High-throughput Genomic Methods Research Articles

Phylogenetic insights into Central European Chorthippus and Pseudochorthippus (Orthoptera: Acrididae) species using ddRADseq data

The evolution of several orthopteran groups, especially within the grasshopper family Acrididae, remains poorly understood. This is particularly true for the subfamily Gomphocerinae, which comprises cryptic sympatric and syntopic species. Previous mitochondrial studies have highlighted major discrepancies between taxonomic and phylogenetic hypotheses, thereby emphasizing the necessity of genome-wide approaches.In this study, we employ double-digest restriction site-associated DNA sequencing (ddRADseq) to reconstruct the evolution of Central European Chorthippus and Pseudochorthippus species, especially C.smardai, P.tatrae and the C.biguttulus group.Our phylogenomic analyses recovered deep discordance with mitochondrial DNA barcoding, emphasizing its unreliability in Gomphocerinae grasshoppers. Specifically, our data robustly distinguished the C.biguttulus group and confirmed the distinctiveness of C.eisentrauti, also shedding light on its presence in the Berchtesgaden Alps. Moreover, our results support the reclassification of C.smardai to the genus Pseudochorthippus and of P.tatrae to the genus Chorthippus.Our study demonstrates the efficiency of high-throughput genomic methods such as RADseq without prior optimization to elucidate the complex evolution of grasshopper radiations with direct taxonomic implications. While RADseq has predominantly been utilized for population genomics and within-genus phylogenomics, its application extends to resolve relationships between deeply-diverged clades representative of distinct genera.

A novel heterozygous truncating variant in the AGO1 gene in an Iranian family with schizophrenia as an unreported symptom.

Intellectual disability (ID) and autism spectrum disorders (ASDs) are the most common developmental disorders in humans. Combined, they affect between 3% and 5% of the population. Although high-throughput genomic methods are rapidly increasing the pool of ASD genes, many cases remain idiopathic. AGO1 is one of the less-known genes related to ID/ASD. This gene encodes a core member protein of the RNA-induced silencing complex, which suppresses mRNA expression through cleavage, degradation, and/or translational repression. Generally, patients with defects in the AGO1 gene manifest varying degrees of ID, speech delay, and autistic behaviors. Herein, we used whole-exome sequencing (WES) to investigate an Iranian family with two affected members one of whom manifested ID and autism and the other showed borderline ID and schizophrenia. WES analysis identified a novel heterozygous truncating variant (NM_012199.5:c.1298G>A, p.Trp433Ter) in the AGO1 gene that co-segregated with the phenotypes using Sanger sequencing. Moreover, the structural analysis showed that due to this variant, two critical domains (Mid and PIWI) of the AGO1 protein have been lost, which has a detrimental effect on the protein's function and structure. To the best of our knowledge, schizophrenia has not been reported in patients with AGO1 deficiency, which is a novel phenotypic finding that expands the AGO1-related behavioral disorders. Moreover, this study's findings determined that patients with the same variant in the AGO1 gene may show heterogeneity in manifested phenotypes.

Assessing Molecular Diversity in Native and Introduced Populations of Red Wood Ant Formica paralugubris.

The Formica rufa group comprises several ant species which are collectively referred to as "red wood ants" and play key roles in boreal forest ecosystems, where they are ecologically dominant and greatly influence habitat dynamics. Owing to their intense predatory activity, some of these species are used as biocontrol agents against several forest insect pests and for this aim in Italy, nearly 6000 ant nests were introduced from their native areas in the Alps to several Appeninic sites during the last century. In this work, we assessed and compared the genetic variability and structure of native and introduced populations of F. paralugubris, thus evaluating the extent of genetic drift that may have occurred since the time of introduction, using amplified fragment length polymorphism (AFLP) markers. PCR amplification with a fam_EcoRI-TAC/MseI-ATG primers combination produced a total of 147 scorable bands, with 17 identified as outlier loci. The genetic variation was higher in the introduced population compared to the native ones that, on the other hand, showed a higher diversity between nests. AMOVA results clearly pointed out that the overall genetic structure was dominated by among-worker variation, considering all populations, the Alpine vs. Apennine groups and the comparison among native and related introduced populations (all ranging between 77.84% and 79.84%). Genetic analyses unveiled the existence of six main different groups that do not entirely mirror their geographic subdivision, pointing towards a wide admixture between populations, but, at the same time, rapid diversification of some Apennine populations. Future studies based on high-throughput genomic methods are needed to obtain a thorough understanding of the effects of environmental pressure on the genetic structure and mating system of these populations.

