Genomic Findings Research Articles

Post-Operative Radiotherapy in Prostate Cancer: Is It Time for a Belt and Braces Approach?

Approximately 30% of patients treated with radical prostatectomy (RP) for prostate cancers experience biochemical recurrence (BCR). Post-operative radiation therapy (RT) can be either offered immediately after the surgery in case of aggressive pathological features or proposed early if BCR occurs. Until recently, little data were available regarding the optimal RT timing, protocol, volumes to treat, and the benefit of adding androgen deprivation therapies to post-operative RT. In this review, we aim to pragmatically discuss current literature data on these points. Early salvage RT appears to be the optimal post-operative approach, improving oncological outcomes especially with low prostate-specific antigen (PSA) levels, as well as sparing several unnecessary adjuvant treatments. The standard RT dose is still 64–66 Gy to the prostate bed in conventional fractionation, but hypofractionation protocols are emerging pending on late toxicity data. Several scientific societies have published contouring atlases, even though they are heterogeneous and deserve future consensus. During salvage RT, the inclusion of pelvic lymph nodes is also controversial, but preliminary data show a possible benefit for PSA > 0.34 ng/ml at the cost of increased hematological side effects. Concomitant ADT and its duration are also discussed, possibly advantageous (at least in terms of metastasis-free survival) for PSA rates over 0.6 ng/ml, taking into account life expectancy and cardiovascular comorbidities. Intensified regimens, for instance, with new-generation hormone therapies, could further improve outcomes in carefully selected patients. Finally, recent advances in molecular imaging, as well as upcoming breakthroughs in genomics and artificial intelligence tools, could soon reshuffle the cards of the current therapeutic strategy.

Abstract 9855: Bayesian Multivariate Genetic Analysis of Lipids Improves Translational Insight

Introduction: Genome-wide association studies (GWAS) of blood lipids have yielded several translational insights for coronary artery disease. Hypothesis: We tested the hypothesis that novel Bayesian methods leveraging genetic correlation can improve insights from lipid genetics analyses. Methods: We used published lipids GWAS results from 297,626 participants of the Million Veterans Program (MVP), and generated summary statistics for 276,096 participants of the UK Biobank. We employed a multivariate adaptive shrinkage (mashR) model to each dataset. MashR allows the effect of a SNP to be modeled as a mixture of multivariate normal distributions across lipid levels (LDL-C, HDL-C, Total Cholesterol, and triglycerides) and an empirical Bayes approach to appropriately nudge the posterior estimates of the effect in accordance with the overall patterns observed in the data. Results: In addition to reproducing all 318 loci significantly associated with lipids in MVP, we identified 4,689 novel loci associated with lipids using mashR. We observed a high degree of agreement between MVP and UK Biobank, with over 95% of UKBB replicated in MVP and an additional 1246 identified. In genetic prioritization analyses, mashR shows improvement in the detection of known Mendelian genes and the consistency of ranks among subgroups compared with univariate results. Our approach improves the estimate of enrichment parameters of known genomic features similar to established GWAS variants. Finally, combining these joint estimates with tools for incorporating the genetic correlation structure, mashR improves the proportion of variation explained by lipid polygenic risk scores by up to 58% (se 0.015) when compared to univariate substrates across lipid traits. This improvement holds across both European and non-European individuals. We show that much of this gain in power arises from improved precision of the posterior estimates where the ratio of original standard error over posterior marginal variance reflects an increased ‘effective sample size’. Conclusions: Our framework shows that Bayesian multivariate genetic analysis markedly improves power for genomic discovery, better identifies causal genes, and improves polygenic risk prediction for complex traits.

