Large Genomic Datasets Research Articles

Gauging ages of tiger swallowtail butterflies using alternate SNP analyses

Divergence times underpin diverse evolutionary hypotheses, but conflicting age estimates across studies diminish the validity of such hypotheses. These conflicts have continued to grow as large genomics datasets become commonplace and analytical approaches proliferate. To provide more stable temporal intervals, age estimations should be interpreted in the context of both the type of data and analysis being used. Here, we use multispecies coalescent (MSC), concatenation-based, and categorical data transformation approaches on genome-wide SNP data to infer divergence ages within the Papilio glaucus group of tiger swallowtail butterflies in North America. While the SNP data supported previously recognized relationships within the group (P. multicaudata, ((P. eurymedon, P. rutulus), (P. appalachiensis, P. canadensis, P. glaucus))), estimated ages of divergence between the major lineages varied substantially among analyses. MSC produced wide credibility intervals particularly for deeper nodes, reflecting uncertainty in the coalescence times as a possible result of conflicting signal across gene trees. Concatenation, in contrast, gave narrower and more well-defined posterior distributions for the node ages; however, the higher precision of these time estimates is a likely artefact due to more simplistic underlying assumptions of this approach that do not account for conflict among gene trees. Transformed categorical data analysis gave the least precise and the most variable results, with its simple substitution model coupled with a relaxed clock tending to produce spurious results from large genome-wide datasets. While median node ages differed considerably between analyses (∼2 Mya between MSC and concatenation-based results), their corresponding credibility intervals nonetheless highlight common temporal patterns for deeper divergences in the group as well as finer-scale phylogeography. Age distributions across analyses support an origin of the group during the warm period of the early to mid-Pliocene. Late Pliocene climate aridification and cooling drove divergence between eastern and western groups that further diversified during the period of repeated Pleistocene glaciations. Our results provide a structured comparative assessment of divergence time estimates and evolutionary relationships in a well-studied group of butterflies, and support better understanding of analytical biases in divergence time estimation.

Implications of Large-Effect Loci for Conservation: A Review and Case Study with Pacific Salmon.

The increasing feasibility of assembling large genomic datasets for non-model species presents both opportunities and challenges for applied conservation and management. A popular theme in recent studies is the search for large-effect loci that explain substantial portions of phenotypic variance for a key trait(s). If such loci can be linked to adaptations, 2 important questions arise: 1) Should information from these loci be used to reconfigure conservation units (CUs), even if this conflicts with overall patterns of genetic differentiation? 2) How should this information be used in viability assessments of populations and larger CUs? In this review, we address these questions in the context of recent studies of Chinook salmon and steelhead (anadromous form of rainbow trout) that show strong associations between adult migration timing and specific alleles in one small genomic region. Based on the polygenic paradigm (most traits are controlled by many genes of small effect) and genetic data available at the time showing that early-migrating populations are most closely related to nearby late-migrating populations, adult migration differences in Pacific salmon and steelhead were considered to reflect diversity within CUs rather than separate CUs. Recent data, however, suggest that specific alleles are required for early migration, and that these alleles are lost in populations where conditions do not support early-migrating phenotypes. Contrasting determinations under the US Endangered Species Act and the State of California's equivalent legislation illustrate the complexities of incorporating genomics data into CU configuration decisions. Regardless how CUs are defined, viability assessments should consider that 1) early-migrating phenotypes experience disproportionate risks across large geographic areas, so it becomes important to identify early-migrating populations that can serve as reliable sources for these valuable genetic resources; and 2) genetic architecture, especially the existence of large-effect loci, can affect evolutionary potential and adaptability.

Reliable and Scalable Identification and Prioritization of Putative Cellulolytic Anaerobes With Large Genome Data.

