Distinct Clusters Research Articles

Raman Spectroscopy Imaging in Evaluation of Immune Cells Activation Status

Introduction: The ability to assess the activation status of immune cells is useful for medical diagnostics and currently also for monitoring the performance of immune cells-based advanced therapy medicinal products (ATMP) such as the chimeric antigen receptor (CAR)-T cells. CAR-T cells numbers and immunophenotype are routinely monitored to avoid poor expansion or exhaustion phenotype. In this study, we utilized Raman spectroscopy (RS) as a label-free, non-destructive method for assessing T cells activation and macrophages polarization (pro-inflammatory M1 vs immunosuppressive M2). RS is a well-defined, non-elastic light scattering-based spectroscopic technique that followed by chemometric analysis provides unique and comprehensive information regarding the molecular structure, metabolic signature and biochemical composition of tissues and cells at subcellular level. To assess if RS would be a valid approach for detection of CAR-T activation we first developed CD19CAR-transduced Jurkat cell line model. Although Jurkat cells, a leukemic T cell line, lack cytolytic activity, they do produce IL-2 and upregulate CD69 upon activation like CAR-T thus serve as a convenient model. Materials and Methods: T cells and monocyte were isolated from healthy donors' blood. T cells were activated with coated CD3/CD28 beads. Jurkat cells were transduced with anti-CD19 chimeric TCR. CAR-J cells activation upon contact with CD19+ cells were confirmed by CD69 expression (flow cytometry) and calcium fluxes (fluorescence microscopy). Monocyte were differentiated to macrophages using CSF1, while THP1 monocytic cell line with PMA. Macrophages were subsequently polarized into M1 and M2 with IL-4 and IL-13 or IFN-gamma and LPS respectively. M0 macrophages or naïve T cells were used as a reference and control. Cell phenotypes were confirmed using flow cytometry, q-PCR, fluorescent confocal microscopy and proliferation assay. Cells were fixed in 0.5% glutaraldehyde and imaged at 533 and 633 nm with a WITec Alpha 300 Raman microscope. Collected data were analyzed by chemometric methods: K-means cluster analysis (KMCA) followed by Partial Least Squares-Discrimination Analysis (PLS-DA) and Principal Component Analysis (PCA). Results: RS analysis of polarized macrophages showed the increased signal from hemoproteins (740-760 cm -1) in all three states. Interestingly we observed different hemoproteins to lipids (1440, 1240, 1660, 2850 cm -1) ratio between M0, M1 and M2. Also degree of lipid unsaturation can be considered an important parameter in the identification of the M1 phenotype of macrophages. Chemometric analyzes revealed significant morphological (increased cell and nucleus sizes) biochemical and difference between activated T lymphocytes and naive cells. Carotenoids present in non-activated T cells disappeared after activation. Conversely, lipids in the form of distinct clusters were observed after activation. Moreover, in the spectra of cell nuclei (733, 790, 1583 cm -1) of activated lymphocytes, less condensed chromatin was manifested by a lower intensity of marker bands. We observed similar changes for activated CAR-Jurkat, though the morphological changes were more subtle and carotenoids were not observed in Jurkat cells. Conclusions: The obtained results indicate the potential utility of Raman spectroscopy methods in identifying lymphocyte states activation and macrophage polarization . Acknowledgements. The studies were performed as a part of the “ Label-free and rapid optical imaging, detection and sorting of leukemia cells” project carried out within the Team-Net programme of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund.

IRF4-BLOC1S5, the First Genomic Fusion Gene Identified in the TEMPI Syndrome

The TEMPI syndrome is a rare multi-systemic disease, characterize by telangiectasias, elevated erythropoietin and erythrocytosis, monoclonal gammopathy, perinephric fluid collections, and intrapulmonary shunting (Sykes et al, N Engl J Med 2011). Plasma cell is supposed to play a pathogenic role in the syndrome (Sykes et al, Blood 2020), but the underlying molecular mechanism remains poorly understood. Here, we introduced a novel IRF4-BLOC1S5 fusion gene in the TEMPI syndrome, highly expressed in the monoclonal plasma cell population. We presented one case of the TEMPI syndrome with typical clinical symptoms. The patient received bortezomib and dexamethasone treatment. After 4 cycles of treatment, the patient achieved a satisfied response with a negative MRD. The symptoms were relieved, with the disappearance of the cyanosis, telangiectasias, perinephric-fluid collection, and serum M protein, amelioration of intrapulmonary shunting, normalization of erythrocyte count and erythropoietin level. To explore the possible mechanism of the TEMPI syndrome, we performed whole genome sequencing and transcriptome analysis to identify genomic and transcriptomic abnormalities of our patient. WGS of paired tumor (CD138 + plasma cells) and normal samples revealed a somatic inversion on chromosome 6. Bulk RNA-Seq showed that this inversion caused an out-of-frame fusion transcript IRF4-BLOC1S5, which generated a short N-terminal-truncated IRF4 protein. Western blotting confirmed that the corresponding truncated-IRF4 protein was expressed in the CD138 + plasma cells, but not CD138 - cells of this patient and normal donors, as well as MM samples. We performed single cell RNA-sequencing to identify distinct clusters of this patient and found a strong expansion (11.2-fold) of plasma population companied with high expression of truncated-IRF4 in our patient in the case and this unique expanded plasma cluster expressed one specific immunoglobulin clonotype sharing the same CDR3 sequence. Functional analysis illustrated that differentially expressed genes in plasma cells between this patient and healthy controls were enriched in the KEGG pathways such as ribosome and protein processing in endoplasmic reticulum. Besides, the expression of IRF4-BLOC1S5 was only detected at diagnosis and significantly decreased after the BD treatment and in the clinical follow-up (up to 18 months). IRF4-BLOC1S5 may serve as a molecular marker to surveille the disease progression of this patient. In conclusion, IRF4-BLOC1S5 is the first fusion gene reported in the TEMPI syndrome. IRF4-BLOC1S5 was highly proposed to play an important role in the pathophysiology of the TEMPI syndrome based on the high correlation between IRF4-BLOC1S5 expression level and symptoms of the syndrome. More molecular events responsible for the TEMPI syndrome still await to be identified, albeit a ultrarare occurrence of this disease. An intensive international collaboration is strongly proposed to completely elucidate its molecular mechanisms.

