Afirma Genomic Sequencing Classifier and Xpression Atlas Molecular Findings in Consecutive Bethesda III-VI Thyroid Nodules.

CONTEXT: Broad genomic analyses among thyroid histologies have been described from relatively small cohorts. OBJECTIVE: Investigate the molecular findings across a large, real-world cohort of thyroid fine-needle aspiration (FNA) samples. DESIGN: Retrospective analysis of RNA sequencing data files. SETTING: Clinical Laboratory Improvement Amendments laboratory performing Afirma Genomic Sequencing Classifier (GSC) and Xpression Atlas (XA) testing. PARTICIPANTS: A total of 50 644 consecutive Bethesda III-VI nodules. INTERVENTION: None. MAIN OUTCOME MEASURES: Molecular test results. RESULTS: Of 48 952 Bethesda III/IV FNAs studied, 66% were benign by Afirma GSC. The prevalence of BRAF V600E was 2% among all Bethesda III/IV FNAs and 76% among Bethesda VI FNAs. Fusions involving NTRK, RET, BRAF, and ALK were most prevalent in Bethesda V (10%), and 130 different gene partners were identified. Among small consecutive Bethesda III/IV sample cohorts with one of these fusions and available surgical pathology excision data, the positive predictive value of an NTRK or RET fusion for carcinoma or noninvasive follicular thyroid neoplasm with papillary-like nuclear features was >95%, whereas for BRAF and ALK fusions it was 81% and 67%, respectively. At least 1 genomic alteration was identified by the expanded Afirma XA panel in 70% of medullary thyroid carcinoma classifier-positive FNAs, 44% of Bethesda III or IV Afirma GSC suspicious FNAs, 64% of Bethesda V FNAs, and 87% of Bethesda VI FNAs. CONCLUSIONS: This large study demonstrates that almost one-half of Bethesda III/IV Afirma GSC suspicious and most Bethesda V/VI nodules had at least 1 genomic variant or fusion identified, which may optimize personalized treatment decisions.

Analytical validation of the Percepta genomic sequencing classifier; an RNA next generation sequencing assay for the assessment of Lung Cancer risk of suspicious pulmonary nodules.

BACKGROUND: Bronchoscopy is a common procedure used for evaluation of suspicious lung nodules, but the low diagnostic sensitivity of bronchoscopy often results in inconclusive results and delays in treatment. Percepta Genomic Sequencing Classifier (GSC) was developed to assist with patient management in cases where bronchoscopy is inconclusive. Studies have shown that exposure to tobacco smoke alters gene expression in airway epithelial cells in a way that indicates an increased risk of developing lung cancer. Percepta GSC leverages this idea of a molecular "field of injury" from smoking and was developed using RNA sequencing data generated from lung bronchial brushings of the upper airway. A Percepta GSC score is calculated from an ensemble of machine learning algorithms utilizing clinical and genomic features and is used to refine a patient's risk stratification. METHODS: The objective of the analysis described and reported here is to validate the analytical performance of Percepta GSC. Analytical performance studies characterized the sensitivity of Percepta GSC test results to input RNA quantity, the potentially interfering agents of blood and genomic DNA, and the reproducibility of test results within and between processing runs and between laboratories. RESULTS: Varying the amount of input RNA into the assay across a nominal range had no significant impact on Percepta GSC classifier results. Bronchial brushing RNA contaminated with up to 10% genomic DNA by nucleic acid mass also showed no significant difference on classifier results. The addition of blood RNA, a potential contaminant in the bronchial brushing sample, caused no change to classifier results at up to 11% contamination by RNA proportion. Percepta GSC scores were reproducible between runs, within runs, and between laboratories, varying within less than 4% of the total score range (standard deviation of 0.169 for scores on 4.57 scale). CONCLUSIONS: The analytical sensitivity, analytical specificity, and reproducibility of Percepta GSC laboratory results were successfully demonstrated under conditions of expected day to day variation in testing. Percepta GSC test results are analytically robust and suitable for routine clinical use.

Improving lung cancer risk stratification leveraging whole transcriptome RNA sequencing and machine learning across multiple cohorts.