In the era of high-throughput sequencing, genetic information that is inherently whispering hints of the microbes’ functional niches is becoming easily accessible; however, properly identifying and characterizing these genetic hints to infer the microbes’ functional niches remains a challenge. Regarding genome-centric interpretation on the specific functional niche of cellulose hydrolysis for anaerobes, often encountered in practice is a lack of confidence in predicting the anaerobes’ real cellulolytic competency based solely on abundances of the varying carbohydrate-active enzyme modules annotated or on their taxonomy affiliation. Recognition of the synergy machineries that include but not limited to the cellulosome gene clusters is equally important as the annotation of individual carbohydrate-active modules or genes. In the interpretation of complete genomes of 2,768 microbe strains whose phenotypes have been well documented, with the incorporation of an automatic recognition of synergy among the carbohydrate active elements annotated, an explicit genotype–phenotype correlation was evidenced to be feasible for cellulolytic anaerobes, and a bioinformatic pipeline was developed accordingly. This genome-centric pipeline would categorize putative cellulolytic anaerobes into six genotype groups based on differential cellulose-hydrolyzing capacity and varying synergy mechanisms. Suggested in this genotype–phenotype correlation analysis was a finer categorization of the cellulosome gene clusters: although cellulosome complexes, by their nature, could enable the assembly of a number of carbohydrate-active units, they do not certainly guarantee the formation of the cellulose–enzyme–microbe complex or the cellulose-hydrolyzing activity of the corresponding anaerobe strains, for example, the well-known Clostridium acetobutylicum strains. Also, recognized in this genotype-phenotype correlation analysis was the genetic foundation of a previously unrecognized machinery that may mediate the microbe–cellulose adhesion, to be specific, enzymes encoded by genes harboring both the surface layer homology and cellulose-binding CBM modules. Applicability of this pipeline on scalable annotation of large genome datasets was further tested with the annotation of 7,902 reference genomes downloaded from NCBI, from which 14 genomes of putative paradigm cellulose-hydrolyzing anaerobes were identified. We believe the pipeline developed in this study would be a good add as a bioinformatic tool for genome-centric interpretation of uncultivated anaerobes, specifically on their functional niche of cellulose hydrolysis.

Gene Variants Involved in Nonsense-Mediated mRNA Decay Suggest a Role in Autism Spectrum Disorder.

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition with unclear etiology. Many genes have been associated with ASD risk, but the underlying mechanisms are still poorly understood. An important post-transcriptional regulatory mechanism that plays an essential role during neurodevelopment, the Nonsense-Mediated mRNA Decay (NMD) pathway, may contribute to ASD risk. In this study, we gathered a list of 46 NMD factors and regulators and investigated the role of genetic variants in these genes in ASD. By conducting a comprehensive search for Single Nucleotide Variants (SNVs) in NMD genes using Whole Exome Sequencing data from 1828 ASD patients, we identified 270 SNVs predicted to be damaging in 28.7% of the population. We also analyzed Copy Number Variants (CNVs) from two cohorts of ASD patients (N = 3570) and discovered 38 CNVs in 1% of cases. Importantly, we discovered 136 genetic variants (125 SNVs and 11 CNVs) in 258 ASD patients that were located within protein domains required for NMD. These gene variants are classified as damaging using in silico prediction tools, and therefore may interfere with proper NMD function in ASD. The discovery of NMD genes as candidates for ASD in large patient genomic datasets provides evidence supporting the involvement of the NMD pathway in ASD pathophysiology.

Accounting for Missing Pedigree Information with Single-Step Random Regression Test-Day Models

Genomic selection is widely used in dairy cattle breeding, but still, single-step models are rarely used in national dairy cattle evaluations. New computing methods have allowed the utilization of very large genomic data sets. However, an unsolved model problem is how to build genomic- (G) and pedigree- (A22) relationship matrices that satisfy the theoretical assumptions about the same scale and equal base populations. Incompatibility issues have also been observed in the manner in which the genetic groups are included in the model. In this study, we compared three approaches for accounting for missing pedigree information: (1) GT_H used the full Quaas and Pollak (QP) transformation for the genetic groups, including both the pedigree-based and the genomic-relationship matrices, (2) GT_A22 used the partial QP transformation that omitted QP transformation in G−1, and (3) GT_MF used the metafounder approach. In addition to the genomic models, (4) an official animal model with a unknown parent groups (UPG) from the QP transformation and (5) an animal model with the metafounder approach were used for comparison. These models were tested with Nordic Holstein test-day production data and models. The test-day data included 8.5 million cows with a total of 173.7 million records and 10.9 million animals in the pedigree, and there were 274,145 genotyped animals. All models used VanRaden method 1 in G and had a 30% residual polygenic proportion (RPG). The G matrices in GT_H and GT_A22 were scaled to have an average diagonal equal to that of A22. Comparisons between the models were based on Mendelian sampling terms and forward prediction validation using linear regression with solutions from the full- and reduced-data evaluations. Models GT_H and GT_A22 gave very similar results in terms of overprediction. The MF approach showed the lowest bias.