Spatial Multiomics Profiling of Angioimmunoblastic T-Cell Lymphoma

Introduction: Angioimmunoblastic T-cell lymphoma (AITL) is a subtype of non-Hodgkin lymphoma (NHL) characterized primarily by a T follicular helper cell (TFH) phenotype. AITL presents with a highly complex tumor microenvironment with the tumor cells on a scaffold of highly arborized CD21+ follicular dendritic cells found in proximity to high endothelial venules (HEVs). The organized structure of the lymph node with distinct B cell and T cell zones is disrupted in the lymph node samples from patients with AITL. Lymphoid follicles in most AITL cases tend to be depleted, not hyperplastic as in other lymphomas. AITL tumor cells express CD10, BCL6, PD1, CXCL13, C-X-C motif chemokine receptor 5 (CXCR5), ICOS, and signaling lymphocytic activation molecule-associated protein (SAP) with diagnosis based on the expression of at least 2 of these markers. Our study uses novel spatial omics technology (Deterministic Barcoding in Tissue (DBiT-seq) and multiplexed immunofluorescence staining (CODEX)) to fully characterize the transcriptional and proteomic landscape of AITL. Methods: In this study, initial FFPE biopsy specimens from 22 patients with AITL were collected from archives. 10 µm thick tissue sections on poly-L-lysine coated slides were prepared for the performance of multiplexed immunofluorescence imaging (CODEX) for 12 immune-related markers: Podoplanin, CD20, CD4, CD8, CD68, CD45RO, CD3e, GranzymeB, PD1, Ki67, CD21 and CD34 using the PhenoCycler-Fusion system. We also validated anti-EBV Latent Membrane Protein 1 antibody. Cell segmentation was performed using a Stardist-based model in QuPath and downstream analysis done using the Seurat package. On an adjacent tissue section, we used Spatial CITE-seq to co-map the whole transcriptome and ~160 proteins. Results: From the multiplexed immunofluorescence staining, we observed CD21+ Follicular dendritic cells in close proximity to HEVs as expected from the spatial architecture of AITL. We also found 11 spatially distinct clusters differentially expressing the marker proteins. We observed a lower number of CD8+ cytotoxic T cells relative to CD4+ Helper T cells. We also identified cellular neighborhoods and their composition found a population of FDCs that directly interact with the tumor cells. From spatial CITE-seq, we average of ~670 genes were found in each spot. We also found the expression of CXCL13, TET2, DNMT3A and PDCP1 which are markers associated with AITL. There was a strong correlation between the transcript signals detected in Spatial CITE-seq and the protein expression from CODEX. Conclusions: Our study revealed the spatial organization of the tumor microenvironment of AITL. We envision that combining multiplexed immunofluorescence and Spatial CITE-seq will further our understanding of the AITL tumor environment and could lead to the discovery of novel clinical targets.

GC-MS Fingerprinting Combined with Chemical Pattern-Recognition Analysis Reveals Novel Chemical Markers of the Medicinal Seahorse

Seahorse is a valuable marine-animal drug widely used in traditional Chinese medicine (TCM), and which was first documented in the “Ben Cao Jing Ji Zhu” during the Liang Dynasty. Hippocampus kelloggi (HK) is the most common seahorse species in the medicinal material market and is one of the genuine sources of medicinal seahorse documented in the Chinese pharmacopeia. It is mainly cultivated in the Shandong, Fujian, and Guangxi Provinces in China. However, pseudo-HK, represented by Hippocampus ingens (HI) due to its similar appearance and traits, is often found in the market, compromising the safety and efficacy of clinical use. Currently, there is a lack of reliable methods for identifying these species based on their chemical composition. In this study, we employed, for the first time, a strategy combining gas chromatography-mass spectrometry (GC-MS) fingerprints and chemical patterns in order to identify HK and HI; it is also the first metabolomic study to date of HI as to chemical components. The obtained results revealed remarkable similarities in the chemical fingerprints, while significant differences were also observed. By employing hierarchical cluster analysis (HCA) and principal component analysis (PCA), based on the relative contents of their characteristic peaks, all 34 samples were successfully differentiated according to their species of origin, with samples from the same species forming distinct clusters. Moreover, nonadecanoic acid and behenic acid were exclusively detected in HK samples, further distinguishing them from HI samples. Additionally, the relative contents of lauric acid, tetradecanoic acid, pentadecanoic acid, n-hexadecanoic acid, palmitoleic acid, margaric acid, oleic acid, fenozan acid, eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA) exhibited significant differences between HK and HI (p < 0.0001), as determined by an unpaired t-test. Orthogonal partial least squares discriminant analysis (OPLS-DA) identified seven components (DHA, EPA, n-hexadecanoic acid, tetradecanoic acid, palmitoleic acid, octadecanoic acid, and margaric acid) with high discriminatory value (VIP value > 1). Thus, nonadecanoic acid, behenic acid, and these seven compounds can be utilized as chemical markers for distinguishing HK from HI. In conclusion, our study successfully developed a combined strategy of GC-MS fingerprinting and chemical pattern recognition for the identification of HK and HI, and we also discovered chemical markers that can directly differentiate between the two species. This study can provide a foundation for the authentication of Hippocampus and holds significant importance for the conservation of wild seahorse resources.