BACKGROUND: Bronchoscopy for suspected lung cancer has low diagnostic sensitivity, rendering many inconclusive results. The Bronchial Genomic Classifier (BGC) was developed to help with patient management by identifying those with low risk of lung cancer when bronchoscopy is inconclusive. The BGC was trained and validated on patients in the Airway Epithelial Gene Expression in the Diagnosis of Lung Cancer (AEGIS) trials. A modern patient cohort, the BGC Registry, showed differences in key clinical factors from the AEGIS cohorts, with less smoking history, smaller nodules and older age. Additionally, we discovered interfering factors (inhaled medication and sample collection timing) that impacted gene expressions and potentially disguised genomic cancer signals. METHODS: In this study, we leveraged multiple cohorts and next generation sequencing technology to develop a robust Genomic Sequencing Classifier (GSC). To address demographic composition shift and interfering factors, we synergized three algorithmic strategies: 1) ensemble of clinical dominant and genomic dominant models; 2) development of hierarchical regression models where the main effects from clinical variables were regressed out prior to the genomic impact being fitted in the model; and 3) targeted placement of genomic and clinical interaction terms to stabilize the effect of interfering factors. The final GSC model uses 1232 genes and four clinical covariates - age, pack-years, inhaled medication use, and specimen collection timing. RESULTS: In the validation set (N = 412), the GSC down-classified low and intermediate pre-test risk subjects to very low and low post-test risk with a specificity of 45% (95% CI 37-53%) and a sensitivity of 91% (95%CI 81-97%), resulting in a negative predictive value of 95% (95% CI 89-98%). Twelve percent of intermediate pre-test risk subjects were up-classified to high post-test risk with a positive predictive value of 65% (95%CI 44-82%), and 27% of high pre-test risk subjects were up-classified to very high post-test risk with a positive predictive value of 91% (95% CI 78-97%). CONCLUSIONS: The GSC overcame the impact of interfering factors and achieved consistent performance across multiple cohorts. It demonstrated diagnostic accuracy in both down- and up-classification of cancer risk, providing physicians actionable information for many patients with inconclusive bronchoscopy.

Analytical and Clinical Validation of Expressed Variants and Fusions From the Whole Transcriptome of Thyroid FNA Samples.

Introduction: The Afirma® Xpression Atlas (XA) detects gene variants and fusions in thyroid nodule FNA samples from a curated panel of 511 genes using whole-transcriptome RNA-sequencing. Its intended use is among cytologically indeterminate nodules that are Afirma GSC suspicious, Bethesda V/VI nodules, or known thyroid metastases. Here we report its analytical and clinical validation. Methods: DNA and RNA were purified from the same sample across 943 blinded FNAs and compared by multiple methodologies, including whole-transcriptome RNA-seq, targeted RNA-seq, and targeted DNA-seq. An additional 695 blinded FNAs were used to define performance for fusions between whole-transcriptome RNA-seq and targeted RNA-seq. We quantified the reproducibility of the whole-transcriptome RNA-seq assay across laboratories and reagent lots. Finally, variants and fusions were compared to histopathology results. Results: Of variants detected in DNA at 5 or 20% variant allele frequency, 74 and 88% were also detected by XA, respectively. XA variant detection was 89% when compared to an alternative RNA-based detection method. Low levels of expression of the DNA allele carrying the variant, compared with the wild-type allele, was found in some variants not detected by XA. 82% of gene fusions detected in a targeted RNA fusion assay were detected by XA. Conversely, nearly all variants or fusions detected by XA were confirmed by an alternative method. Analytical validation studies demonstrated high intra-plate reproducibility (89%-94%), inter-plate reproducibility (86-91%), and inter-lab accuracy (90%). Multiple variants and fusions previously described across the spectrum of thyroid cancers were identified by XA, including some with approved or investigational targeted therapies. Among 190 Bethesda III/IV nodules, the sensitivity of XA as a standalone test was 49%. Conclusion: When the Afirma Genomic Sequencing Classifier (GSC) is used first among Bethesda III/IV nodules as a rule-out test, XA supplements genomic insight among those that are GSC suspicious. Our data clinically and analytically validate XA for use among GSC suspicious, or Bethesda V/VI nodules. Genomic information provided by XA may inform clinical decision-making with precision medicine insights across a broad range of FNA sample types encountered in the care of patients with thyroid nodules and thyroid cancer.

