and nonreference retrotransposon insertions. Many SVs were phased, assembled, and experimentally validated. We identified multiple allele-specific deletions and duplications within the tumor suppressor gene FHIT. Taking aneuploidy into account, we reanalyzed K562 RNA-seq and whole-genome bisulfite sequencing data for allele-specific expression and allele-specific DNA methylation. We also show examples of how deeper insights into regulatory complexity are gained by integrating genomic variant information and structural context with functional genomics and epigenomics data. Furthermore, using K562 haplotype information, we produced an allele-specific CRISPR targeting map. This comprehensive whole-genome analysis serves as a resource for future studies that utilize K562 as well as a framework for the analysis of other cancer genomes.

Extensive and deep sequencing of the Venter/HuRef genome for developing and benchmarking genome analysis tools

We produced an extensive collection of deep re-sequencing datasets for the Venter/HuRef genome using the Illumina massively-parallel DNA sequencing platform. The original Venter genome sequence is a very-high quality phased assembly based on Sanger sequencing. Therefore, researchers developing novel computational tools for the analysis of human genome sequence variation for the dominant Illumina sequencing technology can test and hone their algorithms by making variant calls from these Venter/HuRef datasets and then immediately confirm the detected variants in the Sanger assembly, freeing them of the need for further experimental validation. This process also applies to implementing and benchmarking existing genome analysis pipelines. We prepared and sequenced 200 bp and 350 bp short-insert whole-genome sequencing libraries (sequenced to 100x and 40x genomic coverages respectively) as well as 2 kb, 5 kb, and 12 kb mate-pair libraries (49x, 122x, and 145x physical coverages respectively). Lastly, we produced a linked-read library (128x physical coverage) from which we also performed haplotype phasing.

Rare Event Detection Using Error-corrected DNA and RNA Sequencing

Conventional next-generation sequencing techniques (NGS) have allowed for immense genomic characterization for over a decade. Specifically, NGS has been used to analyze the spectrum of clonal mutations in malignancy. Though far more efficient than traditional Sanger methods, NGS struggles with identifying rare clonal and subclonal mutations due to its high error rate of ~0.5-2.0%. Thus, standard NGS has a limit of detection for mutations that are >0.02 variant allele fraction (VAF). While the clinical significance for mutations this rare in patients without known disease remains unclear, patients treated for leukemia have significantly improved outcomes when residual disease is <0.0001 by flow cytometry. In order to mitigate this artefactual background of NGS, numerous methods have been developed. Here we describe a method for Error-corrected DNA and RNA Sequencing (ECS), which involves tagging individual molecules with both a 16 bp random index for error-correction and an 8 bp patient-specific index for multiplexing. Our method can detect and track clonal mutations at variant allele fractions (VAFs) two orders of magnitude lower than the detection limit of NGS and as rare as 0.0001 VAF.

A mixed-methods study to explore the supportive care needs of breast cancer survivors

OBJECTIVE

Needs assessment is the essence of quality cancer survivorship care. The aim of this study was to explore the supportive care needs of breast cancer survivors (BCS) in the first 5 years post treatment.

METHODS

A mixed-methods approach was employed. A quantitative study included a Supportive Care Needs Survey, which was completed by 250 BCS to identify the level of their needs for help. The quantitative data informed semistructured qualitative interviews undertaken with 60 BCS to explore in detail their posttreatment needs and experiences.

RESULTS

32.4% and 16.8% reported 1 to 5 and greater than or equal to 6 needs for help, respectively. The regression analyses revealed that women within 2 years posttreatment and with higher educational level had higher levels of Psychological and Health Care System/Information needs. The qualitative data revealed "continuity of care" and "lifestyle advice and self-management" as prominent survivorship concerns. It was shown that determination to continue normal life, social support, and feeling overwhelmed by information were all experienced as important influences on survivors' need for help.

CONCLUSIONS

Posttreatment needs vary with BCS characteristics and to the domains concerned. The approach to posttreatment care needs to be personalized and viable.

Abstract 4878: Sensitive and specific DNA and RNA sequencing techniques for detecting minimal residual disease

The goal of this study was to assess the clinical applicability of molecular genetic techniques in the study of minimal residual disease (MRD) in pediatric acute myeloid leukemia (AML) via DNA and RNA-based methods. Currently, multi-parametric flow cytometry (MPFC) for surface immunophenotypes is the gold standard for MRD with a limit of detection between 0.001-0.002. However, approximately one-third of children with no detectable MRD by MPFC after first induction still relapse and suffer inferior outcomes. One potential reason is that a majority of refractory or recurrent AML clones will present with altered immunophenotypes compared to diagnostic specimens. In contrast, DNA mutations present at diagnosis will remain in refractory disease and therefore, a gene-based MRD platform surveying many genes could enable targeted, gene-specific therapy.