Data-Driven Harmonization of 2022 Who and ICC Classifications of Myelodysplastic Syndromes/Neoplasms (MDS): A Study By the International Consortium for MDS (icMDS)

Background. The inclusion of gene mutations and chromosomal abnormalities in the 2022 WHO and ICC Classifications of MDS has enhanced diagnostic precision and is expected to improve clinical decision-making process. Although these two systems share similarities, clinically relevant discrepancies still exist and potentially cause inconsistency in their adoption in a clinical setting. In this study on behalf of the International Consortium for MDS (icMDS), we adopted a data-driven approach to provide a harmonization roadmap between the 2022 WHO and ICC classification for MDS. A modified Delphi Process consensus approach is currently ongoing among icMDS experts to finalize a harmonized MDS classification scheme. Methods. We analyzed retrospective international cohorts of patients with a diagnosis of MDS (n=7017) and AML (n=1002) according to WHO 2016 criteria. Hierarchical Dirichlet Processes were applied to define clusters capturing broad dependencies among all gene mutations and cytogenetic abnormalities. To investigate the features of importance and their impact on the clustering process, we employed the SHapley Additive exPlanations approach (SHAP). This allowed to define harmonized labels for each clinical entity. The clinical relevance of the unsupervised clustering was assessed through the analysis of phenotypic features and outcomes among each group. ( Blood 2022;140: 9828-9830) Results. Patients' characteristics are summarized in Table 1. We identified 9 clusters, defined by specific genomic features. The cluster of highest hierarchical importance was characterized by biallelic inactivation of TP53 (biTP53). According to SHAP analysis, bi TP53 was defined as 2 or more TP53 mutations, or 1 mutation with copy number loss or cnLOH. Most patients assigned to bi TP53 cluster had TP53 VAF>10% (77.9%) and complex karyotype (70.1%). Assignment to bi TP53 cluster was irrespective of blast count. Patients with monoallelic TP53 mutation segregated into other clusters. Hierarchically, the second cluster included patients with del(5q). SHAP analysis highlighted 5q deletion alone, or with one other chromosomal abnormality other than -7, and absence of bi TP53, as the most relevant features. Most of these patients had blast counts <5% (88.1%). The third distinct cluster included patients with SF3B1 mutations (in the absence of concurrent del(7q), abn3q26.2, complex karyotype or RUNX1 mutation). Most patients with MDS and SF3B1 mutation had <5% blasts (94.2%). Common co-mutated variants in the SF3B1 cluster included mutant DNMT3A (25.2%) and TET2 (38.3%). Morphologically defined MDS cases (i.e., not meeting criteria for bi TP53, del(5q) or SF3B1) were preferentially assigned to the following additional clusters: SF3B1 and concurrent higher-risk mutations (e.g., RUNX1 and ASXL1); SRSF2 and concomitant TET2 mutations; U2AF1 mutations with del(20q), del(7q) or -7; SRSF2 with TET2 mutations and co-mutational patterns including RUNX1 and ASXL1; and AML-like genomic signatures. Our analyses suggest that morphologically defined MDS is characterized by a large heterogeneity in terms of mutation profiles, not entirely captured by the presence of unilineage versus multilineage dysplasia, percentage of bone marrow blasts, and presence of hypocellularity and fibrosis. To better investigate the continuum between high risk MDS (i.e., patients with ≥10% blasts) and AML, an exploratory comparison was made using a cohort of AML (defined according to WHO 2016) patients analyzed using the same statistical methods. Only a partial overlap in genetic signatures was observed between MDS with ≥10% blasts and AML. However, similarities were observed between the AML-like MDS clusters (characterized by mutant NPM1, bZIP CEBPA, and Core Binding Factor abnormalities) and AML clusters defined by the same genetic signature, thus supporting the classification of these entities as AML, irrespective of blast count. Conclusion. Our study demonstrated the utility of a data-driven approach based on advanced statistical methods to generate a harmonized classification for MDS. Table 2 shows a provisional, hierarchical classification algorithm. Further refinement of entity labels and classification criteria is the subject of the ongoing modified Delphi Process consensus approach among icMDS experts.

Transcriptomic Signature and Functional Abnormalities of Bone Marrow Mesenchymal Stromal Cells Mediate Disease Progression of Myelodysplastic/Myeloproliferative Neoplasms

Background Mesenchymal stromal cells (MSC) are vital components of the bone marrow (BM) microenvironment niche and regulate hematopoietic stem and progenitor cell (HSPC) activity. While HSPC harbor primary disease driver mutations, MSC in myelodysplastic syndrome and myeloproliferative neoplasm (MDS/MPN) have been shown to influence the emergence and selection of leukemic clones and disease progression. However, it remains unclear if there are specific functional characteristics or genetic signals of MSC that interact with HSPC at different stages of disease progression. We examined BM samples from MDS/MPN patients with high rate of leukemic transformation in less than 2 years (HR) versus those with stable disease for 5 years (SD), to investigate the phenotypic differentiation, functional characteristics, and transcriptomic profiles of MSC and the HSPC counterparts. Methods We obtained BM samples at diagnosis and follow-up from MDS/MPN patients including chronic myelomonocytic leukemia (CMML) treated at Singapore General Hospital. We studied 5 HR and 13 SD patient samples to compare against 5 healthy donor samples. MSC were derived in MesenCult-ACF media (StemCell Technologies), and phenotypic characterization was done after 1-2 passages. HSPC were subjected to co-culture expansion with MSC in MyeloCult-H5100 for 7 days, and long-term culture-initiating cell assays (LTC-IC) using MSC as feeder cells for 6 weeks. Total RNA extracted from MSC and HSPC were subjected to bulk and single-cell RNA sequencing (scRNA-Seq) (10X Genomics platform). Principal component analysis (PCA) was used for the uniform manifold approximation and projection (UMAP). Gene set enrichment analysis (GSEA) was performed on differentially expressed genes to examine canonical signaling pathways. Cellchat algorithm was applied to predict intercellular communication between MSC and HSPC using scRNA-Seq data. Results Baseline clinical variables for the international prognostic scoring system (IPSS) including age at diagnosis, myeloblast percentage and cytogenetics were not significantly different between HR and SD patients. Decreased osteogenic and chondrogenic differentiation, but relatively preserved adipogenic differentiation capacity was observed in all MDS/MPN MSC samples. A significantly lower yield of CD34+ CD38- primitive HSPC with myeloid skewing was observed in the co-culture experiments with MDS/MPN MSC, compared to healthy MSC. Pre-treatment of SD-MSC feeder with the hypomethylating agent 5-azacytidine improved the clonogenic potential of the corresponding co-cultured HSPC, but did not restore the HSPC colony yield with HR-MSC feeder. Unbiased PCA of bulk RNA-Seq data demonstrated greater clustering of HR-MSC, while SD and healthy MSC overlapped significantly. Genes associated with mesenchymal multipotency and differentiation (PODXL20, FOXQ122, SERPINA923) were generally downregulated in MDS/MPN MSC, likely attributed to promoter hypermethylation. Significant differential upregulation of CD74 and IL-6 expression was observed in HR-MSC, which suggested a uniquely enriched inflammatory signature. GSEA results showed that pathways associated with G2M cell cycle checkpoint, DNA repair, glycolysis, and response to azacytidine were suppressed in HR-MSC. UMAP on scRNA-Seq data of longitudinal MSC samples identified distinct clustering of HR-MSC at disease progression. Cell-cell interaction analysis predicted a strong communication between collagen-encoding genes (COL1A1 and COL1A2) in HR-MSC at diagnosis and CD44, a negative regulator of HSPC proliferation, and the interaction was further enhanced at disease progression (Figure 1). Ongoing analysis of downstream gene expression changes and secretome profile of HSPC in MDS/MPN MSC-based in-vivo scaffold models will be presented. Conclusion Together, our data point towards a degree of co-development and communication between MSC and HSPC during disease progression of MDS/MPN. Further characterization of the unique functional and transcriptomic signatures in MSC may help to optimize disease prognostication and identify novel therapeutic targets in MDS/MPN patients.

