Evolution of variable lymphocyte receptor B antibody loci in jawless vertebrates

Three types of variable lymphocyte receptor (VLR) genes, VLRA, VLRB, and VLRC, encode antigen recognition receptors in the extant jawless vertebrates, lampreys and hagfish. The somatically diversified repertoires of these VLRs are generated by serial stepwise copying of leucine-rich repeat (LRR) sequences into an incomplete germline VLR gene. Lymphocytes that express VLRA or VLRC are T cell–like, while VLRB-expressing cells are B cell–like. Here, we analyze the composition of the VLRB locus in different jawless vertebrates to elucidate its configuration and evolutionary modification. The incomplete germline VLRB genes of two hagfish species contain short noncoding intervening sequences, whereas germline VLRB genes in six representative lamprey species have much longer intervening sequences that exhibit notable genomic variation. Genomic clusters of potential LRR cassette donors, fragments of which are copied to complete VLRB gene assembly, are identified in Japanese lamprey and sea lamprey. In the sea lamprey, 428 LRR cassettes are located in five clusters spread over a total of 1.7 Mbp of chromosomal DNA. Preferential usage of the different donor cassettes for VLRB assemblage is characterized in our analysis, which reveals evolutionary modifications of the lamprey VLRB genes, elucidates the organization of the complex VLRB locus, and provides a comprehensive catalog of donor VLRB cassettes in sea lamprey and Japanese lamprey.

Identifying and characterizing lincRNA genomic clusters reveals its cooperative functions in human cancer

Background Emerging evidence has revealed that some long intergenic non-coding RNAs (lincRNAs) are likely to form clusters on the same chromosome, and lincRNA genomic clusters might play critical roles in the pathophysiological mechanism. However, the comprehensive investigation of lincRNA clustering is rarely studied, particularly the characterization of their functional significance across different cancer types. Methods In this study, we firstly constructed a computational method basing a sliding window approach for systematically identifying lincRNA genomic clusters. We then dissected these lincRNA genomic clusters to identify common characteristics in cooperative expression, conservation among divergent species, targeted miRNAs, and CNV frequency. Next, we performed comprehensive analyses in differentially-expressed patterns and overall survival outcomes for patients from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) across multiple cancer types. Finally, we explored the underlying mechanisms of lincRNA genomic clusters by functional enrichment analysis, pathway analysis, and drug-target interaction. Results We identified lincRNA genomic clusters according to the algorithm. Clustering lincRNAs tended to be co-expressed, highly conserved, targeted by more miRNAs, and with similar deletion and duplication frequency, suggesting that lincRNA genomic clusters may exert their effects by acting in combination. We further systematically explored conserved and cancer-specific lincRNA genomic clusters, indicating they were involved in some important mechanisms of disease occurrence through diverse approaches. Furthermore, lincRNA genomic clusters can serve as biomarkers with potential clinical significance and involve in specific pathological processes in the development of cancer. Moreover, a lincRNA genomic cluster named Cluster127 in DLK1-DIO3 imprinted locus was discovered, which contained MEG3, MEG8, MEG9, MIR381HG, LINC02285, AL132709.5, and AL132709.1. Further analysis indicated that Cluster127 may have the potential for predicting prognosis in cancer and could play their roles by participating in the regulation of PI3K-AKT signaling pathway. Conclusions Clarification of the lincRNA genomic clusters specific roles in human cancers could be beneficial for understanding the molecular pathogenesis of different cancer types.

Evolution of the Parvalbumin Genes in Teleost Fishes after the Whole-Genome Duplication

Parvalbumin is considered a major fish allergen. Here, we report the molecular evolution of the parvalbumin genes in bony fishes based on 19 whole genomes and 70 transcriptomes. We found unexpectedly high parvalbumin diversity in teleosts; three main gene types (pvalb-α, pvalb-β1, and pvalb-β2, including oncomodulins) originated at the onset of vertebrates. Teleosts have further multiplied the parvalbumin gene repertoire up to nine ancestral copies—two copies of pvalb-α, two copies of pvalb-β1, and five copies of pvalb-β2. This gene diversity is a result of teleost-specific whole-genome duplication. Two conserved parvalbumin genomic clusters carry pvalb-β1 and β2 copies, whereas pvalb-α genes are located separately in different linkage groups. Further, we investigated parvalbumin gene expression in 17 tissues of the common carp (Cyprinus carpio), a species with 21 parvalbumin genes in its genome. Two pvalb-α and eight pvalb-β2 copies are highly expressed in the muscle, while two alternative pvalb-α copies show expression in the brain and the testes, and pvalb-β1 is dominant in the retina and the kidney. The recent pairs of muscular pvalb-β2 genes show differential expression in this species. We provide robust genomic evidence of the complex evolution of the parvalbumin genes in fishes.