Recently, the heterogeneous and unique genomic landscape of pediatric AML was characterized in the TARGET project and highlights the potential for leveraging companion molecular screens in the analysis of AML MRD. Contrary to adult AML, pediatric AML is not primarily characterized by single nucleotide variants (SNVs), but rather by complex structural variants (SV) that are difficult to detect using standard next generation sequencing techniques and analysis pipelines. The need to detect these complex SV and SNVs, all of which are at low allelic ratios (AR) at the remission state, demands assays that are capable of analyzing both DNA and RNA molecules at levels of detection comparable to MPFC.

As proof of principal, we leveraged a unique capture technique (ArcherDx; CoreAML) to detect an important SV in pediatric AML, the internal tandem duplication in Fms related tyrosine Kinase (FLT3-ITD). Via a serial dilution of cells with known FLT3-ITD allelic ratio (MV4-11 cells), to determine a limit of detection, we were able to detect the mutation as low as 0.001. Next, we bench-marked the technique using diagnostic and relapse samples, and successfully detected FLT3-ITD at AR &lt;0.01.

To complement the DNA-based approach, we compared two error-corrected (EC) RNA assays (a Druley lab developed protocol and the ArcherDx; HemeV2 panel) to further assay SVs that cause novel gene fusions and aberrant exon usage, which are not detectable via DNA assays. Compared to existing cytogenetic and RNA-seq data, this new platform was capable of detecting known and novel cryptic translocations.

Finally, we integrated the results from the FLT3-ITD assay and the RNA assay with our custom error-corrected sequencing data to create a novel bioinformatics workflow for assessing the biological implications of MRD clones detected. Collectively, our data support that EC sequencing, at both the DNA and RNA level, enable accurate detection of low allelic variants that could be used for improved clinical MRD diagnostics, prognostication and therapeutic selection.

Citation Format: Erin L. Crowgey, Nitin Mahajan, Wing H. Wong, Edward A. Kolb, Todd Druley. Sensitive and specific DNA and RNA sequencing techniques for detecting minimal residual disease [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4878. doi:10.1158/1538-7445.AM2017-4878

Stable 5-Hydroxymethylcytosine (5hmC) Acquisition Marks Gene Activation During Chondrogenic Differentiation

Regulation of gene expression changes during chondrogenic differentiation by DNA methylation and demethylation is little understood. Methylated cytosines (5mC) are oxidized by the ten-eleven-translocation (TET) proteins to 5-hydroxymethylcytosines (5hmC), 5-formylcytosines (5fC), and 5-carboxylcytosines (5caC), eventually leading to a replacement by unmethylated cytosines (C), ie, DNA demethylation. Additionally, 5hmC is stable and acts as an epigenetic mark by itself. Here, we report that global changes in 5hmC mark chondrogenic differentiation in vivo and in vitro. Tibia anlagen and growth plate analyses during limb development at mouse embryonic days E 11.5, 13.5, and 17.5 showed dynamic changes in 5hmC levels in the differentiating chondrocytes. A similar increase in 5hmC levels was observed in the ATDC5 chondroprogenitor cell line accompanied by increased expression of the TET proteins during in vitro differentiation. Loss of TET1 in ATDC5 decreased 5hmC levels and impaired differentiation, demonstrating a functional role for TET1-mediated 5hmC dynamics in chondrogenic differentiation. Global analyses of the 5hmC-enriched sequences during early and late chondrogenic differentiation identified 5hmC distribution to be enriched in the regulatory regions of genes preceding the transcription start site (TSS), as well as in the gene bodies. Stable gains in 5hmC were observed in specific subsets of genes, including genes associated with cartilage development and in chondrogenic lineage-specific genes. 5hmC gains in regulatory promoter and enhancer regions as well as in gene bodies were strongly associated with activated but not repressed genes, indicating a potential regulatory role for DNA hydroxymethylation in chondrogenic gene expression.