Embracing Green Computing in Molecular Phylogenetics.

Molecular evolutionary analyses require computationally intensive steps such as aligning multiple sequences, optimizing substitution models, inferring evolutionary trees, testing phylogenies by bootstrap analysis, and estimating divergence times. With the rise of large genomic data sets, phylogenomics is imposing a big carbon footprint on the environment with consequences for the planet’s health. Electronic waste and energy usage are large environmental issues. Fortunately, innovative methods and heuristics are available to shrink the carbon footprint, presenting researchers with opportunities to lower the environmental costs and greener evolutionary computing. Green computing will also enable greater scientific rigor and encourage broader participation in big data analytics.

Enhancing coding skills with CloudStor SWAN

CloudStor SWAN (AARNet, 2022) is a research-focused web service for running analyses that is available to staff and students at many research institutes and Universities across Australia and New Zealand. In 2021, we used SWAN as a teaching tool in the master-level subject, Computational Genomics (COMP90016) at The University of Melbourne. This subject aims to teach students how to analyse large genomic datasets using best practices software tools, pipelines and student-written, custom code. 
 
 Although CloudStor SWAN was not conceived as a teaching tool, we worked with their technical staff to tailor the service to our use case. This innovative use of existing research infrastructure allowed us to effectively transition the subject to remote learning. Students and staff could log in to the service using their existing University credentials, from anywhere in the world, without the use of a VPN. The ability to access the platform from a web browser allowed for a consistent computing environment for all students regardless of operating system, and without having to worry about software installations on local machines. This presented a significantly improved experience from the custom servers that had been used in the past.
 
 We used SWAN for weekly workshops during semester and for assessment in the form of assignments and an exam. It allowed us to format subject material in Jupyter notebooks where we could seamlessly integrate text, graphics and code. Additionally, assessed code questions can incorporate automatic marking and written submissions can be checked for plagiarism. SWAN also allowed us to introduce students to the UNIX command line, an important skillset that was not previously taught in the University of Melbourne Master of Science (Bioinformatics) program.
 
 From a student perspective, SWAN allowed for a practical skillset to be developed alongside theoretical knowledge from other aspects of the course. The platform was simple to learn and allowed students to focus on the subject content and the tasks asked of them, rather than on the interface. From a teacher’s perspective, having a unified platform allowed for a single set of clear instructions, improved troubleshooting and clearer management of tool versions and software dependencies. The use of Jupyter notebooks simplified lesson plans and assessments by integrating multiple elements into single documents. This element also made the lessons more easily sharable between colleagues and collaborators.
 
 Our integration of this technology into our tertiary teaching has served as a model for a similar use at a different Australian university. We hope to share the lessons learned from this subject, the advantages of using CloudStor SWAN in a teaching environment for both staff and students and provide some advice for others who may want to adapt it to fit their own teaching needs.
 Presentation link: https://youtu.be/8tutCO1hd9c
 References
 
 AARNet. (2022). CloudStor: Access, store, share and work with your data in one place. https://www.aarnet.edu.au/network-and-services/cloud-services/cloudstor

A computationally tractable birth-death model that combines phylogenetic and epidemiological data.

Inferring the dynamics of pathogen transmission during an outbreak is an important problem in infectious disease epidemiology. In mathematical epidemiology, estimates are often informed by time series of confirmed cases, while in phylodynamics genetic sequences of the pathogen, sampled through time, are the primary data source. Each type of data provides different, and potentially complementary, insight. Recent studies have recognised that combining data sources can improve estimates of the transmission rate and the number of infected individuals. However, inference methods are typically highly specialised and field-specific and are either computationally prohibitive or require intensive simulation, limiting their real-time utility. We present a novel birth-death phylogenetic model and derive a tractable analytic approximation of its likelihood, the computational complexity of which is linear in the size of the dataset. This approach combines epidemiological and phylodynamic data to produce estimates of key parameters of transmission dynamics and the unobserved prevalence. Using simulated data, we show (a) that the approximation agrees well with existing methods, (b) validate the claim of linear complexity and (c) explore robustness to model misspecification. This approximation facilitates inference on large datasets, which is increasingly important as large genomic sequence datasets become commonplace.