Whole Genome Sequencing Identifies Microdeletions Affecting TET2 and RUNX1 with Clinical Impact in Myeloid Malignancies

Background:The current routine genetic work-up in hematological malignancies includes chromosome banding analysis (CBA) to detect complete or partial chromosomal deletions and fusions, and the identification of point mutations and small deletions or insertions by sequencing panels (max. length ~50 bp). Deletions of individual genes (e.g. IKZF1 in ALL) are only detected by specifically designed molecular tools. Therefore, those microdeletions might be overlooked by the current gold standard despite their clinical relevance. We established a bioinformatic pipeline to screen for microdeletions in whole genome sequencing (WGS) data of myeloid malignancies.Aim:(1) Screen for recurrent microdeletions in myeloid malignancies with a normal karyotype, and (2) characterize a patient specific profile of microdeletions in genes with known clinical and/or prognostic relevance.Patients and Methods:We analyzed 1356 cases (M/F: 778/578) of myeloid malignancies with a normal karyotype according to CBA (aCML: n=47; AML: n=251; CMML: n=165, mastocytosis: n=90; MDS: n=415, MDS/MPN-RS-T: n=69; MDS/MPN-U: n=42; MPN: n=250; PNH: n=27) using WGS. Median age was 71 [20-94] years. Amplification-free WGS was performed on the NovaSeq or HiSeq system with a median coverage of 103x (Illumina, San Diego, CA). Reads were aligned to the human reference genome (GRCh37, Ensembl annotation, Isaac aligner) and somatic copy number variant (CNV) discovery was performed with GATK (v 4.0.2.1), following best practice guidelines. Only gene overlapping CNV calls were considered for analysis (gene coordinates biomaRt (v 2.42.1), GRCh37 Ensembl).Results:On average, 38 genes per patient were partially or completely deleted and the size of the deletions ranged from 0.9 kb to 32 Mb (median 399 kb). The microdeletions affected a broad list of genes, but no gene was present in >5% of myeloid malignancies. As technical validation, we used 36 B-ALL samples (normal karyotype) and identified the known deletions of IKZF1 (42%); PAX5 (25%) and CDKN2A/CDKN2B (22%) with expected incidences.We focused on a patient-by-patient analysis of genes (n=47) with known clinical relevance in myeloid malignancies. We identified deleted genes in 46 out of 1356 patients (3.4%). In aCML 13% of patients had one of the above-mentioned genes deleted (6/47), in mastocytosis only 1% (1/90). The most frequently deleted genes were TET2 (20/1356, 1.5%) and RUNX1 (9/1356, 0.7%). Other deletions also affected transcription factors (e.g. GATA2) or epigenetic regulators (e.g. DNMT3A, figure 1). No deletion of splicing factors, RAS genes or cohesion complex regulators was observed. We found only two deletions of kinases, which are predominantly affected by activating mutations (both FLT3). Instead, the deletions in 41 patients involved genes with a known loss-of-function mutation profile in myeloid malignancies. This corresponds to 89% (41/46) of patients with microdeletions or 3% (41/1356) of all analyzed patients with myeloid malignancies. Microdeletions are thus another genetic element that can lead to loss of gene activity.Deletions and mutations are either alternative genetic mechanisms or co-operate as double hits to affect the same gene. We found additional mutations present in 18 of the 46 patients with microdeletions (39%, figure 1). The majority of these (n=14) involved TET2. TET2 mutations had a median variant allele frequency of 82% [9-100%] indicative of a mutation on the non-deleted allele. For the remaining genes (incl. RUNX1), deletions are predominantly an alternative genetic mechanism to mutations.For validation of WGS results we applied interphase FISH and identified 6/9 RUNX1 deletions. The remaining three microdeletions were only detectable by WGS and too small to be identified by FISH.Conclusions:(1) WGS data unrevealed a plethora of microdeletions, which can be an alternative genetic mechanism to mutations, but are not detected with today's standard diagnostic tools.(2) In the light of increasingly personalized therapy and diagnostics, all genetic mechanisms should be considered, which impact the function of clinically relevant genes.(3) Bioinformatic pipelines for WGS as a potential diagnostic tool in the near future should address microdeletions in genes with relevance for patients' diagnosis, prognosis and hopefully targeted treatment. [Display omitted] DisclosuresKern: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership. Haferlach: MLL Munich Leukemia Laboratory: Other: Part ownership.