Molecular Variants and Their Risks for Malignancy in Cytologically Indeterminate Thyroid Nodules.

Background: Gene panels are routinely used to assess predisposition to hereditary cancers by simultaneously testing multiple susceptibility genes and/or variants. More recently, genetic panels have been implemented as part of solid tumor malignancy testing assessing somatic alterations. One example is targeted variant panels for thyroid nodules that are not conclusively malignant or benign upon fine-needle aspiration (FNA). We systematically reviewed published studies from 2009 to 2018 that contained genetic data from preoperative FNA specimens on cytologically indeterminate thyroid nodules (ITNs) that subsequently underwent surgical resection. Pooled prevalence estimates per gene and variant, along with their respective positive predictive values (PPVs) for malignancy, were calculated. Summary: Our systematic search identified 540 studies that were supplemented by 18 studies from bibliographies or personal files. Sixty-one studies met all inclusion criteria and included >4600 ITNs. Overall, 26% of nodules contained at least 1 variant or fusion. However, half of them did not include details on the specific gene, variant, and/or complete fusion pair reported for inclusion toward PPV calculations. The PPVs of genomic alterations reported at least 10 times were limited to BRAFV600E (98%, 95% confidence interval [CI 96-99%]), PAX8/PPARG (55% [CI 34-78%]), HRASQ61R (45% [CI 22-72%]), BRAFK601E (42% [CI 19-68%]), and NRASQ61R (38% [CI 23-55%]). Excluding BRAFV600E, the pooled PPV for all other specified variants and fusions was 47%. Multiple variants within the same nodule were identified in ∼1% of ITN and carried a cumulative PPV of 77%. Conclusions: The chance that a genomic alteration predicts malignancy depends on the individual variant or fusion detected. Only five alterations were reported at least 10 times; BRAFV600E had a PPV of 98%, while the remaining four had individual PPVs ranging from 38% to 55%. The small sample size of most variants and fusion pairs found among ITNs, however, limits confidence in their individual PPV point estimates. Better specific reporting of genomic alterations with cytological category, histological subtype, and cancer staging would facilitate better understanding of cancer prediction, and the independent contribution of the genomic profile to prognosis.

Analytical Verification Performance of Afirma Genomic Sequencing Classifier in the Diagnosis of Cytologically Indeterminate Thyroid Nodules.

Background: Fine needle aspiration (FNA) cytology, a diagnostic test central to thyroid nodule management, may yield indeterminate results in up to 30% of cases. The Afirma® Genomic Sequencing Classifier (GSC) was developed and clinically validated to utilize genomic material obtained during the FNA to accurately identify benign nodules among those deemed cytologically indeterminate so that diagnostic surgery can be avoided. A key question for diagnostic tests is their robustness under different perturbations that may occur in the lab. Herein, we describe the analytical performance of the Afirma GSC. Results: We examined the analytical sensitivity of the Afirma GSC to varied input RNA amounts and the limit of detection of malignant signals with heterogenous samples mixed with adjacent normal or benign tissues. We also evaluated the analytical specificity from potential interfering substances such as blood and genomic DNA. Further, the inter-laboratory, intra-run, and inter-run reproducibility of the assay were examined. Analytical sensitivity analysis showed that Afirma GSC calls are tolerant to variation in RNA input amount (5-30 ng), and up to 75% dilution of malignant FNA material. Analytical specificity studies demonstrated Afirma GSC remains accurate in presence of up to 75% blood or 30% genomic DNA. The Afirma GSC results are highly reproducible across different operators, runs, reagent lots, and laboratories. Conclusion: The analytical robustness and reproducibility of the Afirma GSC test support its routine clinical use among thyroid nodules with indeterminant FNA cytology.