Insensitivity to Flaws Leads to Damage Tolerance in Brittle Architected Meta-Materials

Cellular solids are instrumental in creating lightweight, strong, and damage-tolerant engineering materials. By extending feature size down to the nanoscale, we simultaneously exploit the architecture and material size effects to substantially enhance structural integrity of architected meta-materials. We discovered that hollow-tube alumina nanolattices with 3D kagome geometry that contained pre-fabricated flaws always failed at the same load as the pristine specimens when the ratio of notch length (a) to sample width (w) is no greater than 1/3, with no correlation between failure occurring at or away from the notch. Samples with (a/w) > 0.3, and notch length-to-unit cell size ratios of (a/l) > 5.2, failed at a lower peak loads because of the higher sample compliance when fewer unit cells span the intact region. Finite element simulations show that the failure is governed by purely tensile loading for (a/w) < 0.3 for the same (a/l); bending begins to play a significant role in failure as (a/w) increases. This experimental and computational work demonstrates that the discrete-continuum duality of architected structural meta-materials may give rise to their damage tolerance and insensitivity of failure to the presence of flaws even when made entirely of intrinsically brittle materials.

The primate-specific noncoding RNA HPAT5 regulates pluripotency during human preimplantation development and nuclear reprogramming

Long intergenic noncoding RNAs (lincRNAs) are derived from thousands of loci in mammalian genomes and are frequently enriched in transposable elements (TEs). Although families of TE-derived lincRNAs have recently been implicated in the regulation of pluripotency, little is known of the specific functions of individual family members. Here we characterize three new individual TE-derived human lincRNAs, human pluripotency-associated transcripts 2, 3 and 5 (HPAT2, HPAT3 and HPAT5). Loss-of-function experiments indicate that HPAT2, HPAT3 and HPAT5 function in preimplantation embryo development to modulate the acquisition of pluripotency and the formation of the inner cell mass. CRISPR-mediated disruption of the genes for these lincRNAs in pluripotent stem cells, followed by whole-transcriptome analysis, identifies HPAT5 as a key component of the pluripotency network. Protein binding and reporter-based assays further demonstrate that HPAT5 interacts with the let-7 microRNA family. Our results indicate that unique individual members of large primate-specific lincRNA families modulate gene expression during development and differentiation to reinforce cell fate.

Genome-wide mapping of DNA hydroxymethylation in osteoarthritic chondrocytes

OBJECTIVE

To examine the genome-wide distribution of hydroxymethylated cytosine (5hmC) in osteoarthritic (OA) and normal chondrocytes in order to investigate the effect on OA-specific gene expression.

METHODS

Cartilage was obtained from OA patients undergoing total knee arthroplasty or from control patients undergoing anterior cruciate ligament reconstruction. Genome-wide sequencing of 5hmC-enriched DNA was performed in a small cohort of normal and OA chondrocytes to identify differentially hydroxymethylated regions (DhMRs) in OA chondrocytes. Data from the genome-wide sequencing of 5hmC-enriched DNA were intersected with global OA gene expression data to define subsets of genes and pathways potentially affected by increased 5hmC levels in OA chondrocytes.

RESULTS

A total of 70,591 DhMRs were identified in OA chondrocytes as compared to normal chondrocytes, 44,288 (63%) of which were increased in OA chondrocytes. The majority of DhMRs (66%) were gained in gene bodies. Increased DhMRs were observed in ∼50% of genes previously implicated in OA pathology including MMP3, LRP5, GDF5, and COL11A1. Furthermore, analyses of gene expression data revealed gene body gain of 5hmC appears to be preferentially associated with activated, but not repressed, genes in OA chondrocytes.

CONCLUSION

This study provides the first genome-wide profiling of 5hmC distribution in OA chondrocytes. We had previously reported a global increase in 5hmC levels in OA chondrocytes. Gain of 5hmC in the gene body is found to be characteristic of activated genes in OA chondrocytes, highlighting the influence of 5hmC as an epigenetic mark in OA. In addition, this study identifies multiple OA-associated genes that are potentially regulated either singularly by gain of DNA hydroxymethylation or in combination with loss of DNA methylation.