An Open-Source Toolkit That Powers the Genome-Wide Analysis of Mature B-Cell Lymphomas (GAMBL) Project

Introduction: Genome- and transcriptome-wide analyses continue to enhance our understanding of the molecular pathogenesis of cancer. In lymphomas, this has enabled the identification of hundreds of recurrently mutated genes, highlighting genetic heterogeneity and relationships both within and among clinical entities. While the growing availability of lymphoma genomic data sets can be leveraged to integrate genomic analyses into diagnostic testing and clinical trials, the ability to rapidly process genomic data sets in a reproducible manner serves as a barrier to this goal. To this end, we developed a suite of tools Lymphoid Cancer Research modules (LCR-modules) to facilitate the discovery of novel drivers and molecular features in lymphoma cancers and perform quantitative comparisons between disease entities. We demonstrate here how this toolkit enabled a meta-analysis of lymphoma genomic data involving genome-wide profiles of 3330 patients.Methods: We assembled a collection of whole genome, whole exome, and RNA sequencing data from a combination of controlled-access repositories and ongoing projects at BC Cancer. The scope of genomic analysis of mature B-cell lymphomas (GAMBL) project includes cell lines and patient tumors from all common mature B cell neoplasms, comprising a total of 4612 samples from 3330 patients. To facilitate the project, we developed a suite of open-source and custom bioinformatics tools (https://github.com/LCR-BCCRC/lcr-modules) that leverages the Snakemake workflow management system and includes lymphoma-centric modules for the discovery and annotation of common mutation types, analysis of B-cell receptor repertoires and discovery of novel aSHM targets and relevant non-coding mutations, and RNA-seq analysis with batch correction and normalization. Individual modules are configured to create an automated, scalable, and reproducible workflow that runs each step as dictated by the availability of new data. The cohort-level integrative analysis and comparisons across entities are handled by our custom R package GAMBLR, which facilitates open-ended data analysis and custom visualizations.Results: Simple somatic mutations (SSM) were detected using a workflow that utilizes four algorithms to identify high-confidence variants with validated default thresholds for filtering of germline variants and common FFPE-associated artifacts, allowing for processing of samples without matched normal tissue. This automated and reproducible workflow facilitated the discovery of novel genes significantly mutated across lymphomas and broadened our understanding of the scope of aberrant somatic hypermutation (aSHM) and other non-coding mutations. Specifically, HNRNPU, STAT3, TFAP4, RRAGC were found to be mutated at relatively low frequencies, and their presence is a distinct feature of certain lymphomas or novel genetic subgroups within lymphoma types (Figure 1A).The aSHM analysis and discovery of novel hypermutated regions is handled by a custom tool Rainstorm. As a result, we were able to detect sites preferentially hypermutated in a single entity, such as the transcription start site of BACH2, mutated at lower rates than the other common target sites but significantly more in BL compared to other entities (Figure 1B). Combining aSHM at target sites discovered using our toolkit with other genetic features allowed us to explore and establish novel genetic subgroups within Burkitt lymphoma and follicular lymphoma.SV analysis can be conducted using Manta, GRIDSS, and JaBbA modules with downstream processing in GAMBLR. In B-cell lymphomas, the most common SVs identified using the automated workflow were targeting MYC, BCL2, and CCND1. Unsurprisingly, the most common translocation partner among B-cell lymphomas was the immunoglobulin heavy chain, but the novel BCL6-FOXP1, CD274-BACH2, BCL6-RHOH translocations in DLBCLs and MYC-BCL6 translocations in BLs were identified, among others (Figure 1C).Conclusions: We present here the modularized workflow for scalable and automated analysis of genomic and transcriptomic data and demonstrate that it can be successfully deployed across thousands of tumour samples for the discovery of known and novel lymphoma biology. This represents an important advancement in reproducibility that will facilitate clinical translation of genomic discoveries. [Display omitted] DisclosuresGrande: Sage Bionetworks: Current Employment. Coyle: Allakos, Inc.: Consultancy. Steidl: AbbVie: Consultancy; Trillium Therapeutics: Research Funding; Epizyme: Research Funding; Seattle Genetics: Consultancy; Curis Inc.: Consultancy; Bayer: Consultancy; Bristol-Myers Squibb: Research Funding. Scott: Abbvie: Consultancy; NanoString Technologies: Patents & Royalties: Patent describing measuring the proliferation signature in MCL using gene expression profiling.; Celgene: Consultancy; AstraZeneca: Consultancy; Incyte: Consultancy; Janssen: Consultancy, Research Funding; Rich/Genentech: Research Funding; BC Cancer: Patents & Royalties: Patent describing assigning DLBCL COO by gene expression profiling–licensed to NanoString Technologies. Patent describing measuring the proliferation signature in MCL using gene expression profiling. . Morin: Epizyme: Patents & Royalties; Celgene: Consultancy; Foundation for Burkitt Lymphoma Research: Membership on an entity's Board of Directors or advisory committees.