Identification of Hürthle cell cancers: solving a clinical challenge with genomic sequencing and a trio of machine learning algorithms.

BACKGROUND: Identification of Hürthle cell cancers by non-operative fine-needle aspiration biopsy (FNAB) of thyroid nodules is challenging. Resultingly, non-cancerous Hürthle lesions were conventionally distinguished from Hürthle cell cancers by histopathological examination of tissue following surgical resection. Reliance on histopathological evaluation requires patients to undergo surgery to obtain a diagnosis despite most being non-cancerous. It is highly desirable to avoid surgery and to provide accurate classification of benignity versus malignancy from FNAB preoperatively. In our first-generation algorithm, Gene Expression Classifier (GEC), we achieved this goal by using machine learning (ML) on gene expression features. The classifier is sensitive, but not specific due in part to the presence of non-neoplastic benign Hürthle cells in many FNAB. RESULTS: We sought to overcome this low-specificity limitation by expanding the feature set for ML using next-generation whole transcriptome RNA sequencing and called the improved algorithm the Genomic Sequencing Classifier (GSC). The Hürthle identification leverages mitochondrial expression and we developed novel feature extraction mechanisms to measure chromosomal and genomic level loss-of-heterozygosity (LOH) for the algorithm. Additionally, we developed a multi-layered system of cascading classifiers to sequentially triage Hürthle cell-containing FNAB, including: 1. presence of Hürthle cells, 2. presence of neoplastic Hürthle cells, and 3. presence of benign Hürthle cells. The final Hürthle cell Index utilizes 1048 nuclear and mitochondrial genes; and Hürthle cell Neoplasm Index leverages LOH features as well as 2041 genes. Both indices are Support Vector Machine (SVM) based. The third classifier, the GSC Benign/Suspicious classifier, utilizes 1115 core genes and is an ensemble classifier incorporating 12 individual models. CONCLUSIONS: The accurate algorithmic depiction of this complex biological system among Hürthle subtypes results in a dramatic improvement of classification performance; specificity among Hürthle cell neoplasms increases from 11.8% with the GEC to 58.8% with the GSC, while maintaining the same sensitivity of 89%.

Performance of a Genomic Sequencing Classifier for the Preoperative Diagnosis of Cytologically Indeterminate Thyroid Nodules.

Importance: Use of next-generation sequencing of RNA and machine learning algorithms can classify the risk of malignancy in cytologically indeterminate thyroid nodules to limit unnecessary diagnostic surgery. Objective: To measure the performance of a genomic sequencing classifier for cytologically indeterminate thyroid nodules. Design, Setting, and Participants: A blinded validation study was conducted on a set of cytologically indeterminate thyroid nodules collected by fine-needle aspiration biopsy between June 2009 and December 2010 from 49 academic and community centers in the United States. All patients underwent surgery without genomic information and were assigned a histopathology diagnosis by an expert panel blinded to all genomic information. There were 210 potentially eligible thyroid biopsy samples with Bethesda III or IV indeterminate cytopathology that constituted a cohort previously used to validate the gene expression classifier. Of these, 191 samples (91.0%) had adequate residual RNA for validation of the genomic sequencing classifier. Algorithm development and independent validation occurred between August 2016 and May 2017. Exposures: Thyroid nodule surgical histopathology diagnosis by an expert panel blinded to all genomic data. Main Outcomes and Measures: The primary end point was measurement of genomic sequencing classifier sensitivity, specificity, and negative and positive predictive values in biopsies from Bethesda III and IV nodules. The secondary end point was measurement of classifier performance in biopsies from Bethesda II, V, and VI nodules. Results: Of the 183 included patients, 142 (77.6%) were women, and the mean (range) age was 51.7 (22.0-85.0) years. The genomic sequencing classifier had a sensitivity of 91% (95% CI, 79-98) and a specificity of 68% (95% CI, 60-76). At 24% cancer prevalence, the negative predictive value was 96% (95% CI, 90-99) and the positive predictive value was 47% (95% CI, 36-58). Conclusions and Relevance: The genomic sequencing classifier demonstrates high sensitivity and accuracy for identifying benign nodules. Its 36% increase in specificity compared with the gene expression classifier potentially increases the number of patients with benign nodules who can safely avoid unnecessary diagnostic surgery.