Documenting elimination of co-circulating COVID-19 clusters using genomics in New South Wales, Australia

Objective To adapt ‘fishplots’ to describe real-time evolution of SARS-CoV-2 genomic clusters. Results This novel analysis adapted the fishplot to depict the size and duration of circulating genomic clusters over time in New South Wales, Australia. It illuminated the effectiveness of interventions on the emergence, spread and eventual elimination of clusters and distilled genomic data into clear information to inform public health action.

MDS and MDS/MPN Genomic Subgroups Demonstrate Differential E x Vivo Drug Sensitivity

Background Myelodysplastic syndromes (MDS) and MDS/MPNs are heterogeneous disorders with various combinations of mutations and cytogenetic abnormalities associated with distinct clinical phenotypes, prognosis, and implications for targeted therapies. We previously demonstrated that ex vivo drug sensitivity screening (DSS) identified subgroups of MDS and MDS/MPNs with differing patterns of sensitivity to various drug classes including hypomethylating agents (HMAs), kinase inhibitors, and other small molecules. In this study, we used hierarchical clustering to identify MDS and MDS/MPN genomic subgroups in a large single-center cohort. We then examined associations between these genomic subgroups and ex vivo sensitivity to various drug classes in a cohort of patients with ex vivo DSS. Methods Patients: We identified 294 patients with MDS or MDS/MPNs who had cytogenetics and HemeSTAMP NGS panel (164 genes) performed at Stanford between June 2018 and June 2021. A separate, partially overlapping cohort of 60 patients had ex vivo DSS as described below. Genomic clusters: We used a hierarchical Dirichlet Process (HDP), incorporating mutations and cytogenetics, to identify genomic subgroups. We included pathogenic and likely pathogenic variants with VAF >2% and excluded variants of unknown significance. Ex vivo DSS: Fresh bone marrow aspirates and peripheral blood specimens were RBC-lysed and resuspended in serum-free media with cytokines (Spinner et al, Blood Adv 2020;4(12):2768-78). Samples were plated in 384-well microtiter plates and screened against a collection of up to 74 drugs and 36 drug combinations in triplicate. Specimens were treated for 72 hours and assayed using flow cytometry to assess for blast viability. Statistical analysis: An HDP model was trained on the cohort of 294 patients. To tune the hyperparameters of the model, the log-likelihood of the test data was optimized using cross validation combined with Gaussian Process Bayesian optimization. Inference using the trained model was performed on 60 patients with ex vivo DSS producing a genomic component distribution for each patient. Jensen-Shannon distance was then computed between each pair of patients using their genomic component distributions. Patients were then clustered via agglomerative clustering (average linkage and using a maximum distance cutoff of 0.5) using this distance matrix. Ex vivo sensitivity to drug classes was then compared across clusters using ANOVA on drug sensitivity per drug class averaged over each patient. Results Patient characteristics: Among all 294 patients, the median age was 73 years, 78% had MDS, 16% had CMML, and 6% had other MDS/MPNs. 45% had >5% blasts and 53% had higher risk disease with IPSS-R >3.5. 94% had at least 1 mutation or cytogenetic abnormality with a median of 2 mutations (range 0-7). Among the 60 patients with ex vivo DSS, the median age was 77 years, 82% had MDS, 18% had CMML or other MDS/MPN, 55% had >5% blasts, and 67% had higher risk disease. Genomic subgroups and clusters: An HDP model trained on all 294 patients identified 16 genomic subgroups. Applying these genomic subgroups to the 60 patients with ex vivo DSS, we identified 12 genomic clusters, of which 6 clusters were most common: cluster 0 (enriched for RUNX1/BCOR mutations, N=6), cluster 1 (enriched for TET2/SRSF2/ASXL1, N=13), cluster 3 (enriched for DNMT3A, N=8), cluster 6 (enriched for KRAS/NRAS, N=5), cluster 7 (enriched for STAG2/ASXL1, N=6), and cluster 10 (enriched for TP53/complex cytogenetics, N=5). Associations between genomic clusters and drug sensitivity: Ex vivo drug sensitivity for 60 patients, organized by genomic cluster, is shown in Figure 1A. Ex vivo sensitivity to various drug classes is shown for the most common clusters in Figure 1B. Cluster 10 (enriched for TP53/complex cytogenetics) demonstrated greater ex vivo sensitivity to proteasome inhibitors (p=0.018). In Cluster 6 (enriched for NRAS/KRAS), there was a trend towards greater ex vivo resistance to HMAs and PARP inhibitors (p=0.1 for both comparisons). Conclusions Hierarchical clustering identified distinct genomic subgroups of MDS and MDS/MPNs, which displayed differing ex vivo sensitivity to various drug classes. While the small sample size limits our analysis, these associations between genotype and drug sensitivity phenotype are hypothesis generating and have potential implications for personalized therapy in MDS and MDS/MPNs. Figure 1 Figure 1. Disclosures Spinner: Notable Labs: Honoraria. Schaffert: Notable Labs: Consultancy, Current holder of stock options in a privately-held company, Ended employment in the past 24 months. Santaguida: Notable Labs: Consultancy, Current holder of individual stocks in a privately-held company. Kita: Notable Labs: Current Employment, Current holder of stock options in a privately-held company. Aleshin: Notable Labs: Consultancy. Greenberg: Notable Labs: Research Funding.