Single-Cell Transcriptomic Profiling of T Cells from Blinatumomab-Treated Patients with B-Cell Precursor Acute Lymphoblastic Leukemia (BCP-ALL) Reveals Circulating CD8 T Cell Subsets Associated with Treatment Response

Background Blinatumomab (Blina), a bispecific CD3xCD19 T-cell engager (BiTE®), is approved for patients with relapsed/refractory (R/R) and measurable residual disease (MRD) positive B-Cell Precursor Acute Lymphoblastic Leukemia (BCP-ALL). However, a significant percentage of patients do not respond to Blina, and the determinants of resistance are currently unknown. Here, we used Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to identify pre-treatment T cell features associated with response. Methods PBMCs of 13 patients with BCP-ALL who received Blina at the LMU Munich University Hospital from 2016 to 2021 were collected before treatment start. CITE-seq(BD Rhapsody Platform) was performed on a total of 34.907 cells isolated via magnetic-activated cell sorting based on the expression of CD3. An AbSeq panel containing 7 surface markers (CD4, CD8, CCR7, CD45RA, CX3CR1, CD127 and PD1) and a customized T-cell targeted RNA sequencing panel (477 genes) were employed. A Monte-Carlo permutation test was used to compare the proportions of cells in different clusters between Responders (R) and Non-Responders (NR). Significant differences were assumed with a fold change threshold of at least 1.5 (log2FC (LFC) ≥ 0.58) and a false discovery rate (FDR) cutoff of < 0.05. Results We used CITE-seq to characterize T cells in peripheral blood (PB) samples obtained from R/R (n=3) and MRD+ (n=10) BCP-ALL patients. Ten patients (9 MRD+, 1 R/R) responded to the Blina treatment. An integrated strategy based on Reciprocal Principal Component Analysis(RPCA) was implemented and bioinformatic analysis of gene expression mapped in two dimensions via uniform manifold approximation and projection (UMAP) identified 12 CD3 pos distinct clusters. Within CD8 pos T cells of R, we found an increased proportion of CCR7 low Granzyme K(GZMK) high Effector Memory-like cells (GZMK high TEM-like, LFC 0.78, 95CI: 0.66 to 0.92; FDR=0.0012) and a reduced percentage of Naïve cells(CCR7 high LEF1 high CD62L high), when compared to NR (LFC -0.59, 95CI: -0.54 to -0.64; FDR =0.0012, Figure1A). Atrend towards an increased proportion of CCR7 lowCX3CR1 highGZMB high terminal effector-like cells (GZMB high TE-like, LFC 0.57, 95CI: 0.45-0.70; FDR=0.0013) was also noted in CD8 pos T cells of R vs NR. Peripheral CD4 pos T cells of R exhibited a tendency towards an increased proportion of GZMK high cytotoxic T cells (LFC 0.43, 95CI: 0.26 to 0.58; FDR=0.0019) and a reduced percentage of FOXP3 high T regulatory cells (LFC -0.19, 95CI: -0.03 to -0.34; FDR=0.025), compared to NR. Furthermore, we investigated gene expression differences between R and NR cells in each cell cluster. Naïve T cells (CD8 and CD4) of NR showed a significantly increased (LFC>1, FDR<0.05, Figure1B) PRDM1 (Blimp-1) expression, a key promoter of T-cell terminal differentiation that is known to be involved in CD19 CAR-T dysfunction. CD8GZMB high TE-likecells of NR showed upregulated expression of genes involved in chemokine production and cytokine signaling (CCL3, IFNGR1) and downregulated LAIR2, a decoy receptor protecting CD8 T cells from collagen-induced exhaustion. Additionally, TOX, a regulator of T cell exhaustion, was moderately upregulated in CD8GZMB high TE-like cells of NR (LFC 0.41, FDR=0.007). Conclusions Taken together, we were able to identify CD8 T cell subsets that are associated with a response to Blina treatment, as well as gene sets involved in BiTE-mediated T-cell function. Interestingly, the PRDM1 overexpression we found in both CD4 and CD8 naive T cells of Non-Responders has already been linked to T-cell dysfunction in the context of CD19 CAR-T cell therapy. We plan to confirm the results of our analysis in an independent patient cohort. Concomitantly, we will enrich the dataset with results from multi-parameter flow cytometry and in vitro T-cell functional assays conducted using longitudinally collected PB samples from Blina-treated patients. Figure1 A)Log Fold Change(log FC) of the proportion of Responders vs Non Responders CD8 pos cells in each cluster. The dots represent the observed logFC, the horizontal line the bootstrapped 95%Confidence Intervals; PTGDR2= prostaglandin D2 receptor 2; ns= not significant B) Differentially expressed genes in Non Responders vs Responders CD8 pos naïve T cells. FDR=False Discovery Rate; CCL5= Chemokine (C-C motif) ligand 5 ; PRDM1= PR/SET Domain 1;SGK1= serum/glucocorticoid regulated kinase 1.