Analytical performance of Envisia: a genomic classifier for usual interstitial pneumonia.

BACKGROUND: Clinical guidelines specify that diagnosis of interstitial pulmonary fibrosis (IPF) requires identification of usual interstitial pneumonia (UIP) pattern. While UIP can be identified by high resolution CT of the chest, the results are often inconclusive, making surgical lung biopsy necessary to reach a definitive diagnosis (Raghu et al., Am J Respir Crit Care Med 183(6):788-824, 2011). The Envisia genomic classifier differentiates UIP from non-UIP pathology in transbronchial biopsies (TBB), potentially allowing patients to avoid an invasive procedure (Brown et al., Am J Respir Crit Care Med 195:A6792, 2017). To ensure patient safety and efficacy, a laboratory developed test (LDT) must meet strict regulatory requirements for accuracy, reproducibility and robustness. The analytical characteristics of the Envisia test are assessed and reported here. METHODS: The Envisia test utilizes total RNA extracted from TBB samples to perform Next Generation RNA Sequencing. The gene count data from 190 genes are then input to the Envisia genomic classifier, a machine learning algorithm, to output either a UIP or non-UIP classification result. We characterized the stability of RNA in TBBs during collection and shipment, and evaluated input RNA mass and proportions on the limit of detection of UIP. We evaluated potentially interfering substances such as blood and genomic DNA. Intra-run, inter-run, and inter-laboratory reproducibility of test results were also characterized. RESULTS: RNA content within TBBs preserved in RNAprotect is stable for up to 14 days with no detectable change in RNA quality. The Envisia test is tolerant to variation in RNA input (5 to 30 ng), with no impact on classifier results. The Envisia test can tolerate dilution of non-UIP and UIP classification signals at the RNA level by up to 60% and 20%, respectively. Analytical specificity studies utilizing UIP and non-UIP samples mixed with genomic DNA (up to 30% relative input) demonstrated no impact to classifier results. The Envisia test tolerates up to 22% of blood contamination, well beyond the level observed in TBBs. The test is reproducible from RNA extraction through to Envisia test result (standard deviation of 0.20 for Envisia classification scores on > 7-unit scale). CONCLUSIONS: The Envisia test demonstrates the robust analytical performance required of an LDT. Envisia can be used to inform the diagnoses of patients with suspected IPF.

Detection of Clonal and Subclonal Copy-Number Variants in Cell-Free DNA from Patients with Breast Cancer Using a Massively Multiplexed PCR Methodology.

We demonstrate proof-of-concept for the use of massively multiplexed PCR and next-generation sequencing (mmPCR-NGS) to identify both clonal and subclonal copy-number variants (CNVs) in circulating tumor DNA. This is the first report of a targeted methodology for detection of CNVs in plasma. Using an in vitro model of cell-free DNA, we show that mmPCR-NGS can accurately detect CNVs with average allelic imbalances as low as 0.5%, an improvement over previously reported whole-genome sequencing approaches. Our method revealed differences in the spectrum of CNVs detected in tumor tissue subsections and matching plasma samples from 11 patients with stage II breast cancer. Moreover, we showed that liquid biopsies are able to detect subclonal mutations that may be missed in tumor tissue biopsies. We anticipate that this mmPCR-NGS methodology will have broad applicability for the characterization, diagnosis, and therapeutic monitoring of CNV-enriched cancers, such as breast, ovarian, and lung cancer.

Improved specificity of TALE-based genome editing using an expanded RVD repertoire.

Transcription activator-like effector (TALE) proteins have gained broad appeal as a platform for targeted DNA recognition, largely owing to their simple rules for design. These rules relate the base specified by a single TALE repeat to the identity of two key residues (the repeat variable diresidue, or RVD) and enable design for new sequence targets via modular shuffling of these units. A key limitation of these rules is that their simplicity precludes options for improving designs that are insufficiently active or specific. Here we address this limitation by developing an expanded set of RVDs and applying them to improve the performance of previously described TALEs. As an extreme example, total conversion of a TALE nuclease to new RVDs substantially reduced off-target cleavage in cellular studies. By providing new RVDs and design strategies, these studies establish options for developing improved TALEs for broader application across medicine and biotechnology.