Genomic Subgroups Impact Post-Transplant Survival in Patients with Myelodysplastic Syndrome: A CIBMTR Analysis

Background Myelodysplastic syndromes (MDS) are clonal stem cell malignancies characterized by cytopenia, inefficient hematopoiesis, dysplasia in one or more myeloid cell lineages and increased risk of development of acute myeloid leukemia. It has been well appreciated that genomic alterations play a key role in MDS pathogenesis. The Revised International Prognostic Scoring System (IPSS-R) algorithm is commonly used to predict overall survival but may fail to recapitulate reliable prognostic information at the individual patient level, especially at the time of hematopoietic cell transplantation (HCT). Current World Health Organization (WHO) classification includes MDS with isolated 5q deletion as the only genetically defined category. Comprehensive analysis of recurrent genomic features by unsupervised clustering empowers discovery of potential prognostic molecular signatures. Methods Using whole blood samples obtained from 494 MDS patients at the time of HCT, we conducted whole-genome sequencing (WGS) and somatic variant processing via a custom analytic pipeline based on OCTOPUS and a set of annotation databases. Multiple filters allowed for selection and fine-tuning of criteria, including removal of variants with Gnomad allele frequency above 10x10 -06, removal of noncoding variants in low complexity and repetitive regions, those with no functional indications from ANNOVAR annotations, CADD conservative score under 15, and absence in HGMD or COSMIC databases. Highly annotated clinical data, including cytogenetic abnormalities at the latest time point prior to HCT, were obtained from CIBMTR forms. K-means clustering was applied to recurrent mutations and cytogenetic abnormalities to identify clinically relevant genomic subtypes. The optimal cluster number was determined by Gap-status algorithm. Statistics of clinical characteristics were compared among different genomic subgroups by Chi-squared test for categorical variables and Mann-Whitney U test for continuous variables. Overall survival association tests were conducted by Cox multivariate models. Relapse and transplant-related risk were performed by competing risk analysis using Fine-Gray models. Models were adjusted for patient-, disease-, and HCT-related factors. Results The somatic genomic landscape in our MDS cohort was examined for the total count of recurrent mutations at the sample level and gene level. Among 53 recurrently mutated genes in 257 of 494 MDS cases, TP53, TET2, RUNX1, DNMT3A, and ASXL1 were the most frequently mutated genes in our MDS cohort. Based on k-means clustering of the recurrent mutational and cytogenetic data, we detected five clusters that stratified our MDS patient cohort, including one reference cluster with no recurrent somatic mutations or cytogenetic abnormalities. Compared to the reference subgroup, significantly higher cytogenetic scores and IPSS-R scores were observed in genomic clusters with TP53 mutations (cytogenetic score: P=3.42E-07*; IPSS-R score: P= 2.38E-10*) and cytogenetic abnormalities del5q, or tri8p (cytogenetic score: P= 2.38E-10*; IPSS-R score: P=0.09) , or mono7 (cytogenetic score: P=3.29E-13*; IPSS-R score: P=1.38E-05*) (data not shown). Cox multivariate models revealed that genomic clusters with TP53 and del5q mutations (P<0.001*) or tri8p (P=0.02*) mutations have strong associations with post-transplant overall survival outcome (Figure 1A). Furthermore, competing risk analysis confirmed significantly higher risk of relapse in genomic subgroups with TP53 and del5q mutations in the reduced intensity conditioning regimen setting (P=0.01) (Figure 1B), while significantly higher risk of transplant-related mortality was found in the genomic subgroup with tri8p in the myeloablative conditioning regimen setting (P=0.03) (Figure 1C). Conclusion Our study suggests that molecular signatures from MDS patient genomes at HCT may provide an independent prognosis of post-transplant survival. Additionally, our data suggests that the choice of regimen intensity could be informed by knowledge of the individual genomic signature of a given MDS patient. Figure 1 Figure 1. Disclosures Saber: Govt. COI: Other.