Privacy-preserving genotype imputation with fully homomorphic encryption.

Genotype imputation is the inference of unknown genotypes using known population structure observed in large genomic datasets; it can further our understanding of phenotype-genotype relationships and is useful for QTL mapping and GWASs. However, the compute-intensive nature of genotype imputation can overwhelm local servers for computation and storage. Hence, many researchers are moving toward using cloud services, raising privacy concerns. We address these concerns by developing an efficient, privacy-preserving algorithm called p-Impute. Our method uses homomorphic encryption, allowing calculations on ciphertext, thereby avoiding the decryption of private genotypes in the cloud. It is similar to k-nearest neighbor approaches, inferring missing genotypes in a genomic block based on the SNP genotypes of genetically related individuals in the same block. Our results demonstrate accuracy in agreement with the state-of-the-art plaintext solutions. Moreover, p-Impute is scalable to real-world applications as its memory and time requirements increase linearly with the increasing number of samples. p-Impute is freely available for download here: https://doi.org/10.5281/zenodo.5542001.

Capturing Biomarkers and Molecular Targets in Cellular Landscapes From Dynamic Reaction Network Models and Machine Learning.

Computational dynamic ODE models of cell function describing biochemical reactions have been created for decades, but on a small scale. Still, they have been highly effective in describing and predicting behaviors. For example, oscillatory phospho-ERK levels were predicted and confirmed in MAPK signaling encompassing both positive and negative feedback loops. These models typically were limited and not adapted to large datasets so commonly found today. But importantly, ODE models describe reaction networks in well-mixed systems representing the cell and can be simulated with ordinary differential equations that are solved deterministically. Stochastic solutions, which can account for noisy reaction networks, in some cases, also improve predictions. Today, dynamic ODE models rarely encompass an entire cell even though it might be expected that an upload of the large genomic, transcriptomic, and proteomic datasets may allow whole cell models. It is proposed here to combine output from simulated dynamic ODE models, completed with omics data, to discover both biomarkers in cancer a priori and molecular targets in the Machine Learning setting.

Artificial intelligence-aided clinical annotation of a large multi-cancer genomic dataset

To accelerate cancer research that correlates biomarkers with clinical endpoints, methods are needed to ascertain outcomes from electronic health records at scale. Here, we train deep natural language processing (NLP) models to extract outcomes for participants with any of 7 solid tumors in a precision oncology study. Outcomes are extracted from 305,151 imaging reports for 13,130 patients and 233,517 oncologist notes for 13,511 patients, including patients with 6 additional cancer types. NLP models recapitulate outcome annotation from these documents, including the presence of cancer, progression/worsening, response/improvement, and metastases, with excellent discrimination (AUROC > 0.90). Models generalize to cancers excluded from training and yield outcomes correlated with survival. Among patients receiving checkpoint inhibitors, we confirm that high tumor mutation burden is associated with superior progression-free survival ascertained using NLP. Here, we show that deep NLP can accelerate annotation of molecular cancer datasets with clinically meaningful endpoints to facilitate discovery.

Charting the tumor antigen maps drawn by single-cell genomics.

The remarkable specificity of antibodies has enabled precision cancer immunotherapies, including chimeric antigen receptor Tcells and antibody-drug conjugates. In parallel, single-cell genomics technologies present the possibility of a comprehensive annotation of antigen expression throughout tissues of the human body and on cancer cells. We reflect on the rationale for antigen targets currently used in immunotherapies, their adverse effects revealed in the clinic, and the opportunity to utilize large genomics datasets to de-risk potential targets and nominate optimal antigens for therapy.

Introduction: the secret lives of microbial mobile genetic elements.

Introduction: the secret lives of microbial mobile genetic elements.