Expanding the scope of noninvasive prenatal testing: detection of fetal microdeletion syndromes.

OBJECTIVE: The purpose of this study was to estimate the performance of a single-nucleotide polymorphism (SNP)-based noninvasive prenatal test for 5 microdeletion syndromes. STUDY DESIGN: Four hundred sixty-nine samples (358 plasma samples from pregnant women, 111 artificial plasma mixtures) were amplified with the use of a massively multiplexed polymerase chain reaction, sequenced, and analyzed with the use of the Next-generation Aneuploidy Test Using SNPs algorithm for the presence or absence of deletions of 22q11.2, 1p36, distal 5p, and the Prader-Willi/Angelman region. RESULTS: Detection rates were 97.8% for a 22q11.2 deletion (45/46) and 100% for Prader-Willi (15/15), Angelman (21/21), 1p36 deletion (1/1), and cri-du-chat syndromes (24/24). False-positive rates were 0.76% for 22q11.2 deletion syndrome (3/397) and 0.24% for cri-du-chat syndrome (1/419). No false positives occurred for Prader-Willi (0/428), Angelman (0/442), or 1p36 deletion syndromes (0/422). CONCLUSION: SNP-based noninvasive prenatal microdeletion screening is highly accurate. Because clinically relevant microdeletions and duplications occur in >1% of pregnancies, regardless of maternal age, noninvasive screening for the general pregnant population should be considered.

A high-throughput screen for teratogens using human pluripotent stem cells.

There is need in the pharmaceutical and chemical industries for high-throughput human cell-based assays for identifying hazardous chemicals, thereby reducing the overall reliance on animal studies for predicting the risk of toxic responses in humans. Despite instances of human-specific teratogens such as thalidomide, the use of human cell-teratogenicity assays has just started to be explored. Herein, a human pluripotent stem cell test (hPST) for identifying teratogens is described, benchmarking the in vitro findings to traditional preclinical toxicology teratogenicity studies and when available to teratogenic outcomes in humans. The hPST method employs a 3-day monolayer directed differentiation of human embryonic stem cells. The teratogenic risk of a compound is gauged by measuring the reduction in nuclear translocation of the transcription factor SOX17 in mesendodermal cells. Decreased nuclear SOX17 in the hPST model was strongly correlated with in vivo teratogenicity. Specifically, 71 drug-like compounds with known in vivo effects, including thalidomide, were examined in the hPST. A threshold of 5 µM demonstrated 94% accuracy (97% sensitivity and 92% specificity). Furthermore, 15 environmental toxicants with physicochemical properties distinct from small molecule pharmaceutical agents were examined and a similarly strong concordance with teratogenicity outcomes from in vivo studies was observed. Finally, to assess the suitability of the hPST for high-throughput screens, a small library of 300 kinase inhibitors was tested, demonstrating the hPST platform's utility for interrogating teratogenic mechanisms and drug safety prediction. Thus, the hPST assay is a robust predictor of teratogenicity and appears to be an improvement over existing in vitro models.

Eri1 regulates microRNA homeostasis and mouse lymphocyte development and antiviral function.

Natural killer (NK) cells play a critical role in early host defense to infected and transformed cells. Here, we show that mice deficient in Eri1, a conserved 3'-to-5' exoribonuclease that represses RNA interference, have a cell-intrinsic defect in NK-cell development and maturation. Eri1(-/-) NK cells displayed delayed acquisition of Ly49 receptors in the bone marrow (BM) and a selective reduction in Ly49D and Ly49H activating receptors in the periphery. Eri1 was required for immune-mediated control of mouse CMV (MCMV) infection. Ly49H(+) NK cells deficient in Eri1 failed to expand efficiently during MCMV infection, and virus-specific responses were also diminished among Eri1(-/-) T cells. We identified miRNAs as the major endogenous small RNA target of Eri1 in mouse lymphocytes. Both NK and T cells deficient in Eri1 displayed a global, sequence-independent increase in miRNA abundance. Ectopic Eri1 expression rescued defective miRNA expression in mature Eri1(-/-) T cells. Thus, mouse Eri1 regulates miRNA homeostasis in lymphocytes and is required for normal NK-cell development and antiviral immunity.

