Analysis of 5-Azacytidine Resistance Models Reveals a Set of Targetable Pathways

The mechanisms by which myelodysplastic syndrome (MDS) cells resist the effects of hypomethylating agents (HMA) are currently the subject of intensive research. A better understanding of mechanisms by which the MDS cell becomes to tolerate HMA and progresses to acute myeloid leukemia (AML) requires the development of new cellular models. From MDS/AML cell lines we developed a model of 5-azacytidine (AZA) resistance whose stability was validated by a transplantation approach into immunocompromised mice. When investigating mRNA expression and DNA variants of the AZA resistant phenotype we observed deregulation of several cancer-related pathways including the phosphatidylinosito-3 kinase signaling. We have further shown that these pathways can be modulated by specific inhibitors that, while blocking the proliferation of AZA resistant cells, are unable to increase their sensitivity to AZA. Our data reveal a set of molecular mechanisms that can be targeted to expand therapeutic options during progression on AZA therapy.

Identification of Somatic Mutations From Bulk and Single-Cell Sequencing Data

Somatic mutations are DNA variants that occur after the fertilization of zygotes and accumulate during the developmental and aging processes in the human lifespan. Somatic mutations have long been known to cause cancer, and more recently have been implicated in a variety of non-cancer diseases. The patterns of somatic mutations, or mutational signatures, also shed light on the underlying mechanisms of the mutational process. Advances in next-generation sequencing over the decades have enabled genome-wide profiling of DNA variants in a high-throughput manner; however, unlike germline mutations, somatic mutations are carried only by a subset of the cell population. Thus, sensitive bioinformatic methods are required to distinguish mutant alleles from sequencing and base calling errors in bulk tissue samples. An alternative way to study somatic mutations, especially those present in an extremely small number of cells or even in a single cell, is to sequence single-cell genomes after whole-genome amplification (WGA); however, it is critical and technically challenging to exclude numerous technical artifacts arising during error-prone and uneven genome amplification in current WGA methods. To address these challenges, multiple bioinformatic tools have been developed. In this review, we summarize the latest progress in methods for identification of somatic mutations and the challenges that remain to be addressed in the future.

Integrated Proteomic and Metabolomic Analyses of the Mitochondrial Neurodegenerative Disease MELAS

MELAS (mitochondrial encephalomyopathy, lactic acidosis, stroke-like episodes) is a progressive neurodegenerative disease caused by pathogenic mitochondrial DNA variants. The pathogenic mechanism of MELAS remains enigmatic due to the exceptional clinical...

MaveDB v2: a curated community database with over three million variant effects from multiplexed functional assays

A central problem in genomics is understanding the effect of individual DNA variants. Multiplexed Assays of Variant Effect (MAVEs) can help address this challenge by measuring all possible single nucleotide variant effects in a gene or regulatory sequence simultaneously. Here we describe MaveDB v2, which has become the database of record for MAVEs. MaveDB now contains a large fraction of published studies, comprising over two hundred datasets and three million variant effect measurements. We created tools and APIs to streamline data submission and access, transforming MaveDB into a hub for the analysis and dissemination of these impactful datasets.

trioPhaser: using Mendelian inheritance logic to improve genomic phasing of trios

Background When analyzing DNA sequence data of an individual, knowing which nucleotide was inherited from each parent can be beneficial when trying to identify certain types of DNA variants. Mendelian inheritance logic can be used to accurately phase (haplotype) the majority (67–83%) of an individual's heterozygous nucleotide positions when genotypes are available for both parents (trio). However, when all members of a trio are heterozygous at a position, Mendelian inheritance logic cannot be used to phase. For such positions, a computational phasing algorithm can be used. Existing phasing algorithms use a haplotype reference panel, sequencing reads, and/or parental genotypes to phase an individual; however, they are limited in that they can only phase certain types of variants, require a specific genotype build, require large amounts of storage capacity, and/or require long run times. We created trioPhaser to address these challenges. Results trioPhaser uses gVCF files from an individual and their parents as initial input, and then outputs a phased VCF file. Input trio data are first phased using Mendelian inheritance logic. Then, the positions that cannot be phased using inheritance information alone are phased by the SHAPEIT4 phasing algorithm. Using whole-genome sequencing data of 52 trios, we show that trioPhaser, on average, increases the total number of phased positions by 21.0% and 10.5%, respectively, when compared to the number of positions that SHAPEIT4 or Mendelian inheritance logic can phase when either is used alone. In addition, we show that the accuracy of the phased calls output by trioPhaser are similar to linked-read and read-backed phasing. Conclusion trioPhaser is a containerized software tool that uses both Mendelian inheritance logic and SHAPEIT4 to phase trios when gVCF files are available. By implementing both phasing methods, more variant positions are phased compared to what either method is able to phase alone.