The Prediction of a 3-Protein-Based Model on the Prognosis of Head and Neck Squamous Cell Carcinoma.

Background Head and neck squamous cell carcinoma (HNSCC) is one of the commonest malignant tumors. Using high-throughput genomic methods, RNA-based diagnostic and prognostic models for HNSCC with potential clinical value have been developed. However, the clinical utility and reproducibility of these models are uncertain. Because the complex regulatory processes occurring after mRNA is transcribed, the abundance of proteins in a cell can never be fully predicted or explained by their corresponding mRNA expression. We aimed to assume and verify a novel protein signature for checking the HNSCC patients' prognosis. Methods The functional proteomic data of 332 HNSCC cases were collected from The Cancer Proteome Atlas (TCPA), and the related follow-up and clinical data were acquired from The Cancer Genome Atlas (TCGA). This study adopted multivariate and univariate Cox regression analysis, Akaike Information Criterion, receiver operating characteristic (ROC) analysis, and Kaplan-Meier method. Results Patients' clinical features in both sets were comparable (all, P > 0.05). The area under the ROC curve (AUC) for the 3-protein signature (X4EBP1_pT37T46, HER3_pY1289, and NF2) in the test set was 0.655 and in the combined cohort (all 332 patients combined) was 0.699. In addition, the 3-protein signature exhibited better predictive value for the survival of HNSCC patients as in comparison with conventional clinical factors like age, gender, tumor stage, and smoking history (TNM stage). Conclusion The 3-protein signature developed in this study exhibits good performance in predicting the overall survival of with HNSCC patients. The 3-protein signature exhibited better predictive value for survival than conventional clinical factors just like gender, TNM stage, smoking history, and age.

Influence of De novo mutation in autism

Autism and autism spectrum disorders (ASD) affects the areas of social awareness and interaction, verbal and nonverbal communication, and behaviors and interests, which are grouped among neurobehavioral disorders. One in 68 children is affected with ASD according to the estimate from the Center for Disease Control and Prevention's autism and developmental disabilities monitoring network and an increasing prevalence observed worldwide. A characteristic heterogeneity in ASD determined genetic variability as a major contributor. Whole-exome sequencing of ASD individuals revealed that discrete de novo mutation (single-nucleotide variation or small indels) also contribute to the overall genetic risk of ASD apart from rare genetic variation affecting single nucleotides of protein-coding DNA or rare genomic copy number variants as assessed through other high-throughput genomic methods. These genes are involved in synaptic transmission and regulated during brain development by acting upstream or downstream of Wingless- related integration site (WNT), bone morphogenetic proteins/transforming growth factor-β, sonic hedgehog, fibroblast growth factor, and retinoic acid signaling pathways. The current review focuses on genes involved in synaptic function, undergoing de novo mutation leading to ASD condition.