Targeted gene addition to a predetermined site in the human genome using a ZFN-based nicking enzyme.

Zinc-finger nucleases (ZFNs) drive highly efficient genome editing by generating a site-specific DNA double-strand break (DSB) at a predetermined site in the genome. Subsequent repair of this break via the nonhomologous end-joining (NHEJ) or homology-directed repair (HDR) pathways results in targeted gene disruption or gene addition, respectively. Here, we report that ZFNs can be engineered to induce a site-specific DNA single-strand break (SSB) or nick. Using the CCR5-specific ZFNs as a model system, we show that introduction of a nick at this target site stimulates gene addition using a homologous donor template but fails to induce significant levels of the small insertions and deletions (indels) characteristic of repair via NHEJ. Gene addition by these CCR5-targeted zinc finger nickases (ZFNickases) occurs in both transformed and primary human cells at efficiencies of up to ∼1%-8%. Interestingly, ZFNickases targeting the AAVS1 "safe harbor" locus revealed similar in vitro nicking activity, a marked reduction of indels characteristic of NHEJ, but stimulated far lower levels of gene addition-suggesting that other, yet to be identified mediators of nick-induced gene targeting exist. Introduction of site-specific nicks at distinct endogenous loci provide an important tool for the study of DNA repair. Moreover, the potential for a SSB to direct repair pathway choice (i.e., HDR but not NHEJ) may prove advantageous for certain therapeutic applications such as the targeted correction of human disease-causing mutations.

Determination of the human cardiomyocyte mRNA and miRNA differentiation network by fine-scale profiling.

To gain insight into the molecular regulation of human heart development, a detailed comparison of the mRNA and miRNA transcriptomes across differentiating human-induced pluripotent stem cell (hiPSC)-derived cardiomyocytes and biopsies from fetal, adult, and hypertensive human hearts was performed. Gene ontology analysis of the mRNA expression levels of the hiPSCs differentiating into cardiomyocytes revealed 3 distinct groups of genes: pluripotent specific, transitional cardiac specification, and mature cardiomyocyte specific. Hierarchical clustering of the mRNA data revealed that the transcriptome of hiPSC cardiomyocytes largely stabilizes 20 days after initiation of differentiation. Nevertheless, analysis of cells continuously cultured for 120 days indicated that the cardiomyocytes continued to mature toward a more adult-like gene expression pattern. Analysis of cardiomyocyte-specific miRNAs (miR-1, miR-133a/b, and miR-208a/b) revealed an miRNA pattern indicative of stem cell to cardiomyocyte specification. A biostatistitical approach integrated the miRNA and mRNA expression profiles revealing a cardiomyocyte differentiation miRNA network and identified putative mRNAs targeted by multiple miRNAs. Together, these data reveal the miRNA network in human heart development and support the notion that overlapping miRNA networks re-enforce transcriptional control during developmental specification.

MicroRNA-29 regulates T-box transcription factors and interferon-γ production in helper T cells.

MicroRNA (miRNA)-deficient helper T cells exhibit abnormal IFN-γ production and decreased proliferation. However, the contributions of individual miRNAs to this phenotype remain poorly understood. We conducted a screen for miRNA function in primary T cells and identified individual miRNAs that rescue the defects associated with miRNA deficiency. Multiple members of the miR-17 and miR-92 families enhanced miRNA-deficient T cell proliferation whereas miR-29 largely corrected their aberrant interferon-γ (IFN-γ) expression. Repression of IFN-γ production by miR-29 involved direct targeting of both T-bet and Eomes, two transcription factors known to induce IFN-γ production. Although not usually expressed at functionally relevant amounts in helper T cells, Eomes was abundant in miRNA-deficient cells and was upregulated after miR-29 inhibition in wild-type cells. These results demonstrate that miR-29 regulates helper T cell differentiation by repressing multiple target genes, including at least two that are independently capable of inducing the T helper 1 (Th1) cell gene expression program.