Developing Deep Learning Pipeline of Whole-Slide Images for Enhanced Diffuse Large B Cell Lymphoma (DLBCL) Subtyping and Outcome Prediction: Leveraging Self-Attention Transformer for Training and Inference

Background: Deep learning algorithms can help to analyze whole-slide images (WSI) in lymphoma pathology, identifying deeper features and patterns that may not be easily discernible to human observers. This pilot project is focused on diffuse large B-cell lymphoma (DLBCL), a heterogeneous disease with diverse genetic alterations. By leveraging self-attention trained clusters and transformers, the project aims to identify patterns and associations between mutational status and overall survival, potentially enhancing personalized treatment strategies and improving data reliability. Methods: This study employed a computer vision pipeline learning system to classify DLBCL subtypes using self-discovery of discriminatory features from scanned WSI. The workflow is shown in Figure 1a. First, we segmented tiles or patches from the gigapixel-sized WSI of 223 lymphoma cancer biopsy slides sourced from The Cancer Genome Atlas (TCGA), DLBCL, and Stanford DLBCL-Morph datasets. to optimize extraction of relevant features. For feature extraction, we utilized self-supervised pretraining with a Vision Transformer (ViT) network on a dataset of 1,515,000 patches. These patches were grouped into morphologically similar clusters using K-means, representing various lymphoma proliferation patterns. Dimensionality reduction with UMAP allowed for computational efficiency and feature visualization. Extracted features were used to predict overall survival using Bag-of-Words (BoWs). We further enhanced the model by incorporating geometric information, including nuclear characteristics from HoverNet. We assessed the model's performance using key metrics such as area under the curve (AUC) and accuracy. Additionally, genomic mutations with a frequency of 10% or higher from the TCGA were incorporated for enhanced predictive capability, including PIM1, SGK1, CARD11, KMT2D, SOCS1, BTG1, MUC16 and FAT4. Correlations and hierarchical clustering were performed on mutations, patches, and outcomes (Figure 1b). Results: Using the self-trained ViT encoder as the backbone and random forest, we demonstrate an accuracy of 0.88 on a weakly supervised task with all samples. The performance is significantly improved when compared to ResNet-50 trained on ImageNet. In addition, saliency maps from multi-attention heads provide excellent interpretations of morphological characteristics, including tumor stroma, cell location and necrosis. With the feature embeddings extracted by ViT, 10 morphologically distinct clusters were identified. The algorithms identified morphologically similar and dissimilar clusters among tiles that represent variation in the distribution of lymphoma cells as seen in clustering results. There was a strong correlation between clusters one and four, the mutated genes, and an increased probability of poor outcomes. When this was visualized by the pathologist, cluster 4 showed a high level of necrosis in the WSI. Finally, using the segmented WSI cortex with clustering distribution, our method achieves an AUC of 0.77 for predicting the vital status of alive/dead as an outcome. Conclusions: Our pipeline effectively leverages WSI to employ machine learning and deep learning tools for disease classification and outcome prediction in DLBCL. By computationally analyzing the entire tumor landscape, it captures tumor heterogeneity and disease risk, establishing correlations between patch-level characteristics, genomic mutations and overall outcomes. The Vision Transformer (ViT) plays a pivotal role in this process, successfully identifying specific features in histopathology tissue by leveraging self-attention mechanisms, which enables precise feature extraction and accurate analysis for DLBCL classification. Notably, the ViT model achieves high performance across various tasks without the need for external labeled data, owing to its self-supervised learning capability that enables independent learning and extraction of meaningful information. Based on this discovery cohort from this dataset, we will update the analysis using a larger, independent external cohort from institutional archives, and the results will be presented at the annual meeting.

Genomic Landscape of Epstein-Barr Virus in Endemic Burkitt Lymphoma

Introduction: Endemic Burkitt lymphoma (eBL) is a fast-growing B-cell malignancy strongly linked to Epstein-Barr virus (EBV) infection, a member of the herpes virus family. The virus is widespread, infecting 95% of the global population, and has also been linked to several other cancers, including nasopharyngeal carcinoma (NPC), diffuse large B-cell lymphoma (DLBCL), Hodgkin lymphoma (HL), natural killer T-cell lymphoma (NKTCL), gastric carcinoma (GC) and leiomyosarcomas. The distribution of these cancers is inconsistent in different regions, raising the hypothesis of whether different strains are linked to specific cancers. Using high throughput sequencing, we obtained EBV whole genome sequences from the plasma of children with eBL diagnosis from a large cohort study in East Africa. Methods: Twenty-one EBV-positive patients (Sixteen BL, one diffuse large B-cell lymphoma, one lymphoblastoid lymphoma, one clear cell sarcoma, one small blue cell tumour and one alveolar rhabdomyosarcoma) were recruited from St Mary's Hospital Lacor, in Uganda, between 2020 and 2023. Venous blood samples were collected in appropriate DNA tubes, and ~ 5 ml of plasma was used for cfDNA extraction using QIAamp circulating Nucleic acid kit (Qiagen, USA). The cfDNA libraries were prepared using the Thruplex Tag- Seq kit (Takara Bio Inc. Japan), target capture and enrichment was performed using an EBV-custom panel (IDT Inc. USA) before sequencing. The Illumina fastq files were processed using an in-house pipeline, and paired-end reads were aligned to a bespoke hybrid EBV reference genome (NC_007605.1 and EBNA genes from NC_009334) using BWA with an alt-ware method. The average sequencing depth and EBV genome coverage were 5164 and 97.8%, respectively. Following the GATK best practice workflows (version 4.0), 15,318 variants were called across all samples using Varscan and Vardict after base and variant recalibration. Functional annotation was performed using the SNPEff package (v5.1d) according to the reference genome (NC_007605.1, NCBI annotation, Nov 2013). For the phylogenetic analysis, 322 public EBV genomes were downloaded from the GenBank and aligned with 21 EBV genomes from the current study using a multiple sequence alignment program (MAFFT v7.52), and the maximum likelihood of the phylogenetic relationship was inferred using RAxML (v8.0), assuming a general time reversible (GTR) model. The inferred phylogeny was subsequently rooted using the evolutionary placement (EPA) algorithm from RAxML using a Macacine herpesvirus 4 genome sequence (NC_006146) as an outer group. The tree was annotated using FigTree software (v1.4.4). The study was approved by the Oxford Tropical Research Ethics Committee (OxTREC Ref: 15-19), National Institute of Medical Research (NIMR/HQ/R.8a/Vol.IX/3408), Uganda National Council of Science and Technology (Ref: HS529ES), and St Mary's Hospital Lacor Institutional Research Ethics Committee. Participants provided written informed consent and assent for minors. Results: We examined the genomes of 21 EBV samples, including 18 type-1 and 3 type-2 strains. Our analysis revealed an average of 729 mutations per sample, comprised of 707 SNVs and 26 indels (<50bp) per sample (Figure 1). Within 16 eBL tumours, we observed the 30-bp deletion in LMP-1 in 43.8% (7/16) cases. This deletion has been previously reported in NPC in Southeast Asia and is associated with high oncogenic potential and weak immunogenicity. We also identified a novel frameshift variant (15-bp deletion) in LMP-1 in 66.7% (14/21) of tumour samples. The LMP-1 gene encodes a 356-amino acid polypeptide with a cytoplasmic amino terminus, a transmembrane domain, and a cytoplasmic carboxy terminus. The C-terminus interacts with cellular proteins, activating the transcriptional nuclear factor-kB (NF-kB) pathway associated with antiapoptotic cell signalling. Additionally, we observed a high degree of polymorphism in EBNA-1, EBNA3s, LMP-2A, BALF1, and BOLF-1 viral genes. Our phylogenetic analysis revealed distinct clustering of African EBV genomes compared to Asian strains and NPC versus eBL (Figure 2). Conclusions: Overall, our study provides the first-ever comprehensive mutational profile of EBV in eBL clinical samples, offering valuable insights into the somatic events of EBV in tumour biology.