Leveraging long read sequencing from a single individual to provide a comprehensive resource for benchmarking variant calling methods

A high-confidence, comprehensive human variant set is critical in assessing accuracy of sequencing algorithms, which are crucial in precision medicine based on high-throughput sequencing. Although recent works have attempted to provide such a resource, they still do not encompass all major types of variants including structural variants (SVs). Thus, we leveraged the massive high-quality Sanger sequences from the HuRef genome to construct by far the most comprehensive gold set of a single individual, which was cross validated with deep Illumina sequencing, population datasets, and well-established algorithms. It was a necessary effort to completely reanalyze the HuRef genome as its previously published variants were mostly reported five years ago, suffering from compatibility, organization, and accuracy issues that prevent their direct use in benchmarking. Our extensive analysis and validation resulted in a gold set with high specificity and sensitivity. In contrast to the current gold sets of the NA12878 or HS1011 genomes, our gold set is the first that includes small variants, deletion SVs and insertion SVs up to a hundred thousand base-pairs. We demonstrate the utility of our HuRef gold set to benchmark several published SV detection tools.

An ensemble approach to accurately detect somatic mutations using SomaticSeq

SomaticSeq is an accurate somatic mutation detection pipeline implementing a stochastic boosting algorithm to produce highly accurate somatic mutation calls for both single nucleotide variants and small insertions and deletions. The workflow currently incorporates five state-of-the-art somatic mutation callers, and extracts over 70 individual genomic and sequencing features for each candidate site. A training set is provided to an adaptively boosted decision tree learner to create a classifier for predicting mutation statuses. We validate our results with both synthetic and real data. We report that SomaticSeq is able to achieve better overall accuracy than any individual tool incorporated.

Abstract LB-306: An ensemble approach to accurately detect somatic mutations via adaptive boosting

Identifying somatic mutations is a key analysis in cancer research. The challenge lies in the impure and heterogeneous nature of the tumor samples. Oftentimes, an algorithm works well for one tumor but poorly for another. Here, we present an ensemble approach that integrates multiple algorithms and demonstrate its performance and high accuracy with validation from both synthetic data and real data.

Our approach incorporates state-of-the-art callers including MuTect, SomaticSniper, VarScan2, JointSNVMix2, and VarDict for somatic mutation detection. Each of these algorithms has its unique strength, capable of detecting variants that are missed by some others. The call sets are combined based on 70 independent sequencing and genomic features, which are then used by an adaptively boosted decision tree learner. The learner is trained with a sophisticated simulated data to discriminate true mutations from very noisy data of the tumor samples.

In our latest submission to the ICGC-TCGA DREAM Mutation Calling Challenge (the Challenge), our approach obtained an unprecedented somatic SNV detection accuracy of 97.1% with a recall of 94.2% and a precision of 99.9%. The synthetic data was a tumor-normal pair of samples with 30x sequencing depth each. The tumor sample was synthesized by spiking in a whole spectrum of variants ranging from SNVs/Indels to SVs, resulting in an SNV allele frequency (VAF) of 25%.

We further validated our approach with “in silico titration”. The titration mixed two different real genomes at different proportions with validated ground truths to generate different sample conditions, ranging from the simplest case where the normal and tumor were pure to the more challenging case where the tumor and normal tissues cross contaminated. From an VAF of 50%, 25% to 15%, our approach achieved an accuracy of 95.7%, 92.5%, and 85.3% respectively based on cross validation, consistent with the results from the Challenge.

Finally, we validated our approach with three widely-used and published cancer datasets, obtained from TCGA and EGA, including a whole-genome sequenced malignant melanoma cell line, a whole-genome sequenced chronic lymphocytic leukemia cell line, and a whole-exome sequenced colon adenocarcinoma patient sample with experimentally validated somatic mutations. Our approach was trained on the data from the Challenge and applied to the aforementioned samples to measure its accuracy. Our results showed that we achieved a recall of 98.9%, 89.1% and 87.9% respectively. Although precision on real data cannot be measured without a comprehensive whole-genome experimental validation, our comparatively smaller call sets compared to all other methods considered implying that it has the highest precision among all.

We extended our study of the above three validation approaches, namely synthetic genomes, in silico titration, and real samples, to compare with all the five individual callers for accuracy performance. We found that our approach had the highest accuracy when compared to any individual caller. To conclude, our approach is shown to have high accuracy in different types and conditions of tumor samples and by far the best in its class.