A role for noncanonical microRNAs in the mammalian brain revealed by phenotypic differences in Dgcr8 versus Dicer1 knockouts and small RNA sequencing.

Noncanonical microRNAs (miRNAs) and endogenous small interfering RNAs (endo-siRNAs) are distinct subclasses of small RNAs that bypass the DGCR8/DROSHA Microprocessor but still require DICER1 for their biogenesis. What role, if any, they have in mammals remains unknown. To identify potential functional properties for these subclasses, we compared the phenotypes resulting from conditional deletion of Dgcr8 versus Dicer1 in post-mitotic neurons. The loss of Dicer1 resulted in an earlier lethality, more severe structural abnormalities, and increased apoptosis relative to that from Dgcr8 loss. Deep sequencing of small RNAs from the hippocampus and cortex of the conditional knockouts and control littermates identified multiple noncanonical microRNAs that were expressed at high levels in the brain relative to other tissues, including mirtrons and H/ACA snoRNA-derived small RNAs. In contrast, we found no evidence for endo-siRNAs in the brain. Taken together, our findings provide evidence for a diverse population of highly expressed noncanonical miRNAs that together are likely to play important functional roles in post-mitotic neurons.

Estimating the risk of drug-induced proarrhythmia using human induced pluripotent stem cell-derived cardiomyocytes.

Improved in vitro systems for predicting drug-induced toxicity are needed in the pharmaceutical and biotechnology industries to decrease late-stage drug attrition. One unmet need is an early screen for cardiotoxicity, which accounts for about one third of safety-based withdrawn pharmaceuticals. Herein, the first published report of a high-throughput functional assay employing a monolayer of beating human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) is described, detailing a model that accurately detects drug-induced cardiac abnormalities. Using 96-well plates with interdigitated electrode arrays that assess impedance, the rhythmic, synchronous contractions of the iPSC-CMs were detected. Treatment of the iPSC-CMs with 28 different compounds with known cardiac effects resulted in compound-specific changes in the beat rate and/or the amplitude of the impedance measurement. Changes in impedance for the compounds tested were comparable with the results from a related technology, electric field potential assessment obtained from microelectrode arrays. Using the results from the set of compounds, an index of drug-induced arrhythmias was calculated, enabling the determination of a drug's proarrhythmic potential. This system of interrogating human cardiac function in vitro opens new opportunities for predicting cardiac toxicity and studying cardiac biology.

Mammalian microRNAs: experimental evaluation of novel and previously annotated genes.

MicroRNAs (miRNAs) are small regulatory RNAs that derive from distinctive hairpin transcripts. To learn more about the miRNAs of mammals, we sequenced 60 million small RNAs from mouse brain, ovary, testes, embryonic stem cells, three embryonic stages, and whole newborns. Analysis of these sequences confirmed 398 annotated miRNA genes and identified 108 novel miRNA genes. More than 150 previously annotated miRNAs and hundreds of candidates failed to yield sequenced RNAs with miRNA-like features. Ectopically expressing these previously proposed miRNA hairpins also did not yield small RNAs, whereas ectopically expressing the confirmed and newly identified hairpins usually did yield small RNAs with the classical miRNA features, including dependence on the Drosha endonuclease for processing. These experiments, which suggest that previous estimates of conserved mammalian miRNAs were inflated, provide a substantially revised list of confidently identified murine miRNAs from which to infer the general features of mammalian miRNAs. Our analyses also revealed new aspects of miRNA biogenesis and modification, including tissue-specific strand preferences, sequential Dicer cleavage of a metazoan precursor miRNA (pre-miRNA), consequential 5' heterogeneity, newly identified instances of miRNA editing, and evidence for widespread pre-miRNA uridylation reminiscent of miRNA regulation by Lin28.