Plotting for change: an analytical framework to aid decisions on which lineages are candidate species in phylogenomic species discovery

Abstract A recent study argued that coalescent-based models of species delimitation mostly delineate population structure, not species, and called for the validation of candidate species using biological information additional to the genetic information, such as phenotypic or ecological data. Here, we introduce a framework to interrogate genomic datasets and coalescent-based species trees for the presence of candidate species in situations where additional biological data are unavailable, unobtainable or uninformative. For de novo genomic studies of species boundaries, we propose six steps: (1) visualize genetic affinities among individuals to identify both discrete and admixed genetic groups from first principles and to hold aside individuals involved in contemporary admixture for independent consideration; (2) apply phylogenetic techniques to identify lineages; (3) assess diagnosability of those lineages as potential candidate species; (4) interpret the diagnosable lineages in a geographical context (sympatry, parapatry, allopatry); (5) assess significance of difference or trends in the context of sampling intensity; and (6) adopt a holistic approach to available evidence to inform decisions on species status in the difficult cases of allopatry. We adopt this approach to distinguish candidate species from within-species lineages for a widespread species complex of Australian freshwater fishes (Retropinna spp.). Our framework addresses two cornerstone issues in systematics that are often not discussed explicitly in genomic species discovery: diagnosability and how to determine it, and what criteria should be used to decide whether diagnosable lineages are conspecific or represent different species.

15 Differentially Expressed Long Non-coding RNA Associated with Gastrointestinal Nematode Infection in Divergent Immune Response Sheep

Abstract Gastrointestinal nematode infection is one of the major production problems for sheep producers worldwide due its high incidence, morbidity, and mortality in affected flocks. The study of long non-coding RNA (lncRNA) in liver tissue of high (HIR) and low immune responder (LIR) sheep to GINs using RNA-Sequencing technology may provide a better understanding of the gene regulation mechanism associated with the host response to the infection. The aim of this study was to identify differentially expressed (DE) lncRNA between HIR and LIR natural infested sheep and control group. Liver tissue samples from the 13 divergent animals (out of a population of 211) based on their immunoglobulin G levels after vaccination using Hen Egg White (HEW) Lysozyme, and immature abomasum worm counts [HIR (> 4000) (n = 5), LIR (< 1500) (n=5) and control (no parasite challenge) (n=4) groups] were used to perform transcriptomic analysis using RNA-Sequencing. The “Large Gap read mapping “and “Transcript Discovery” tools from CLC Genomics Workbench 20.0.4 (CLC Bio, Aarhus, Denmark), were used to map reads to a reference genome (Oar_rambouillet_v1.0) and transcript discovery, respectively. The FEELnc software was used to identify, from predicted transcript model, potential lncRNAs and classify those transcripts into intro putative lncRNAs and protein coding RNAs. As preliminary results, 8 and 48 DE lncRNAs for HIR and LIR compared to control group were identified, respectively using an adjusted p-value False Discovery Rate (FDR) < 0.05 and Fold change (FC) abs > 2. Functional analyses using the list of DE lncRNAs identified metabolic pathways related to immune function. In depth analysis will help to better understand the physiological mechanisms of resilience of high immune sheep.

DarkQ: continuous genomic monitoring using message queues

Newly sequenced genomes are often not noticed by potential stakeholders because submission to public databases is delayed, and search options are limited. However, the discovery of genomes can be vital: in pathogen outbreaks, fast updates are essential to coordinate containment efforts and prevent further spread. Here we introduce DarkQ, a message queue that allows for instant sharing and discovery of genomes. DarkQ is released under the BSD-2 license at github.com/phiweger/darkq.

Atrial Fibrillation Genomics: Discovery and Translation.