Multiple epistatic DNA variants in a single gene affect gene expression in trans

DNA variants that alter gene expression in trans are important sources of phenotypic variation. Nevertheless, the identity of trans-acting variants remains poorly understood. Single causal variants in several genes have been reported to affect the expression of numerous distant genes in trans. Whether these simple molecular architectures are representative of trans-acting variation is unknown. Here, we studied the large RAS signaling regulator gene IRA2, which contains variants with extensive trans-acting effects on gene expression in the yeast Saccharomyces cerevisiae. We used systematic CRISPR-based genome engineering and a sensitive phenotyping strategy to dissect causal variants to the nucleotide level. In contrast to the simple molecular architectures known so far, IRA2 contained at least seven causal nonsynonymous variants. The effects of these variants were modulated by non-additive, epistatic interactions. Two variants at the 5′-end affected gene expression and growth only when combined with a third variant that also had no effect in isolation. Our findings indicate that the molecular basis of trans-acting genetic variation may be considerably more complex than previously appreciated.

Genetics of Thoroughbred Racehorse Performance

Thoroughbred horses have been selected for racing performance for more than 400 years. Despite continued selection, race times have not improved significantly during the past 60 years, raising the question of whether genetic variation for racing performance still exists. Studies using phenotypes such as race time, money earned, and handicapping, however, demonstrate that there is extensive variation within these traits and that they are heritable. Even so, these are poor measures of racing success since Thoroughbreds race at different ages and distances and on different types of tracks, and some may not race at all. With the advent of genomic tools, DNA variants are being identified that contribute to racing success. Aside from strong associations for myostatin variants with best racing distance, weak to modest associations with racing phenotypes are reported for other genomic regions. These data suggest that diverse genetic strategies have contributed to producing a successful racehorse, and genetic variation contributing to athleticism remains important. Expected final online publication date for the Annual Review of Animal Biosciences, Volume 10 is February 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Identification of candidate genes and enriched biological functions for feed efficiency traits by integrating plasma metabolites and imputed whole genome sequence variants in beef cattle

Background Feed efficiency is one of the key determinants of beef industry profitability and sustainability. However, the cellular and molecular background behind feed efficiency is largely unknown. This study combines imputed whole genome DNA variants and 31 plasma metabolites to dissect genes and biological functions/processes that are associated with residual feed intake (RFI) and its component traits including daily dry matter intake (DMI), average daily gain (ADG), and metabolic body weight (MWT) in beef cattle. Results Regression analyses between feed efficiency traits and plasma metabolites in a population of 493 crossbred beef cattle identified 5 (L-valine, lysine, L-tyrosine, L-isoleucine, and L-leucine), 4 (lysine, L-lactic acid, L-tyrosine, and choline), 1 (citric acid), and 4 (L-glutamine, glycine, citric acid, and dimethyl sulfone) plasma metabolites associated with RFI, DMI, ADG, and MWT (P-value < 0.1), respectively. Combining the results of metabolome-genome wide association studies using 10,488,742 imputed SNPs, 40, 66, 15, and 40 unique candidate genes were identified as associated with RFI, DMI, ADG, and MWT (P-value < 1 × 10−5), respectively. These candidate genes were found to be involved in some key metabolic processes including metabolism of lipids, molecular transportation, cellular function and maintenance, cell morphology and biochemistry of small molecules. Conclusions This study identified metabolites, candidate genes and enriched biological functions/processes associated with RFI and its component traits through the integrative analyses of metabolites with phenotypic traits and DNA variants. Our findings could enhance the understanding of biochemical mechanisms of feed efficiency traits and could lead to improvement of genomic prediction accuracy via incorporating metabolite data.

MitoScape: A big-data, machine-learning platform for obtaining mitochondrial DNA from next-generation sequencing data

The growing number of next-generation sequencing (NGS) data presents a unique opportunity to study the combined impact of mitochondrial and nuclear-encoded genetic variation in complex disease. Mitochondrial DNA variants and in particular, heteroplasmic variants, are critical for determining human disease severity. While there are approaches for obtaining mitochondrial DNA variants from NGS data, these software do not account for the unique characteristics of mitochondrial genetics and can be inaccurate even for homoplasmic variants. We introduce MitoScape, a novel, big-data, software for extracting mitochondrial DNA sequences from NGS. MitoScape adopts a novel departure from other algorithms by using machine learning to model the unique characteristics of mitochondrial genetics. We also employ a novel approach of using rho-zero (mitochondrial DNA-depleted) data to model nuclear-encoded mitochondrial sequences. We showed that MitoScape produces accurate heteroplasmy estimates using gold-standard mitochondrial DNA data. We provide a comprehensive comparison of the most common tools for obtaining mtDNA variants from NGS and showed that MitoScape had superior performance to compared tools in every statistically category we compared, including false positives and false negatives. By applying MitoScape to common disease examples, we illustrate how MitoScape facilitates important heteroplasmy-disease association discoveries by expanding upon a reported association between hypertrophic cardiomyopathy and mitochondrial haplogroup T in men (adjusted p-value = 0.003). The improved accuracy of mitochondrial DNA variants produced by MitoScape will be instrumental in diagnosing disease in the context of personalized medicine and clinical diagnostics.