Ensemble multi‐objective evolutionary algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps

Many methods aim to use data, especially data about gene expression based on high throughput genomic methods, to identify complicated regulatory relationships between genes. The authors employ a simple but powerful tool, called fuzzy cognitive maps (FCMs), to accurately reconstruct gene regulatory networks (GRNs). Many automated methods have been carried out for training FCMs from data. These methods focus on simulating the observed time sequence data, but neglect the optimisation of network structure. In fact, the FCM learning problem is multi-objective which contains network structure information, thus, the authors propose a new algorithm combining ensemble strategy and multi-objective evolutionary algorithm (MOEA), called EMOEA FCM -GRN, to reconstruct GRNs based on FCMs. In EMOEA FCM -GRN, the MOEA first learns a series of networks with different structures by analysing historical data simultaneously, which is helpful in finding the target network with distinct optimal local information. Then, the networks which receive small simulation error on the training set are selected from the Pareto front and an efficient ensemble strategy is provided to combine these selected networks to the final network. The experiments on the DREAM4 challenge and synthetic FCMs illustrate that EMOEA FCM -GRN is efficient and able to reconstruct GRNs accurately.

Abstract A190: Mechanisms governing Foxp3-dependent and -independent gene expression in regulatory T-cells in evolutionary distant mice

Abstract T regulatory (Treg) cells expressing the forkhead box transcription factor (Foxp3) are important regulators of immunity and play a key role in cancer and metastasis by controlling the activity of effector T-cells. Lineage-specifying transcription factors (TFs) establish cell identity and maintain cell specific transcriptional programs. Foxp3 is a key master TF controlling various Treg cell developmental and functional modalities. Foxp3 deficiency in humans and mice causes a life threatening autoimmune disease, and the enforced expression of Foxp3 in CD4+ T-cells confers suppressor functions. However, our understanding of Treg cell regulation during differentiation in the thymus, and in mature Treg cells in the periphery, whether Foxp3 dependent or independent, is still lacking. Single-nucleotide polymorphism (SNPs) may occur within coding genes or noncoding genomic regulatory areas and can have severe phenotypic consequences. We have recently attempted to link SNPs with differential Treg transcriptomic and epigenetic features in humans. However, such studies are limited by their intrinsic correlative nature, and by the limited number of SNPs. Here we use evolutionally distant mice strains C57Bl/6 (B6), CAST/EiJ (Cast) and SPRET/EiJ (Spret), to identify allele-specific imbalances and functional differences in the transcriptional and epigenetic programs during differentiation and in various conditions of Treg cells and their precursors. Using Foxp3 reporter and conditional knockout (KO) systems combined with interbreeding of the evolutionary distant mice strains in vivo, we explore the genetic and molecular mechanisms of Treg cell differentiation and function, employing high-throughput genomic methods including RNA-seq, ATAC-seq and Chip-seq. Such studies may pave a way for the development of novel therapeutic approaches to treat cancer and metastasis. Citation Format: Ariella Glasner, Yi Zhong, Joris Van Der Veeken, Alexander Rudensky. Mechanisms governing Foxp3-dependent and -independent gene expression in regulatory T-cells in evolutionary distant mice [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr A190.

Abstract IA03: Cancer modeling in the CRISPR age

Abstract The widespread use of high-throughput genomic methods is profoundly changing the way we understand, classify, and treat cancers, and provides unprecedented opportunities to design therapeutic approaches tailored to individual patients. The success of these efforts is contingent on our ability to identify, among the hundreds of different genetic lesions, those that actively drive tumorigenesis. The recent development of powerful genome editing methods promises to greatly speed up this process and allows us, for the first time, to model a wide range of previously inaccessible cancer-associated mutations. I will discuss how CRISPR-based gene editing methods are revolutionizing cancer. I will also show how my group is combining computational methods and in vivo somatic genome editing approaches to generate and characterize novel mouse models of human cancers and to explore the noncoding fraction of the cancer genome. Citation Format: Andrea Ventura. Cancer modeling in the CRISPR age [abstract]. In: Proceedings of the AACR Special Conference: Advances in Modeling Cancer in Mice: Technology, Biology, and Beyond; 2017 Sep 24-27; Orlando, Florida. Philadelphia (PA): AACR; Cancer Res 2018;78(10 Suppl):Abstract nr IA03.

Towards a Universal Molecular Microbiological Test.