Our understanding of the fundamental cellular and molecular factors leading to atrial fibrillation (AF) remains stagnant despite significant advancement in ablation and device technologies. Diagnosis and prevention strategies fall behind that of treatment, but expanding knowledge in AF genetics holds the potential to drive progress. We aim to review how an understanding of the genetic contributions to AF can guide an approach to individualized risk stratification and novel avenues in drug discovery. Rare familial forms of AF identified monogenic contributions to the development of AF. Genome-wide association studies (GWAS) further identified single-nucleotide polymorphisms (SNPs) suggesting polygenic and multiplex nature of this common disease. Polygenic risk scores accounting for the multitude of associated SNPs that each confer mildly elevated risk have been developed to translate genetic information into clinical practice, though shortcomings remain. Additionally, novel laboratory methods have been empowered by recent genetic findings to enhance drug discovery efforts. AF is increasingly recognized as a disease with a significant genetic component. With expanding sequencing technologies and accessibility, polygenic risk scores can help identify high risk individuals. Advancement in digital health tools, artificial intelligence and machine learning based on standard electrocardiograms, and genomic driven drug discovery may be integrated to deliver a sophisticated level of precision medicine in this modern era of emphasis on prevention. Randomized, prospective studies to demonstrate clinical benefits of these available tools are needed to validate this approach.

Multiomic biological approaches to the study of child abuse and neglect

Childhood maltreatment, occurring in up to 20-30% of the population, remains far too common, and incorporates a range of active and passive factors, from abuse, to neglect, to the impacts of broader structural and systemic adversity. Despite the effects of childhood maltreatment and adversity on a wide range of adult physical and psychological negative outcomes, not all individuals respond similarly. Understanding the differential biological mechanisms contributing to risk vs. resilience in the face of developmental adversity is critical to improving preventions, treatments, and policy recommendations. This review begins by providing an overview of childhood abuse, neglect, maltreatment, threat, and toxic stress, and the effects of these forms of adversity on the developing body, brain, and behavior. It then examines examples from the current literature of genomic, epigenomic, transcriptomic, and proteomic discoveries and biomarkers that may help to understand risk and resilience in the aftermath of trauma, predictors of traumatic exposure risk, and potential targets for intervention and prevention. While the majority of genetic, epigenetic, and gene expression analyses to date have focused on targeted genes and hypotheses, large-scale consortia are now well-positioned to better understand interactions of environment and biology with much more statistical power. Ongoing and future work aimed at understanding the biology of childhood adversity and its effects will help to provide targets for intervention and prevention, as well as identify paths for how science, health care, and policy can combine efforts to protect and promote the psychological and physiological wellbeing of future generations.

Life in Seas Frozen and Tropical: For Penguins and Beyond, New Discoveries in Ocean Genomics

Life in Seas Frozen and Tropical: For Penguins and Beyond, New Discoveries in Ocean Genomics

FoGS provides a public FAQ repository for social and behavioral genomic discoveries.

Here we introduce ‘FAQs on Genomic Studies’ (FoGS), an open-access repository of explanatory documents that accompany genomic analyses in social and behavioral genomics. For fields such as social and behavioral genomics that are shaped by an ugly history and uncertain future, socially and ethically responsible research and research communication are crucial. FoGS amplifies one such approach towards responsible research communication.

TYK2 in Cancer Metastases: Genomic and Proteomic Discovery.