A Novel NGS-Based Simultaneous Detection of DNA and RNA Biomarkers Using Total Nucleic Acid (TNA) for Acute Lymphocytic Leukemia (ALL)

Background: Acute Lymphocytic Leukemia (ALL) is the most common childhood cancer and accounts for about a quarter of adult acute leukemias. Current NCCN recommendations for clinical testing for risk stratification and treatment guidance include karyotyping, FISH testing for translocations, and RT-PCR for gene fusions and sequencing for DNA mutations detection. Most NGS based approaches test DNA mutations and RNA fusions separately, thereby requiring higher input material and multiple workflows adding to the cost and turn-around-time. An NGS based assay for the detection of DNA variants (NeoGenomics Heme NGS assay) in heme malignancies using Total Nucleic Acid (TNA) is already available in our clinical laboratory and complements FISH based fusion detection and karyotyping but an integral assay to detect both DNA and RNA alterations with a simple workflow for ALL is needed. Methods: We used TNA or RNA spiked-in with DNA to simulate TNA samples, extracted from 93 bone marrow and peripheral blood samples from patients and healthy donors, along with commercial fusion reference myeloid samples Seraseq Myeloid Fusion RNA Mix (SeraCare Inc.) controls. DNA/RNA libraries were prepared using a custom amplicon based Multimodal NGS panel (Qiagen Inc.) targeting 297 genes and 213 genes (select exons) for DNA and RNA fusion detection, respectively. The enriched dual indexed amplicon libraries were sequenced on an Illumina NovaSeq 6000. The sequence data was processed with a customized bioinformatic pipeline for DNA variant as well as a novel machine learning algorithm for RNA fusion detection. We analyzed sensitivity, specificity, accuracy, reproducibility, and repeatability for clinical use. The DNA variants were orthogonally confirmed using other NGS assays, and the RNA fusions were confirmed on an RNA-seq Archer assay or RT-Sanger confirmation assays. Results: Here, we developed and validated a single tube comprehensive NGS panel using a custom multimodal chemistry that uses TNA as input for simultaneous dual detection of DNA and RNA abnormalities in ALL patients' samples. We performed the analytical validation of our Heme NGS assay for the RNA panel to detect fusions in ALL, using TNA input for comprehensive DNA and RNA mutation detection. The fusion concordance was 95% for the RNA fusion panel. The assay detected BCR-ABL1 (7/7), ETV6-RUNX1 (1/1), KMT2A fusions (4/5), TCF3-PBX1 (1/1), and PCM1-JAK2(1/1). The specificity was determined at 100% using a set of 42 fusion negative samples. The limit of detection (LOD) was analyzed using serial dilutions to up to 3 log reduction (LR) using a the Seraseq Myeloid Fusion sample. The fusions were detected down to 1 LR. The reproducibility was tested using a positive fusion and Seraseq samples across three runs and was reported at 100%. Next, a small cohort of ALL samples (n=8) was included as part of this study to simultaneously evaluate DNA and RNA mutations. We detected pathogenic DNA variants in genes previously reported in ALL that included NOTCH1, PTEN, FLT3, IKZF1, JAK1, JAK2, KRAS, NF1, PAX5, U2AF1, TP53, and also RNA fusion BCR-ABL1, and the results were confirmed by an orthogonal NGS assay (NexCourse and RNA-Seqv1 for fusions). One sample carrying a BCR-ABL1 fusion (detected by RNA panel) also harbored mutations in IKZF1 in DNA (detected by DNA panel) that is reported as unfavorable prognostic biomarker for Ph-Like ALL demonstrating comprehensive panel could identify multiple variants within the same sample, demonstrating the advantage DNA+RNA testing has over the classical single gene FISH/RT-PCR testing for the efficient risk stratification and treatment in ALL patients. Conclusions: In this study, we demonstrated that the single tube TNA based NeoGenomics NGS assay can simultaneously detect the DNA and RNA biomarkers associated with ALL for improved diagnostic and prognostic recommendations. The single-tube assay for detection of both RNA fusions and DNA variants using the same sample could offer comprehensive and cost-effective solution for clinical laboratory test for ALL patient care. This is a promising approach that might be used as a dual DNA/RNA alterations detection on other hematological neoplasia. Disclosures Ramesh: Neo Genomics Laboratories: Current Employment. Koo: Neo Genomics Laboratories: Current Employment. Kang: Neo Genomics Laboratories: Current Employment. Ghosal: NeoGenomics Laboratories: Current Employment. Alarcon: NeoGenomics Laboratories: Current Employment. Gyuris: Neo Genomics Laboratories: Current Employment. Jung: NeoGenomics Laboratories, Inc.: Current Employment. Magnan: NeoGenomics Laboratories, Inc.: Current Employment. Nam: NeoGenomics Laboratories, Inc.: Current Employment. Thomas: NeoGenomics Laboratories, Inc.: Current Employment. Fabunan: NeoGenomics Laboratories, Inc.: Current Employment. Petersen: Neo Genomics Laboratories: Current Employment. Lopez-Diaz: NeoGenomics Laboratories, Inc.: Current Employment. Bender: NeoGenomics Laboratories, Inc.: Current Employment. Agersborg: NeoGenomics Laboratories, Inc.: Current Employment. Ye: Neo Genomics Laboratories: Current Employment. Funari: NeoGenomics Laboratories, Inc.: Current Employment.