Aspergillus fumigatus pan-genome analysis identifies genetic variants associated with human infection.

Aspergillus fumigatus is an environmental saprobe and opportunistic human fungal pathogen. Despite an estimated annual occurrence of more than 300,000 cases of invasive disease worldwide, a comprehensive survey of the genomic diversity present in A. fumigatus-including the relationship between clinical and environmental isolates and how this genetic diversity contributes to virulence and antifungal drug resistance-has been lacking. In this study we define the pan-genome of A. fumigatus using a collection of 300 globally sampled genomes (83 clinical and 217 environmental isolates). We found that 7,563 of the 10,907 unique orthogroups (69%) are core and present in all isolates and the remaining 3,344 show presence/absence of variation, representing 16-22% of the genome of each isolate. Using this large genomic dataset of environmental and clinical samples, we found an enrichment for clinical isolates in a genetic cluster whose genomes also contain more accessory genes, including genes coding for transmembrane transporters and proteins with iron-binding activity, and genes involved in both carbohydrate and amino-acid metabolism. Finally, we leverage the power of genome-wide association studies to identify genomic variation associated with clinical isolates and triazole resistance as well as characterize genetic variation in known virulence factors. This characterization of the genomic diversity of A. fumigatus allows us to move away from a single reference genome that does not necessarily represent the species as a whole and better understand its pathogenic versatility, ultimately leading to better management of these infections.

Location of the t(4;14) Translocation Breakpoint Identifies a Subset of Newly-Diagnosed Multiple Myeloma Patients with Poor Prognosis

Introduction: The recombination of chromosomes 4 and 14 (t(4;14)) is a primary, predominantly clonal event in newly diagnosed multiple myeloma (ndMM) that is present in ~15% of patients. The translocation results in enhancer regions from the immunoglobulin heavy chain locus upregulating the expression of NSD2 and FGFR3 genes implicated in the disease biology of this subset of MM patients (Chesi et al. Blood. 1998, Keats et al, Leuk Lymph. 2006). The presence of t(4;14) translocation is a considered a biomarker of aggressive disease and is part of the Revised International Staging System (R-ISS) for clinical risk stratification. However, historically only ~40% of t(4;14) patients are high-risk based on the GEP70 gene expression signature. (Weinhold et al. Leukemia. 2016) Our previous analysis of a large cohort of ndMM patients described the genomic features of t(4;14) vs ndMM overall population demonstrating that only ~25% of t(4;14) patients died within 24 months of diagnosis and described biomarkers in this high-risk subset. This analysis identified both known and novel aberrations in ndMM, including some that were associated with high-risk t(4;14) (Ortiz et al Blood. 2019; 134 (Suppl_1):366). In this updated analysis, we provide a more robust analysis of the t(4;14) dataset and demonstrate the prognostic value of the NSD2 breakpoint location.Methods: We generated a large genomic dataset from t(4;14) ndMM patients with whole genome sequencing (WGS) and RNA-seq from a TOUL dataset (t(4;14) N=114) patients treated in routine practice), the IFM2009 trial (N=19), and the Myeloma Genome Project (MGP) (N=34) for discovery and validation. Gene expression, copy number aberration, single nucleotide variant and translocations were derived from RNAseq and WGS profiling of biopsies from patients aged less than 75 years who received transplant, and integrated with clinical information (including age, OS). Cytogenetic assessments from WGS were made by MANTA and used to identify translocation DNA breakpoint location.Results: In all datasets, three DNA breakpoint locations were identified, and based on their position with respect to the NSD2 gene named “no-disruption” (upstream of NSD2 gene), “early-disruption” (in the 5' UTR of NSD2 gene) and “late-disruption” (in the coding region of NSD2 gene). Using paired RNA-seq data, we identified IGH-NSD2 RNA fusion transcripts relative to the breakpoints that corresponded with previously described NSD2 isoforms. “No-disruption” and “early-disruption” breakpoints predominantly produced a fusion transcript (MB4-1) that retained the full coding sequence of the gene, while the “late-disruption” produced truncated fusion transcripts (MB4-2/3). We conducted survival analysis in our datasets based on both DNA breakpoint location and RNA fusion transcripts. This analysis demonstrated a significant difference in outcome between the patient samples with “no-disruption” and the “late-disruption” breakpoints that associated with good and poor OS, respectively (OS pval < 3e-4) in the discovery TOUL dataset. Patients with “late-disruption” had a median OS of 28.64 mo vs 59.18 mo for “early disruption” and 82.26 mo for those with “no disruption” (Figure). This association was replicated in an independent dataset (MGP N=33, replication pval<4.3e-5). The mOS difference of patients based on which fusion transcript they express is less than the difference based on breakpoint (mOS MB4-1 = 47.38 mo. vs. MB4-2/3 = 60.89 mo.). These analyses demonstrate that the breakpoint location has a stronger association with outcome than fusion transcript expression.Conclusion: From a large genomic dataset, we were able to discover and validate a clear association between the translocation breakpoints and survival outcome in t(4:14) ndMM patients. While prospective validation is needed before clinical application of our finding, molecular identification of high-risk t(4;14) patients using DNA breakpoint location may enable proper risk classification for this patient group at diagnosis, and would provide improved opportunities for risk-adjusted therapy and identification of a therapeutic target for this high-risk subpopulation. Ongoing work on mutations, copy number, and differential gene expression analyses between translocation breakpoint sub-groups and will be presented. [Display omitted] DisclosuresStong: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Ortiz: Bristol Myers Squibb: Current Employment. Towfic: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Pierceall: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company. Flynt: Bristol Myers Squibb: Current Employment. Thakurta: Bristol Myers Squibb: Current Employment, Current equity holder in publicly-traded company, Patents & Royalties.