Clinical sequencing of soft tissue and bone sarcomas delineates diverse genomic landscapes and potential therapeutic targets

The genetic, biologic, and clinical heterogeneity of sarcomas poses a challenge for the identification of therapeutic targets, clinical research, and advancing patient care. Because there are > 100 sarcoma subtypes, in-depth genetic studies have focused on one or a few subtypes. Herein, we report a comparative genetic analyses analysis of 2,138 sarcomas representing 45 pathological entities. This cohort was prospectively analyzed using targeted sequencing to characterize subtype-specific somatic alterations in targetable pathways, rates of whole genome doubling, mutational signatures, and subtype-agnostic genomic clusters. The most common alterations were in cell cycle control and TP53, receptor tyrosine kinases/PI3K/RAS, and epigenetic regulators. Subtype-specific associations included TERT amplification in intimal sarcoma and SWI/SNF alterations in uterine adenosarcoma. Tumor mutational burden, while low compared to other cancers, varied between and within subtypes. This resource will improve sarcoma models, motivate studies of subtype-specific alterations, and inform investigations of genetic factors and their correlations with treatment response.

Documenting Elimination of Co-Circulating Covid-19 Clusters Using Genomics in New South Wales, Australia

Objective: To adapt ‘fishplots’ to describe SARS-CoV-2 genomic cluster evolution. Results: This novel analysis adapted the fishplot to depict the size and duration of circulating genomic clusters over time in New South Wales, Australia. It illuminated the effectiveness of interventions on the emergence, spread and eventual elimination of clusters and distilled genomic data into clear information to inform public health action.

Chromosome-scale assembly of the Canary Island endemic spider Dysdera silvatica (Arachnida, Araneae) sheds light on the origin and genome structure of chemoreceptor gene families in spiders

We present the chromosome-level genome assembly of Dysdera silvatica Schmidt, 1981, a nocturnal ground-dwelling spider endemic from the Canary Islands. The genus Dysdera has undergone a remarkable diversification in this archipelago mostly associated with shifts in the level of trophic specialization, becoming an excellent model to study the genomic drivers of adaptive radiations. The new assembly (1.37 Gb; and scaffold N50 of 174.2 Mb), was performed using the chromosome conformation capture scaffolding technique, represents a continuity improvement of more than 4,500 times with respect to the previous version. The seven largest scaffolds or pseudochromosomes cover 87% of the total assembly size and match consistently with the seven chromosomes of the karyotype of this species, including the characteristic large X chromosome. To illustrate the value of this new resource we performed a comprehensive analysis of the two major arthropod chemoreceptor gene families (i.e., gustatory and ionotropic receptors). We identified 545 chemoreceptor sequences distributed across all pseudochromosomes, with a notable underrepresentation in the X chromosome. At least 54% of them localize in 83 genomic clusters with a significantly lower evolutionary distances between them than the average of the family, suggesting a recent origin of many of them. This chromosome-level assembly is the first high-quality genome representative of the Synspermiata clade, and just the third among spiders, representing a new valuable resource to gain insights into the structure and organization of chelicerate genomes, including the role that structural variants, repetitive elements and large gene families played in the extraordinary biology of spiders.