The standard paradigm for microbiological testing is dependent on the presentation of a patient to a clinician. Tests are then requested based on differential diagnoses using the patient's symptoms as a guide. The era of high-throughput genomic methods has the potential to replace this model for the first time with what could be referred to as "hypothesis-free testing." This approach utilizes one of a variety of methodologies to obtain a sequence from potentially any nucleic acid in a clinical sample, without prior knowledge of its content. We discuss the advantages of such an approach and the challenges in making this a reality.

Combined GWAS and \u2018guilt by association\u2019-based prioritization analysis identifies functional candidate genes for body size in sheep

BackgroundBody size in sheep is an important indicator of productivity, growth and health as well as of environmental adaptation. It is a composite quantitative trait that has been studied with high-throughput genomic methods, i.e. genome-wide association studies (GWAS) in various mammalian species. Several genomic markers have been associated with body size traits and genes have been identified as causative candidates in humans, dog and cattle. A limited number of related GWAS have been performed in various sheep breeds and have identified genomic regions and candidate genes that partly account for body size variability. Here, we conducted a GWAS in Frizarta dairy sheep with phenotypic data from 10 body size measurements and genotypic data (from Illumina ovineSNP50 BeadChip) for 459 ewes.ResultsThe 10 body size measurements were subjected to principal component analysis and three independent principal components (PC) were constructed, interpretable as width, height and length dimensions, respectively. The GWAS performed for each PC identified 11 significant SNPs, at the chromosome level, one on each of the chromosomes 3, 8, 9, 10, 11, 12, 19, 20, 23 and two on chromosome 25. Nine out of the 11 SNPs were located on previously identified quantitative trait loci for sheep meat, production or reproduction. One hundred and ninety-seven positional candidate genes within a 1-Mb distance from each significant SNP were found. A guilt-by-association-based (GBA) prioritization analysis (PA) was performed to identify the most plausible functional candidate genes. GBA-based PA identified 39 genes that were significantly associated with gene networks relevant to body size traits. Prioritized genes were identified in the vicinity of all significant SNPs except for those on chromosomes 10 and 12. The top five ranking genes were TP53, BMPR1A, PIK3R5, RPL26 and PRKDC.ConclusionsThe results of this GWAS provide evidence for 39 causative candidate genes across nine chromosomal regions for body size traits, some of which are novel and some are previously identified candidates from other studies (e.g. TP53, NTN1 and ZNF521). GBA-based PA has proved to be a useful tool to identify genes with increased biological relevance but it is subjected to certain limitations.

RNA-seq Analysis of Irrigated vs. Water Stressed Transcriptomes of Zea mays Cultivar Z59.

Maize (Zea mays) is one of the important food crops of India with other potential uses in preparation of livestock feed and biofuel. But production of maize in India is low compared to top maize producing countries due to abiotic stresses (Ranum et al., 2014). Among abiotic stresses affecting maize production in India, drought is the major limiting factor. In spite of extensive research on drought tolerance in maize no tangible achievements could be made due to the extreme complexity associated with drought tolerance (Ashraf, 2010). Availability of the maize genome and high throughput genomic methods have provided new tools for addressing the complexity associated with drought tolerance (Zivy et al., 2015). We have chosen the drought tolerant genotype Z59 and are developing transcriptomic resources for better understanding of drought tolerance of this cultivar. As part of this work, we have sequenced the irrigated and water stressed transcriptomes of Z59 cultivar and the dataset is reported here.

Bioinformatics Based Approaches to Study Virus-Host Interactions During Chikungunya Virus Infection.

The limitations of high-throughput genomic methods used for studying virus-host interactions make it difficult to directly obtain insights on virus pathogenesis. In this chapter, the central steps of a protein structure similarity based computational approach used to predict the host interactors of Chikungunya virus are explained by highlighting the important aspects that need to be considered. Identification of such conserved set of putative interactions that allow the virus to take control of the host has the potential to deepen our understanding of the virus-specific remodeling processes of the host cell and illuminate new arenas of disease intervention.