Simple SummaryCancer deaths are predominantly due to metastases rather than the primary tumors, and thus there is an urgent need for the discovery of more effective drug therapies for metastatic cancer. Recent genomics, transcriptomics, and proteomics studies have identified tyrosine kinase 2 (TYK2) as an oncogene that is frequently mutated or overexpressed in many types of cancer and metastases. A member of the Janus kinase (JAK) family, TYK2 mediates the signals of numerous cytokines involved in immune and inflammatory signaling. In cancer cells, activation of TYK2 can lead to decreased cell death as well as increased cell growth and invasion. Multiple drugs that specifically block TYK2 or JAKs are currently FDA-approved or in clinical trials. In this review, we provide an overview of the screening, molecular, and animal studies that have characterized the role of TYK2 in cancer and metastases, and the potential of TYK2 inhibitors as effective cancer therapies. Advances in genomic analysis and proteomic tools have rapidly expanded identification of biomarkers and molecular targets important to cancer development and metastasis. On an individual basis, personalized medicine approaches allow better characterization of tumors and patient prognosis, leading to more targeted treatments by detection of specific gene mutations, overexpression, or activity. Genomic and proteomic screens by our lab and others have revealed tyrosine kinase 2 (TYK2) as an oncogene promoting progression and metastases of many types of carcinomas, sarcomas, and hematologic cancers. TYK2 is a Janus kinase (JAK) that acts as an intermediary between cytokine receptors and STAT transcription factors. TYK2 signals to stimulate proliferation and metastasis while inhibiting apoptosis of cancer cells. This review focuses on the growing evidence from genomic and proteomic screens, as well as molecular studies that link TYK2 to cancer prevalence, prognosis, and metastasis. In addition, pharmacological inhibition of TYK2 is currently used clinically for autoimmune diseases, and now provides promising treatment modalities as effective therapeutic agents against multiple types of cancer.

The "GEnomics of Musculo Skeletal Traits TranslatiOnal NEtwork": Origins, Rationale, Organization, and Prospects.

Musculoskeletal research has been enriched in the past ten years with a great wealth of new discoveries arising from genome wide association studies (GWAS). In addition to the novel factors identified by GWAS, the advent of whole-genome and whole-exome sequencing efforts in family based studies has also identified new genes and pathways. However, the function and the mechanisms by which such genes influence clinical traits remain largely unknown. There is imperative need to bring multidisciplinary expertise together that will allow translating these genomic discoveries into useful clinical applications with the potential of improving patient care. Therefore “GEnomics of MusculoSkeletal traits TranslatiOnal NEtwork” (GEMSTONE) aims to set the ground for the: 1) functional characterization of discovered genes and pathways; 2) understanding of the correspondence between molecular and clinical assessments; and 3) implementation of novel methodological approaches. This research network is funded by The European Cooperation in Science and Technology (COST). GEMSTONE includes six working groups (WG), each with specific objectives: WG1-Study populations and expertise groups: creating, maintaining and updating an inventory of experts and resources (studies and datasets) participating in the network, helping to assemble focus groups defined by phenotype, functional and methodological expertise. WG2-Phenotyping: describe ways to decompose the phenotypes of the different functional studies into meaningful components that will aid the interpretation of identified biological pathways. WG3 Monogenic conditions - human KO models: makes an inventory of genes underlying musculoskeletal monogenic conditions that aids the assignment of genes to GWAS signals and prioritizing GWAS genes as candidates responsible for monogenic presentations, through biological plausibility. WG4 Functional investigations: creating a roadmap of genes and pathways to be prioritized for functional assessment in cell and organism models of the musculoskeletal system. WG5 Bioinformatics seeks the integration of the knowledge derived from the distinct efforts, with particular emphasis on systems biology and artificial intelligence applications. Finally, WG6 Translational outreach: makes a synopsis of the knowledge derived from the distinct efforts, allowing to prioritize factors within biological pathways, use refined disease trait definitions and/or improve study design of future investigations in a potential therapeutic context (e.g. clinical trials) for musculoskeletal diseases.

Elucidation of dynamic microRNA regulations in cancer progression using integrative machine learning.

Empowered by advanced genomics discovery tools, recent biomedical research has produced a massive amount of genomic data on (post-)transcriptional regulations related to transcription factors, microRNAs, long non-coding RNAs, epigenetic modifications and genetic variations. Computational modeling, as an essential research method, has generated promising testable quantitative models that represent complex interplay among different gene regulatory mechanisms based on these data in many biological systems. However, given the dynamic changes of interactome in chaotic systems such as cancers, and the dramatic growth of heterogeneous data on this topic, such promise has encountered unprecedented challenges in terms of model complexity and scalability. In this study, we introduce a new integrative machine learning approach that can infer multifaceted gene regulations in cancers with a particular focus on microRNA regulation. In addition to new strategies for data integration and graphical model fusion, a supervised deep learning model was integrated to identify conditional microRNA-mRNA interactions across different cancer stages. In a case study of human breast cancer, we have identified distinct gene regulatory networks associated with four progressive stages. The subsequent functional analysis focusing on microRNA-mediated dysregulation across stages has revealed significant changes in major cancer hallmarks, as well as novel pathological signaling and metabolic processes, which shed light on microRNAs' regulatory roles in breast cancer progression. We believe this integrative model can be a robust and effective discovery tool to understand key regulatory characteristics in complex biological systems. http://sbbi-panda.unl.edu/pin/.