Citation Format: Li Tai Fang, Pegah T. Afshar, John C. Mu, Narges Bani Asadi, Wing H. Wong, Hugo Y. K. Lam. An ensemble approach to accurately detect somatic mutations via adaptive boosting. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-306. doi:10.1158/1538-7445.AM2015-LB-306

MetaSV: an accurate and integrative structural-variant caller for next generation sequencing

Summary: Structural variations (SVs) are large genomic rearrangements that vary significantly in size, making them challenging to detect with the relatively short reads from next-generation sequencing (NGS). Different SV detection methods have been developed; however, each is limited to specific kinds of SVs with varying accuracy and resolution. Previous works have attempted to combine different methods, but they still suffer from poor accuracy particularly for insertions. We propose MetaSV, an integrated SV caller which leverages multiple orthogonal SV signals for high accuracy and resolution. MetaSV proceeds by merging SVs from multiple tools for all types of SVs. It also analyzes soft-clipped reads from alignment to detect insertions accurately since existing tools underestimate insertion SVs. Local assembly in combination with dynamic programming is used to improve breakpoint resolution. Paired-end and coverage information is used to predict SV genotypes. Using simulation and experimental data, we demonstrate the effectiveness of MetaSV across various SV types and sizes.

Availability and implementation: Code in Python is at http://bioinform.github.io/metasv/.

Contact:rd@bina.com

Supplementary information:Supplementary data are available at Bioinformatics online.

Prospective study on the effects of orthotic treatment for medial knee osteoarthritis in Chinese patients: clinical outcome and gait analysis

OBJECTIVE

To evaluate the effectiveness of various orthotic treatments for patients with isolated medial compartment osteoarthritis.

DESIGN

Prospective cohort study with sequential interventions.

SETTING

University-affiliated hospital, Hong Kong.

PATIENTS

From December 2010 to November 2011, 10 patients with medial knee osteoarthritis were referred by orthopaedic surgeons for orthotic treatment. All patients were sequentially treated with flat insole, lateral-wedged insole, lateral-wedged insole with subtalar strap, lateral-wedged insole with arch support, valgus knee brace, and valgus knee brace with lateral-wedged insole with arch support for 4 weeks with no treatment break. Three-dimensional gait analysis and questionnaires were completed after each orthotic treatment.

MAIN OUTCOME MEASURES

The Western Ontario and McMaster Universities Arthritis Index (WOMAC), visual analogue scale scores, and peak and mean knee adduction moments.

RESULTS

Compared with pretreatment, the lateral-wedged insole, lateral-wedged insole with arch support, and valgus knee brace groups demonstrated significant reductions in WOMAC pain score (19.1%, P=0.04; 18.2%, P=0.04; and 20.4%, P=0.02, respectively). The lateral-wedged insole with arch support group showed the greatest reduction in visual analogue scale score compared with pretreatment at 24.1% (P=0.004). Addition of a subtalar strap to lateral-wedged insoles (lateral-wedged insole with subtalar strap) did not produce significant benefit when compared with the lateral-wedged insole alone. The valgus knee brace with lateral-wedged insole with arch support group demonstrated an additive effect with a statistically significant reduction in WOMAC total score (-26.7%, P=0.01). Compliance with treatment for the isolated insole groups were all over 90%, but compliance for the valgus knee brace-associated groups was only around 50%. Gait analysis indicated statistically significant reductions in peak and mean knee adduction moments in all orthotic groups when compared with a flat insole.

CONCLUSIONS

These results support the use of orthotic treatment for early medial compartment knee osteoarthritis.

VarSim: a high-fidelity simulation and validation framework for high-throughput genome sequencing with cancer applications

Summary: VarSim is a framework for assessing alignment and variant calling accuracy in high-throughput genome sequencing through simulation or real data. In contrast to simulating a random mutation spectrum, it synthesizes diploid genomes with germline and somatic mutations based on a realistic model. This model leverages information such as previously reported mutations to make the synthetic genomes biologically relevant. VarSim simulates and validates a wide range of variants, including single nucleotide variants, small indels and large structural variants. It is an automated, comprehensive compute framework supporting parallel computation and multiple read simulators. Furthermore, we developed a novel map data structure to validate read alignments, a strategy to compare variants binned in size ranges and a lightweight, interactive, graphical report to visualize validation results with detailed statistics. Thus far, it is the most comprehensive validation tool for secondary analysis in next generation sequencing.

Availability and implementation: Code in Java and Python along with instructions to download the reads and variants is at http://bioinform.github.io/varsim.

Contact:rd@bina.com

Supplementary information:Supplementary data are available at Bioinformatics online.