Integrative Genomic Analysis Uncovers Unique Diffuse Large B Cell Lymphoma (DLBCL) Immune Environments and Identifies Associations with Specific Oncogenic Alterations

Introduction: Most patients diagnosed with diffuse large B cell lymphoma (DLBCL) are cured with combination chemoimmunotherapy, but 40% will develop relapsed or refractory (r/r) disease, which is often associated with a poor clinical outcome. PD-1 blockade therapy has been investigated in r/r DLBCL; however, response rates in unselected DLBCL patients are disappointing, highlighting the need for deeper understanding of DLBCL immune landscapes, as well as mechanisms that regulate the immune response to checkpoint blockade therapy (CBT) in this disease.In solid cancers, tumor-cell intrinsic oncogenic signaling strongly influences the immune environment and impacts clinical response to CBT. Despite the recent publication of large-scale genomic datasets in DLBCL, the impact of oncogenic signaling on the immune environment remains to be fully elucidated. In this study, we aimed to characterize immune landscapes associated with DLBCL, as well as the role of lymphoma-intrinsic alterations on shaping the immune environment in this disease.Methods: Using gene set variation analysis (GSVA) in a large cohort of primary DLBCLs (n = ~900), a sample-wise enrichment score was generated for gene sets associated with tumor infiltrating lymphocytes. Gene sets were manually curated to include signatures relating to IFNγ response, T helper cell subsets, CD8 + T cell exhaustion, macrophages, and dendritic cells. A DLBCL cell-of-origin (COO) signature was also included in the GSVA to control for the transcriptional and genomic effects of COO. Samples were hierarchically clustered into related groups. Multispectral immunofluorescence (mIF) for canonical T cell markers was used to confirm GSVA clustering. To mechanistically validate our findings, CRISPR/Cas9 gene editing was used to modulate candidate oncogenes and tumor suppressors genes (TSGs) in the syngeneic A20 murine lymphoma model.Results: GSVA performed on transcriptomes from a large genomic DLBCL dataset revealed four distinct DLBCL immune clusters, termed “ABC hot”, “ABC cold”, “GCB hot” and “GCB cold”, defined by differential expression scores of immune related gene sets (Fig 1A). Concordant with our previous work, DLBCLs with PD-L1 gene amplifications, which are associated with a “T-cell inflamed” tumor microenvironment, were enriched in the “ABC hot” cluster (Fig 1B). Conversely, double hit signature DLBCLs, known to be associated with decreased immune cell infiltration and a GCB COO, were enriched in “GCB cold” DLBCLs (Fig 1C).In an internal cohort of diagnostic DLBCL samples (n = 90) for whom RNA sequencing (RNAseq) and FFPE tissue were available, mIF analysis showed that both “ABC hot” and “GCB hot” DLBCLs had significantly higher ratios of CD8 + T cells to lymphoma cells compared to cold DLBCLs. “ABC hot” DLBCLs also had a significantly higher CD4 + T cell to lymphoma cell ratio (Fig 1D).Importantly, several mutations that correlated with particular DLBCL immune clusters were identified. The “ABC cold” cluster was significantly enriched for loss-of-function (LOF) mutations in TMEM30A and MYD88, whereas LOF mutations in ATM and FOXO1 were commonly observed in “GCB cold” DLBCLs. Finally, LOF mutations in SOCS1 and B2M were significantly enriched in “GCB hot” DLBCLs (Fig 1E, 1F).As LOF SOCS1 mutations were strongly associated with “GCB hot” DLBCLs and are also prevalent in other CBT-sensitive lymphomas, we hypothesized that SOCS1 LOF mutations would enhance lymphoma cell vulnerability to CBT due to increased IFNγ sensitivity resulting from unopposed JAK/STAT activation. To test this hypothesis, we generated Socs1 deficient A20 lymphoma cells. Compared to A20 WT, A20 Socs1-/- cells were characterized by increased pStat1 levels