Harnessing rare variants in neuropsychiatric and neurodevelopment disorders-a Keystone Symposia report.

Neurodevelopmental neuropsychiatric disorders, such as autism spectrum disorder and schizophrenia, have strong genetic risk components, but the underlying mechanisms have proven difficult to decipher. Rare, high-risk variants may offer an opportunity to delineate the biological mechanisms responsible more clearly for more common idiopathic diseases. Indeed, different rare variants can cause the same behavioral phenotype, demonstrating genetic heterogeneity, while the same rare variant can cause different behavioral phenotypes, demonstrating variable expressivity. These observations suggest convergent underlying biological and neurological mechanisms; identification of these mechanisms may ultimately reveal new therapeutic targets. At the 2021 Keystone eSymposium "Neuropsychiatric and Neurodevelopmental Disorders: Harnessing Rare Variants" a panel of experts in the field described significant progress in genomic discovery and human phenotyping and raised several consistent issues, including the need for detailed natural history studies of rare disorders, the challenges in cohort recruitment, and the importance of viewing phenotypes as quantitative traits that are impacted by rare variants.

Underlying mechanism of sorafenib resistance in hepatocellular carcinoma: a bioinformatics study based on validated resistance-related genes.

Sorafenib, the first approved targeted therapy for advanced hepatocellular carcinoma (HCC), is often reported to comprised survival-benefit due to resistance. An underlying mechanism of resistance was proposed using bioinformatics analysis based on differentially expressed genes (DEGs) from microarrays. However, most DEGs were invalidated at both the expression level, and the role in causing resistance. Therefore, we conducted a bioinformatics analysis based on experimentally determined sorafenib-resistance-related genes (SRRGs) to elucidate the mechanism of sorafenib resistance. The SRRGs, which have been experimentally determined to promote or inhibit resistance, were collected from published studies. The Database for Annotation, Visualization and Integrated Discovery (DAVID) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to perform Gene Ontology (GO) and pathway enrichment analysis, respectively. A corresponding protein-protein interaction network (PPI) was created using the Cytoscape software program, and network hub genes were proposed. A total of 145 SRRGs, with 117 promoting and 28 inhibiting resistance, were identified. Cell proliferation, migration, development, response to oxygen levels, epithelial-to-mesenchymal transition (EMT), cell skeleton, protein function, and autophagy were all proposed as crucial gene functions related to resistance. The pathways related to cell proliferation or apoptosis, immune function, endocrine metabolism, stem cell function, and differentiation were identified as key resistance-related pathways. A total of 81 hub genes were proposed, including the following top 10 genes: TP53, AKT1, EGFR, STAT3, VEGFA, JUN, MAPK1, IL6, PTEN, and CTNNB1. In conclusion, this study gathered experimentally validated genes that determine sorafenib resistance in HCC, provided an overview of the underlying mechanisms of resistance, and further validated sorafenib resistance in HCC.

Highly contiguous assemblies of 101 drosophilid genomes.

Over 100 years of studies in Drosophila melanogaster and related species in the genus Drosophila have facilitated key discoveries in genetics, genomics, and evolution. While high-quality genome assemblies exist for several species in this group, they only encompass a small fraction of the genus. Recent advances in long-read sequencing allow high-quality genome assemblies for tens or even hundreds of species to be efficiently generated. Here, we utilize Oxford Nanopore sequencing to build an open community resource of genome assemblies for 101 lines of 93 drosophilid species encompassing 14 species groups and 35 sub-groups. The genomes are highly contiguous and complete, with an average contig N50 of 10.5 Mb and greater than 97% BUSCO completeness in 97/101 assemblies. We show that Nanopore-based assemblies are highly accurate in coding regions, particularly with respect to coding insertions and deletions. These assemblies, along with a detailed laboratory protocol and assembly pipelines, are released as a public resource and will serve as a starting point for addressing broad questions of genetics, ecology, and evolution at the scale of hundreds of species.