Draft genome sequence of Oryza sativa elite indica cultivar RP Bio-226.

Rice cultivars show diversity in various factors influencing yield like nitrogen use efficiency, root development, and stress tolerance (Suryapriya et al., 2009; Singh et al., 2014; Begum et al., 2015; Mickelbart et al., 2015). For better understanding of the factors influencing yield of specific rice cultivars it is necessary to apply the high throughput genomic methods like whole genome sequencing, RNA-seq, smallRNA-seq, and ChIP-Seq. Improved Samba Mahsuri (RP Bio-226) is a bacterial leaf blight resistant indica rice cultivar developed through marker assisted selection from BPT5204 and SS1113. It is widely cultivated in Southern India because of its high yield, premium grain quality, and excellent cooking qualities. For understanding this cultivar in terms of nitrogen use efficiency recently we have sequenced the urea nutrition responsive transcriptome of this cultivar (Reddy and Ulaganathan, 2015). Further, with the aim of studying the genomic basis of yield of RP Bio-226 cultivar, we have sequenced the genome and analyzing it. Here we report the genomic dataset available publicly.

Molecular biology of pediatric brain tumors and impact on novel therapies.

Brain tumors are the leading cause of cancer-related death in children. For the past several decades, therapeutic strategies have centered on cytotoxic chemotherapy and radiation therapy due, in part, to limited understanding of genetic events that underlie tumor initiation and maintenance. Significant improvement in high-throughput genomic methods, such as next-generation sequencing, methylation array, and copy number array, in recent years has propelled the knowledge base from which novel therapies are derived. Translation of recent genomic findings into more effective therapies remains the most formidable challenge in improving the outcome for children with brain tumors.

Cohort-wide automated identification of morbid alleles with the human gene connectome (TECH1P.835)

Abstract To determine the disease-causing allele(s) underlying primary human inborn errors, high-throughput genomic methods are applied and provide thousands of gene variants per patient. We recently reported a novel approach, the “human gene connectome” (HGC) - the set of all in silico-predicted biologically plausible routes and distances between all pairs of human genes. We demonstrated that the HGC is the most powerful approach for prioritizing high-throughput genetic variants in Mendelian disease studies. However, there is currently no available method for automating the selection of candidate disease-causing mutant alleles in the absence of a known morbid gene in at least one patient with the disease of interest, posing a major bottleneck in the field in high-throughput clinical genomics. We hypothesized that within a cohort of patients with the same Mendelian (or nearly Mendelian) disease, the cluster that contains the key disease-causing gene for each patient is the HGC-predicted biologically smallest cluster. We then developed and applied a Mendelian clustering algorithm, which estimates the biologically smallest HGC-predicted cluster that contains one allele per patient. By that we estimated and statistically validated a set of disease-causing alleles in a whole exome sequencing cohort herpes simplex encephalitis patients. This new unbiased approach should facilitate the discovery of morbid alleles in patients with primary inborn errors that lack a genetic etiology.

Into the eye of the cytokine storm.

The cytokine storm has captured the attention of the public and the scientific community alike, and while the general notion of an excessive or uncontrolled release of proinflammatory cytokines is well known, the concept of a cytokine storm and the biological consequences of cytokine overproduction are not clearly defined. Cytokine storms are associated with a wide variety of infectious and noninfectious diseases. The term was popularized largely in the context of avian H5N1 influenza virus infection, bringing the term into popular media. In this review, we focus on the cytokine storm in the context of virus infection, and we highlight how high-throughput genomic methods are revealing the importance of the kinetics of cytokine gene expression and the remarkable degree of redundancy and overlap in cytokine signaling. We also address evidence for and against the role of the cytokine storm in the pathology of clinical and infectious disease and discuss why it has been so difficult to use knowledge of the cytokine storm and immunomodulatory therapies to improve the clinical outcomes for patients with severe acute infections.