Primary Acute Myeloid Leukemia Cells Trigger Distinct Activation Patterns in Expanded NK Cells

Increasing efforts are devoted to implement immunotherapies for the treatment of acute myeloid leukemia (AML). Recently, natural killer (NK) cell-based immunotherapy has shown initial encouraging results. However, which patients are most likely to benefit from NK cell-based treatments remains poorly understood. Here, we investigate the responses of NK cells to ex vivo co-cultures with 55 primary AML samples using single-cell transcriptomics. We identify a subgroup of AML patients eliciting strong responses in NK cells and define the molecular programs in NK cells and other microenvironmental cell types triggered by diverse AML cells. We performed functional single-cell immune profiling on co-cultures of primary expanded NK cells and 55 bone marrow (BM) samples from AML patients at diagnosis. We used NK cells expanded using irradiated K562-41BB-L-mbIL-21 feeder cells, resulting in a product commonly used in clinical trials. All expanded NK cells were from one single healthy donor displaying an HLA-C1C2 allotype.We investigated NK cell-treated and untreated cultures through multiplexed single-cell RNA sequencing (scRNA-seq) to dissect adaptive changes in both NK and AML BM cells upon their interaction. In total, we investigated 121 samples through scRNA-seq: 11 monocultures of expanded NK cells, 55 AML BM cells and 55 co-cultures of AML BM cells and expanded NK cells. scRNA-seq yielded 95,715 high quality cells (791 cells per sample on average), including 31,882 NK cells (483 cells per sample on average) and 63,833 AML BM cells (580 cells per sample on average). Unsupervised clustering of the cells revealed distinct clusters separating expanded NK cells from AML cells (Fig. 1a). We studied the sensitivity of leukemic cells to NK cytotoxicity using high-throughput flow cytometry. We further integrated the results with genetic and clinical data of the AML patients to characterize the mechanisms of action driving the transcriptomic responses and leukemic cell susceptibility to NK cell killing. After 24 h co-culture, scRNA-seq revealed that AML BM cells induced distinct gene expression states in the NK cells. The observed tumor-induced NK states were predominantly enriched in co-cultured samples and not found in NK cell monocultures which mostly consisted of resting-state NK cells. Across the co-cultures, half of the AML samples triggered at least 30% of NK cells to change from resting to tumor-induced states (Fig. 1b). Out of the 28 AML samples causing the strongest NK cell induction, 54% primarily triggered a type I interferon (IFN) signature, characterised by expression of genes encoding MX1, OAS1, and IRF7. In contrast, the other 46% mainly induced an activated phenotype, including expression of TNFRSF18 ( GITR), TNFRSF9 ( 4-1BB), TIGIT and HAVCR2 ( TIM-3). Killer cell immunoglobulin-like receptors-HLA ligand mismatchesare well known causes of NK alloreactivity. Thus, we explored whether HLA-C matched (C1C2, n=24) and mismatched (C1C1 or C2C2, n=28) AML samples influenced NK cell responses. Interestingly, no significant difference in the NK cell induction states was observed between matched and mismatched samples (Mann-Whitney p = 0.57). NK cell treatment also induced responses in the patients' own T cells. Both CD4+ and CD8+ T cells upregulated IFN-ɣ and type I IFN genes upon co-culture. The type I IFN signature in CD8+ T cells was mainly found in AML samples triggering strong NK cell induction. CD8+ T cells also upregulated cytotoxicity genes including FASLG and TNFSF10 (TRAIL), along with inhibitory molecules such as LAG3, suggesting that NK cells can promote effector functions of T cells. Our results suggest that AML BM cells from different patients trigger a heterogeneous response in expanded NK cells. Only in half of the cases, the leukemic BM cells induced substantial activation of NK cells. The observed variation in NK states was not determined by HLA-C allotypes, suggesting that other tumor cell features are responsible for the responses. This underscores the importance of understanding the molecular drivers of tumor and NK cell interaction. Our data additionally show that NK cell-based immunotherapy may be able to promote the functionality of the patient's own T cells, demonstrating how our ex vivo modeling approach can help to refine the mechanism of action of immunotherapies. Our work paves the way for identifying subgroups of AML patients responding to NK cell-based immunotherapies.