Biosynthetic Potential of Streptomyces Rationalizes Genome-Based Bioprospecting.

Streptomyces are the most prolific source of structurally diverse microbial natural products. Advancing genome-based analysis reveals the previously unseen potential of Streptomyces to produce numerous novel secondary metabolites, which allows us to take natural product discovery to the next phase. However, at present there is a huge disproportion between the rate of genome reports and discovery of new compounds. From this perspective of harnessing the enduring importance of Streptomyces, we discuss the recent genome-directed advancements inspired by hidden biosynthetic wealth that provide hope for future antibiotics.

Genomics-informed responses in the elimination of COVID-19 in Victoria, Australia: an observational, genomic epidemiological study

BackgroundA cornerstone of Australia's ability to control COVID-19 has been effective border control with an extensive supervised quarantine programme. However, a rapid recrudescence of COVID-19 was observed in the state of Victoria in June, 2020. We aim to describe the genomic findings that located the source of this second wave and show the role of genomic epidemiology in the successful elimination of COVID-19 for a second time in Australia.MethodsIn this observational, genomic epidemiological study, we did genomic sequencing of all laboratory-confirmed cases of COVID-19 diagnosed in Victoria, Australia between Jan 25, 2020, and Jan 31, 2021. We did phylogenetic analyses, genomic cluster discovery, and integrated results with epidemiological data (detailed information on demographics, risk factors, and exposure) collected via interview by the Victorian Government Department of Health. Genomic transmission networks were used to group multiple genomic clusters when epidemiological and genomic data suggested they arose from a single importation event and diversified within Victoria. To identify transmission of emergent lineages between Victoria and other states or territories in Australia, all publicly available SARS-CoV-2 sequences uploaded before Feb 11, 2021, were obtained from the national sequence sharing programme AusTrakka, and epidemiological data were obtained from the submitting laboratories. We did phylodynamic analyses to estimate the growth rate, doubling time, and number of days from the first local infection to the collection of the first sequenced genome for the dominant local cluster, and compared our growth estimates to previously published estimates from a similar growth phase of lineage B.1.1.7 (also known as the Alpha variant) in the UK.FindingsBetween Jan 25, 2020, and Jan 31, 2021, there were 20 451 laboratory-confirmed cases of COVID-19 in Victoria, Australia, of which 15 431 were submitted for sequencing, and 11 711 met all quality control metrics and were included in our analysis. We identified 595 genomic clusters, with a median of five cases per cluster (IQR 2–11). Overall, samples from 11 503 (98·2%) of 11 711 cases clustered with another sample in Victoria, either within a genomic cluster or transmission network. Genomic analysis revealed that 10 426 cases, including 10 416 (98·4%) of 10 584 locally acquired cases, diagnosed during the second wave (between June and October, 2020) were derived from a single incursion from hotel quarantine, with the outbreak lineage (transmission network G, lineage D.2) rapidly detected in other Australian states and territories. Phylodynamic analyses indicated that the epidemic growth rate of the outbreak lineage in Victoria during the initial growth phase (samples collected between June 4 and July 9, 2020; 47·4 putative transmission events, per branch, per year [1/years; 95% credible interval 26·0–85·0]), was similar to that of other reported variants, such as B.1.1.7 in the UK (mean approximately 71·5 1/years). Strict interventions were implemented, and the outbreak lineage has not been detected in Australia since Oct 29, 2020. Subsequent cases represented independent international or interstate introductions, with limited local spread.InterpretationOur study highlights how rapid escalation of clonal outbreaks can occur from a single incursion. However, strict quarantine measures and decisive public health responses to emergent cases are effective, even with high epidemic growth rates. Real-time genomic surveillance can alter the way in which public health agencies view and respond to COVID-19 outbreaks.FundingThe Victorian Government, the National Health and Medical Research Council Australia, and the Medical Research Future Fund.

Synchronized Tissue Acquisition Techniques for Novel Biomarker Discovery: Are You Ready to Waltz?

Synchronized Tissue Acquisition Techniques for Novel Biomarker Discovery: Are You Ready to Waltz?