Density estimation on multivariate censored data with optional Pólya tree

Analyzing the failure times of multiple events is of interest in many fields. Estimating the joint distribution of the failure times in a non-parametric way is not straightforward because some failure times are often right-censored and only known to be greater than observed follow-up times. Although it has been studied, there is no universally optimal solution for this problem. It is still challenging and important to provide alternatives that may be more suitable than existing ones in specific settings. Related problems of the existing methods are not only limited to infeasible computations, but also include the lack of optimality and possible non-monotonicity of the estimated survival function. In this paper, we proposed a non-parametric Bayesian approach for directly estimating the density function of multivariate survival times, where the prior is constructed based on the optional Pólya tree. We investigated several theoretical aspects of the procedure and derived an efficient iterative algorithm for implementing the Bayesian procedure. The empirical performance of the method was examined via extensive simulation studies. Finally, we presented a detailed analysis using the proposed method on the relationship among organ recovery times in severely injured patients. From the analysis, we suggested interesting medical information that can be further pursued in clinics.

Successful carotid endarterectomy in a patient with an aberrant branch from the common carotid artery

We report a patient who had an 80% asymptomatic stenosis in the distal right common carotid artery with an incidental finding of an aberrant branch arising from the right common carotid artery. He underwent an elective right carotid endarterectomy with an uneventful recovery. This is the first case in the literature of a successful endarterectomy in a patient with a common carotid anomaly and it emphasises the importance of careful dissection for unexpected anatomy.

Neural-specific Sox2 input and differential Gli-binding affinity provide context and positional information in Shh-directed neural patterning

In the vertebrate neural tube, regional Sonic hedgehog (Shh) signaling invokes a time- and concentration-dependent induction of six different cell populations mediated through Gli transcriptional regulators. Elsewhere in the embryo, Shh/Gli responses invoke different tissue-appropriate regulatory programs. A genome-scale analysis of DNA binding by Gli1 and Sox2, a pan-neural determinant, identified a set of shared regulatory regions associated with key factors central to cell fate determination and neural tube patterning. Functional analysis in transgenic mice validates core enhancers for each of these factors and demonstrates the dual requirement for Gli1 and Sox2 inputs for neural enhancer activity. Furthermore, through an unbiased determination of Gli-binding site preferences and analysis of binding site variants in the developing mammalian CNS, we demonstrate that differential Gli-binding affinity underlies threshold-level activator responses to Shh input. In summary, our results highlight Sox2 input as a context-specific determinant of the neural-specific Shh response and differential Gli-binding site affinity as an important cis-regulatory property critical for interpreting Shh morphogen action in the mammalian neural tube.

Activation of innate immunity is required for efficient nuclear reprogramming

Retroviral overexpression of reprogramming factors (Oct4, Sox2, Klf4, c-Myc) generates induced pluripotent stem cells (iPSCs). However, the integration of foreign DNA could induce genomic dysregulation. Cell-permeant proteins (CPPs) could overcome this limitation. To date, this approach has proved exceedingly inefficient. We discovered a striking difference in the pattern of gene expression induced by viral versus CPP-based delivery of the reprogramming factors, suggesting that a signaling pathway required for efficient nuclear reprogramming was activated by the retroviral, but not CPP approach. In gain- and loss-of-function studies, we find that the toll-like receptor 3 (TLR3) pathway enables efficient induction of pluripotency by viral or mmRNA approaches. Stimulation of TLR3 causes rapid and global changes in the expression of epigenetic modifiers to enhance chromatin remodeling and nuclear reprogramming. Activation of inflammatory pathways are required for efficient nuclear reprogramming in the induction of pluripotency.

An Oct4-Sall4-Nanog network controls developmental progression in the pre-implantation mouse embryo

Landmark events occur in a coordinated manner during pre-implantation development of the mammalian embryo, yet the regulatory network that orchestrates these events remains largely unknown. Here, we present the first systematic investigation of the network in pre-implantation mouse embryos using morpholino-mediated gene knockdowns of key embryonic stem cell (ESC) factors followed by detailed transcriptome analysis of pooled embryos, single embryos, and individual blastomeres. We delineated the regulons of Oct4, Sall4, and Nanog and identified a set of metabolism- and transport-related genes that were controlled by these transcription factors in embryos but not in ESCs. Strikingly, the knockdown embryos arrested at a range of developmental stages. We provided evidence that the DNA methyltransferase Dnmt3b has a role in determining the extent to which a knockdown embryo can develop. We further showed that the feed-forward loop comprising Dnmt3b, the pluripotency factors, and the miR-290-295 cluster exemplifies a network motif that buffers embryos against gene expression noise. Our findings indicate that Oct4, Sall4, and Nanog form a robust and integrated network to govern mammalian pre-implantation development.