MPN Transformation Is Characterized By Heterogeneous Shifts in Lineage Character

Introduction: The Philadelphia chromosome-negative myeloproliferative neoplasms (MPNs) are characterized by the clonal proliferation of differentiated hematologic cells. However, there is a limited understanding of how perturbations at the transcriptional level may contribute to accelerated or blast-phase disease, together referred to here as transformation. Here we characterize transcriptomic characteristics and cell type composition of MPNs along the transition to transformed disease. Methods: RNAseq and clinical data were collected on 261 patients who were part of BeatAML, including those with MPN, de-novo AML, and transformed MPN. This was combined with data from 56 transformed MPN patients seen at MSK, 11 of whom also had paired chronic-phase data available. Differential expression analysis was performed with DESeq2; both GSEA and GSVA were used to evaluate gene-set enrichment and pathways of biological relevance. A custom RNA-seq lineage deconvolution algorithm was used to estimate hematopoietic stage proportions and was used for unsupervised hierarchical clustering of cases. Kaplan-Meier methods were used to estimate survival among transformed MPN lineage subtypes. Results: The initial analysis included 207 de novo AML cases and 75 MPN-BP cases from BeatAML and MSK on which RNAseq and molecular data were integrated across cohorts. Unsupervised analysis was performed using hierarchical clustering applied to batch corrected, variance stabilized RNA-seq count data. Transformed MPN cases were enriched in 2 of 6 clusters (N = 10, 33.3%; N = 36, 54.5%). Notably, the molecular characteristics in these clusters are varied, suggesting that multiple genomic perturbations may lead to similar pathways of transformation. Gene set variation analysis (GSVA) demonstrated a coalescence of transcriptomic perturbation into specific pathways for transformed MPNs, such as hematopoietic stem cell gene sets (q-value = 0.00018). On the contrary, AML cases typically demonstrated upregulation in later stages of hematopoiesis, such as monocytes (q-value =0.00016). In evaluating specific genes differentially expressed between transformed MPNs compared to de novo AML and tAML patients, microRNA MIR29B1 was particularly upregulated in MPN-BP (logFC = 9.52, q < 0.01), while the proto-oncogene CT45A1 had decreased expression in transformed MPN compared to other AMLs (logFC = -7.47, q < 0.01). Paired GSEA of the 11 patients with myelofibrosis MF and transformed disease showed that transformed disease was enriched for CD34-positive HSCs (NES = 2.64, q-value < 0.0001) and E2F transcription factors (NES 2.66, q-value < 0.0001), as well as doxorubicin resistance (NES 2.40, q-value < 0.0001). Given enrichment of the CD34 positive HSC geneset, we performed our RNA-seq based lineage deconvolution on each of the paired samples ( Figure 1). MPNs appeared to transform in a heterogenous fashion reminiscent of the biological diversity in AML. In aggregate, transformation was associated with decreased monocytic (p = 0.002), natural killer (NK) (p = 0.042) character and expansion of common lymphoid progenitor character (p = 0.022). Lineage composition changes varied, with certain samples showing HSC expansion (ID: 54910, ID: 46567) and others exhibiting LMPP expansion (ID: 29485, 2456). Figure 2 defines seven distinct clusters of transformed MPN patients, driven by their lineage components. While these clusters are transcriptionally distinct, we again see that they are not defined by their genomic or cytogenetic alterations. We evaluated for a difference in overall survival (OS) between each cluster and intermediate-risk de novo AML. We found significantly worse OS among patients in three MPN-BP clusters - MPP (p =0.008), LMPP (p = 0.038), and GMP (p < 0.001) - represented as clusters 2, 5, and 7 in Figure 2. Compared to intermediate risk AML, transformed MPNs had significantly worse OS (HR: 1.79 95% CI: 1.18, 2.71, p = 0.0057). Discussion: To our knowledge, this is the first ever description of the transcriptional characteristics of MPN-BP, its comparison to de novo AML, and the first paired comparison of transcriptional and lineage characteristics for patients with chronic-phase MPNs that subsequently transformed. Our study provides valuable insights into the transcriptomic landscape of transformed MPN including both accelerated and blast phase disease, and draws novel contrasts to de novo AML.

Response of rumen fermentation and microbiota to dietary supplementation of sodium selenite and bio-nanostructured selenium in lactating Barki sheep

Dietary selenium (Se) sources affects the structure of the rumen microbial community and rumen fermentation. This study evaluated the effects of sodium selenite (SS) and bio-nanostructured selenium (SeNSM) on rumen fermentation and structure of rumen microbial community of lactating Barki ewes. Twenty one lactating Barki ewes were assigned into three groups based on their body weight and milk yield. The experiment lasted for 50 days, whenever, the control group was fed basal diet; group SS received basal diets plus sodium selenite as inorganic source of Se; and group SeNSM received basal diet plus organic selenium bio-nanostructured. Ruminal pH and volatile Fatty Acids (VFA) was lower (P < 0.05) in SeNSM group compared to control. Principle Coordinate Analysis separated the microbial communities into three clusters based on feeding treatment. The bacterial community was dominated by phylum Bacteroidetes and Firmicutes that were affected (P < 0.05) by Se sources. Specifically Bacteriodetes was higher (P < 0.05) in SS and SeNSM groups; and Firmicutes was higher (P < 0.05) in the control group. Moreover, the predominant bacterial genera were Prevotella, Rikenellaceae RC9 gut group, Unclassified_Bacteroidales, which were higher (P < 0.05) in SeNSM group. The methanogenic community belonged to phylum Euryarchaeota and was significantly decreased (P < 0.05) by Se supplementation. Principal component analysis based on rumen fermentation parameters, and relative abundances of bacteria and methanogens revealed three distinct clusters. These findings suggest that Se supplementation affected the relative abundances of dominant bacterial groups, declined rumen methanogens and SeNSM supplementation showed some positive impacts on some fibrolytic bacteria.

Profiling of vaginal Lactobacillus jensenii isolated from preterm and full-term pregnancies reveals strain-specific factors relating to host interaction.