upon IFNγ stimulation (Fig 1G). Interestingly, A20 Socs1-/- tumors showed increased sensitivity to α-PD1 therapy compared to A20 WT in syngeneic hosts. Together, these data suggest that tumor-cell intrinsic JAK/STAT activation via SOCS1 -/- increases lymphoma cell sensitivity to IFNγ and α-PD1 therapy (Fig 1H).Conclusion: We have developed a novel immunogenomic platform to define the role of tumor-cell intrinsic alterations on the immune landscape of DLBCL. Confirmatory studies using in vitro and in vivo models validated the effect of key oncogenes and TSGs on the tumor microenvironment, and suggest these candidate genes may impact response to CBT in DLBCL. [Display omitted] DisclosuresSmith: Alexion, AstraZeneca Rare Disease: Other: Study investigator; Celgene, Genetech, AbbVie: Consultancy. Steidl: Trillium Therapeutics: Research Funding; Curis Inc.: Consultancy; Epizyme: Research Funding; Seattle Genetics: Consultancy; Bayer: Consultancy; AbbVie: Consultancy; Bristol-Myers Squibb: Research Funding. Kline: Seagen: Membership on an entity's Board of Directors or advisory committees; Morphosys: Consultancy, Membership on an entity's Board of Directors or advisory committees; Kite/Gilead: Speakers Bureau; Karyopharm: Membership on an entity's Board of Directors or advisory committees; Merck: Consultancy, Research Funding; Verastem: Research Funding; SecuraBio: Membership on an entity's Board of Directors or advisory committees; Regeneron: Membership on an entity's Board of Directors or advisory committees.

Phylogenomics and biogeography of Catalpa (Bignoniaceae) reveal incomplete lineage sorting and three dispersal events

Catalpa Scop. (Bignoniaceae) is a small genus (8 spp.) of trees that is disjunctly distributed among eastern Asia, eastern United States, and the West Indies. Catalpa bears beautiful inflorescences and have been cultivated as important ornamental trees for landscaping, gardening, and timber. However, the phylogenetic relationships and biogeographic history of the genus have remained unresolved. In this study, we used a large genomic dataset that includes data from the chloroplast (plastomes), and nuclear genomes (ITS and 5,759 single-copy nuclear genes) to reconstruct phylogenetic relationship within Catalpa, test interspecific gene flow events within the genus, and infer its biogeographic history. Our phylogenetic results indicate that Catalpa is monophyletic containing two main clades, section Catalpa and section Macrocatalpa. Section Catalpa is further divided into three subclades. While most relationships are congruent between the chloroplast and nuclear datasets, the position of C. ovata differs, likely due to incomplete lineage sorting. Interspecific gene flow events include C. bungei s.s. with vectors of inheritance from C. duclouxii and C. fargesii, supporting a combination of these three species and recognizing a broadly circumscribed C. bungei s.l. Our biogeographic study suggests three main dispersal events, two of which occurred during the Oligocene. The first dispersal event occurred from southwestern North America and Mexico into the Greater Antilles giving rise to the ancestor of the section of Macrocatalpa. The second dispersal event also occurred from southwestern North America and Mexico, but led to central and northern North America, subsequently reaching China through the Bering land bridge, and also reaching Europe through the North Atlantic land bridge. The third dispersal event took place in the Miocene from China to North America and gave rise to a clade composed of C. bignonioides and C. speciosa. This study uses a phylogenomic approach and biogeographical methods to infer the evolutionary history of Catalpa, highlighting issues associated with gene tree discordance, and suggesting that incomplete lineage sorting likely played an important role in the evolutionary history of Catalpa.