Modeling kinetic rate variation in third generation DNA sequencing data to detect putative modifications to DNA bases

Current generation DNA sequencing instruments are moving closer to seamlessly sequencing genomes of entire populations as a routine part of scientific investigation. However, while significant inroads have been made identifying small nucleotide variation and structural variations in DNA that impact phenotypes of interest, progress has not been as dramatic regarding epigenetic changes and base-level damage to DNA, largely due to technological limitations in assaying all known and unknown types of modifications at genome scale. Recently, single-molecule real time (SMRT) sequencing has been reported to identify kinetic variation (KV) events that have been demonstrated to reflect epigenetic changes of every known type, providing a path forward for detecting base modifications as a routine part of sequencing. However, to date no statistical framework has been proposed to enhance the power to detect these events while also controlling for false-positive events. By modeling enzyme kinetics in the neighborhood of an arbitrary location in a genomic region of interest as a conditional random field, we provide a statistical framework for incorporating kinetic information at a test position of interest as well as at neighboring sites that help enhance the power to detect KV events. The performance of this and related models is explored, with the best-performing model applied to plasmid DNA isolated from Escherichia coli and mitochondrial DNA isolated from human brain tissue. We highlight widespread kinetic variation events, some of which strongly associate with known modification events, while others represent putative chemically modified sites of unknown types.

Fast and accurate read alignment for resequencing

MOTIVATION

Next-generation sequence analysis has become an important task both in laboratory and clinical settings. A key stage in the majority sequence analysis workflows, such as resequencing, is the alignment of genomic reads to a reference genome. The accurate alignment of reads with large indels is a computationally challenging task for researchers.

RESULTS

We introduce SeqAlto as a new algorithm for read alignment. For reads longer than or equal to 100 bp, SeqAlto is up to 10 × faster than existing algorithms, while retaining high accuracy and the ability to align reads with large (up to 50 bp) indels. This improvement in efficiency is particularly important in the analysis of future sequencing data where the number of reads approaches many billions. Furthermore, SeqAlto uses less than 8 GB of memory to align against the human genome. SeqAlto is benchmarked against several existing tools with both real and simulated data.

AVAILABILITY

Linux and Mac OS X binaries free for academic use are available at http://www.stanford.edu/group/wonglab/seqalto

CONTACT

whwong@stanford.edu.

Predicting personalized multiple birth risks after in vitro fertilization-double embryo transfer

OBJECTIVE

To report and evaluate the performance and utility of an approach to predicting IVF-double embryo transfer (DET) multiple birth risks that is evidence-based, clinic-specific, and considers each patient's clinical profile.

DESIGN

Retrospective prediction modeling.

SETTING

An outpatient university-affiliated IVF clinic.

PATIENT(S)

We used boosted tree methods to analyze 2,413 independent IVF-DET treatment cycles that resulted in live births. The IVF cycles were retrieved from a database that comprised more than 33,000 IVF cycles.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

The performance of this prediction model, MBP-BIVF, was validated by an independent data set, to evaluate predictive power, discrimination, dynamic range, and reclassification.

RESULT(S)

Multiple birth probabilities ranging from 11.8% to 54.8% were predicted by the model and were significantly different from control predictions in more than half of the patients. The prediction model showed an improvement of 146% in predictive power and 16.0% in discrimination over control. The population standard error was 1.8%.

CONCLUSION(S)

We showed that IVF patients have inherently different risks of multiple birth, even when DET is specified, and this risk can be predicted before ET. The use of clinic-specific prediction models provides an evidence-based and personalized method to counsel patients.

Highly flexible near-infrared metamaterials

Plasmonic or metamaterial nanostructures are usually fabricated on rigid substrate i.e. glass, silicon. Optical functionality of such kinds of nanostructures is limited by the planar surface and thus sensitive to the incident angle of light. In this work, we demonstrated that a tri-layer flexible metamaterials working at near infrared (NIR) regime can be fabricated on transparent PET substrate using flip chip transfer (FCT) technique. FCT technique is solution-free and can also be applied to fabricate other functional nanostructures device on flexible substrate. We demonstrated NIR metamaterial device can be transformed into various shapes by bending the PET substrate.