Genomic Tools for Evolution and Conservation in the Chimpanzee: Pan troglodytes ellioti Is a Genetically Distinct Population

In spite of its evolutionary significance and conservation importance, the population structure of the common chimpanzee, Pan troglodytes, is still poorly understood. An issue of particular controversy is whether the proposed fourth subspecies of chimpanzee, Pan troglodytes ellioti, from parts of Nigeria and Cameroon, is genetically distinct. Although modern high-throughput SNP genotyping has had a major impact on our understanding of human population structure and demographic history, its application to ecological, demographic, or conservation questions in non-human species has been extremely limited. Here we apply these tools to chimpanzee population structure, using ∼700 autosomal SNPs derived from chimpanzee genomic data and a further ∼100 SNPs from targeted re-sequencing. We demonstrate conclusively the existence of P. t. ellioti as a genetically distinct subgroup. We show that there is clear differentiation between the verus, troglodytes, and ellioti populations at the SNP and haplotype level, on a scale that is greater than that separating continental human populations. Further, we show that only a small set of SNPs (10–20) is needed to successfully assign individuals to these populations. Tellingly, use of only mitochondrial DNA variation to classify individuals is erroneous in 4 of 54 cases, reinforcing the dangers of basing demographic inference on a single locus and implying that the demographic history of the species is more complicated than that suggested analyses based solely on mtDNA. In this study we demonstrate the feasibility of developing economical and robust tests of individual chimpanzee origin as well as in-depth studies of population structure. These findings have important implications for conservation strategies and our understanding of the evolution of chimpanzees. They also act as a proof-of-principle for the use of cheap high-throughput genomic methods for ecological questions.

Spatial Organization of the Mouse Genome and Its Role in Recurrent Chromosomal Translocations

The extent to which the three-dimensional organization of the genome contributes to chromosomal translocations is an important question in cancer genomics. We generated a high-resolution Hi-C spatial organization map of the G1-arrested mouse pro-B cell genome and used high-throughput genome-wide translocation sequencing to map translocations from target DNA double-strand breaks (DSBs) within it. RAG endonuclease-cleaved antigen-receptor loci are dominant translocation partners for target DSBs regardless of genomic position, reflecting high-frequency DSBs at these loci and their colocalization in a fraction of cells. To directly assess spatial proximity contributions, we normalized genomic DSBs via ionizing radiation. Under these conditions, translocations were highly enriched in cis along single chromosomes containing target DSBs and within other chromosomes and subchromosomal domains in a manner directly related to pre-existing spatial proximity. By combining two high-throughput genomic methods in a genetically tractable system, we provide a new lens for viewing cancer genomes.

Using high-throughput genomics to study hepatitis C: What determines the outcome of infection?

High-throughput genomic methods are now being used to study a wide variety of viral diseases, in an effort to understand how host responses to infection can lead either to efficient elimination of the pathogen or the development of severe disease. This article reviews how gene expression studies are addressing important clinical issues related to hepatitis C virus infection, in which some 15-25% of infected individuals are able to clear the virus without treatment, while the remainder progress to chronic liver disease that can lead to cirrhosis and death. Similar methods are also being used in an effort to identify the mechanisms underlying the failure of some hepatitis C patients to respond to interferon-alpha/ribavirin therapy. By providing a detailed picture of virus-host interactions, high-throughput genomics could potentially lead to the identification of novel cellular targets for the treatment of hepatitis C.

Extensive genome rearrangements and multiple horizontal gene transfers in a population of pyrococcus isolates from Vulcano Island, Italy.

The extent of chromosome rearrangements in Pyrococcus isolates from marine hydrothermal vents in Vulcano Island, Italy, was evaluated by high-throughput genomic methods. The results illustrate the dynamic nature of the genomes of the genus Pyrococcus and raise the possibility of a connection between rapidly changing environmental conditions and adaptive genomic properties.