Genomics and big data: Research, development and applications

Recent years, genomics and big data analytics have been widely applied and have significant impacts in various important areas of social life worldwide. The development of the next-generation sequencing (NGS) technologies, such as whole-genome sequencing (WGS), whole-exome sequencing (WES), transcriptome, and/or targeted sequencing, has enabled quickly generating the genomes of interested living organisms. Around the world many nations have invested in and promoted the development of genomics and big data analytics. A number of well-established projects on sequencing of human, animal, plant, and microorganism genomes to generate vast amounts of genomic data have been conducted independently or as collaborative efforts by national or international research networks of scientists specializing in different technical fields of genomics, bioinformatics, computational and statistical biology, automation, artificial intelligence, etc. Complicated and large genomic datasets have been effectively established, storage, managed, and used. Vietnam supports this new field of study through setting up governmental authorized institutions and conducting genomic research projects of human and other endemic organisms. In this paper, the research, development, and applications of genomic big data are reviewed with focusing on: (i) Available sequencing technologies for generating genomic datasets; (ii) Genomics and big data initiatives worldwide; (iii) Genomics and big data analytics in selected countries and Vietnam; (iv) Genomic data applications in key areas including medicine for human health care, agriculture - forestry, food safety, and environment.

Transcriptomics of the depressed and PTSD brain

Stress is the response of an organism to demands for change, yet excessive or chronic stress contributes to nearly all psychiatric disorders. The advent of high-throughput transcriptomic methods such as single cell RNA sequencing poses new opportunities to understand the neurobiology of stress, yet substantial barriers to understanding stress remain. Stress adaptation is an organismal survival mechanism conserved across all organisms, yet there is an infinity of potential stressful experiences. Unraveling shared and separate transcriptional programs for adapting to stressful experience remains a challenge, despite methodological and analytic advances. Here we review the state of the field focusing on the technologies used to study the transcriptome for the stress neurobiologist, and also attempt to identify central questions about the heterogeneity of stress for those applying transcriptomic approaches. We further explore how postmortem transcriptome studies aided by preclinical animal models are converging on common molecular pathways for adaptation to aversive experience. Finally, we discuss approaches to integrate large genomic datasets with human neuroimaging and other datasets.

Population genomics of Drosophila suzukii reveal longitudinal population structure and signals of migrations in and out of the continental United States.

Drosophila suzukii, or spotted-wing drosophila, is now an established pest in many parts of the world, causing significant damage to numerous fruit crop industries. Native to East Asia, D. suzukii infestations started in the United States a decade ago, occupying a wide range of climates. To better understand invasion ecology of this pest, knowledge of past migration events, population structure, and genetic diversity is needed. In this study, we sequenced whole genomes of 237 individual flies collected across the continental United States, as well as several sites in Europe, Brazil, and Asia, to identify and analyze hundreds of thousands of genetic markers. We observed strong population structure between Western and Eastern US populations, but no evidence of any population structure between different latitudes within the continental United States, suggesting that there are no broad-scale adaptations occurring in response to differences in winter climates. We detect admixture from Hawaii to the Western United States and from the Eastern United States to Europe, in agreement with previously identified introduction routes inferred from microsatellite analysis. We also detect potential signals of admixture from the Western United States back to Asia, which could have important implications for shipping and quarantine policies for exported agriculture. We anticipate this large genomic dataset will spur future research into the genomic adaptations underlying D. suzukii pest activity and development of novel control methods for this agricultural pest.