Each year, 15 million infants are born preterm (<37 weeks gestation), representing the leading cause of mortality for children under the age of five. Whilst there is no single cause, factors such as maternal genetics, environmental interactions, and the vaginal microbiome have been associated with an increased risk of preterm birth. Previous studies show that a vaginal microbiota dominated by Lactobacillus is, in contrast to communities containing a mixture of genera, associated with full-term birth. However, this binary principle does not fully consider more nuanced interactions between bacterial strains and the host. Here, through a combination of analyses involving genome-sequenced isolates and strain-resolved metagenomics, we identify that L. jensenii strains from preterm pregnancies are phylogenetically distinct from strains from full-term pregnancies. Detailed analysis reveals several genetic signatures that distinguish preterm birth strains, including genes predicted to be involved in cell wall synthesis, and lactate and acetate metabolism. Notably, we identify a distinct gene cluster involved in cell surface protein synthesis in our preterm strains, and profiling the prevalence of this gene cluster in publicly available genomes revealed it to be predominantly present in the preterm-associated clade. This study contributes to the ongoing search for molecular biomarkers linked to preterm birth and opens up new avenues for exploring strain-level variations and mechanisms that may contribute to preterm birth.

1791. Use of pathogen genomics to monitor global emergence of extensively drug-resistant Salmonella Serotype Typhi

Abstract Background Whole-genome sequencing (WGS) has transformed surveillance and outbreak detection. However, its use to complement resource-intensive epidemiologic investigations of emerging pathogens is limited. Here using WGS data of S. Typhi, we investigate the global spread of an emerging pathogen. Methods We used NCBI pathogen detection to search for SNP clusters of ceftriaxone-resistant S. Typhi (CRT) reported in the US and the National Antimicrobial Monitoring System (NARMSNOW) to analyze antibiotic-resistant trends of S. Typhi. Results Pathogen Detection Outbreaks of CRT infections were first reported in the US in 2018. We found that the US CRT isolates formed two distinct clusters linked to travel destinations, having close genetic relatedness with those found in Pakistan or Iraq. Specifically, three US CRT isolates (linked to travel to Iraq) only differed from 10 Iraq isolates (2021-2022) by an average of 3 SNPs (Figure). Further, two US CRT isolates (linked to travel to Pakistan) only differed from one Pakistan isolate (2022) by an average of 5 SNPs. NARMS Now Using 2012-2022 NARMS Now data, we analyzed trends in decreased susceptibility to ciprofloxacin (DSC) and resistance to ≥3 antibiotics recommended for treatment (MDR) among S. Typhi. We also compared the resistance trend pre-pandemic and after 2020. Our analysis included a total of 3,633 S. Typhi isolates. The percentage of DSC isolates increased from 68.5% (2012) to 85.1% (2022), while the percentage of MDR isolates increased from 8.9% to 20.1%. The 3-year (2017-2019) pre-pandemic DSC average was 77.5% and 80.5% after (2020-2022). For MDR isolates, it was 12.4% and 21.8%. Close genetic relatedness between three US outbreak associated ceftriaxone-resistant Typhi strains (linked to travel to Iraq; highlighted in red) and 10 strains isolated in Iraq (highlighted in red). Note the strains are collected in different years. The single nucleotide polymorphism (SNP) tree is automatically generated in the NCBI Pathogen Detection database when sequencing data are submitted to the database. The average SNP distance for the red highlighted isolates in the tree is three (SNP distance range 0∼9). Conclusion The SNP clusters indicate the likely international spread of resistant S. Typhi strains in travelers. Resistance to ciprofloxacin is increasing as is resistance to multiple antimicrobials. Travel restrictions imposed during the pandemic did not appreciatively alter resistance trends. These findings indicate the need for global cooperation in addressing antimicrobial resistance and emerging pathogens. Disclosures All Authors: No reported disclosures

Dynamic Changes in the Thylakoid Proteome of Cyanobacteria during Light-Regulated Thylakoid Membrane Development

Cyanobacteria were among the oldest organisms to undertake oxygenic photosynthesis and have an essential impact on the atmosphere and carbon/nitrogen cycles on the planet. The thylakoid membrane of cyanobacteria represents an intricate compartment that houses a variety of multi-component (pigment–)protein complexes, assembly factors, and regulators, as well as transporters involved in photosynthetic light reactions, and respiratory electron transport. How these protein components are incorporated into membranes during thylakoid formation and how individual complexes are regulated to construct the functional machinery remains elusive. Here, we carried out an in-depth statistical analysis of the thylakoid proteome data obtained during light-induced thylakoid membrane biogenesis in the model cyanobacterium Synechococcus elongatus PCC 7942. A total of 1581 proteins were experimentally quantified, among which 457 proteins demonstrated statistically significant variations in abundance at distinct thylakoid biogenesis stages. Gene Ontology and KEGG enrichment analysis revealed that predominantly photosystems, light-harvesting antennae, ABC transporters, and pathway enzymes involved in oxidative stress responses and protein folding exhibited notable alternations in abundance between high light and growth light. Moreover, through cluster analysis the 1581 proteins were categorized into six distinct clusters that have significantly different trajectories of the change in their abundance during thylakoid development. Our study provides insights into the physiological regulation for the membrane integration of protein components and functionally linked complexes during the cyanobacterial TM biogenesis process. The findings and analytical methodologies developed in this study may be valuable for studying the global responses of TM biogenesis and photosynthetic acclimation in plants and algae.

Application of multiple spatial interpolation approaches to annual rainfall data in the Wadi Cheliff basin (north Algeria)

This study addresses a challenging problem of predicting mean annual precipitation across arid and semi-arid areas in northern Algeria, utilizing deterministic, geostatistical (GS), and machine learning (ML) models. Through the analysis of data spanning nearly five decades and encompassing 150 monitoring stations, the result of Random Forest showed the highest training performance, with R square value (of 0.9524) and the Root Mean Square Error (of 24.98). Elevation emerges as a critical factor, enhancing prediction accuracy in mountainous and complex terrains when used as an auxiliary variable. Cluster analysis further refines our understanding of station distribution and precipitation characteristics, identifying four distinct clusters, each exhibiting unique precipitation patterns and elevation zones. This study helps for a better understanding of precipitation prediction, encouraging the integration of additional variables and the exploration of climate change impacts, thereby contributing to informed environmental management and adaptation strategies across diverse climatic and terrain scenarios.

Single-nucleus sequencing of silkworm larval midgut reveals the immune escape strategy of BmNPV in the midgut during the late stage of infection

The midgut is an important barrier against microorganism invasion and proliferation, yet is the first tissue encountered when a baculovirus naturally invades the host. However, only limited knowledge is available how different midgut cell types contribute to the immune response and the clearance or promotion of viral infection. Here, single-nucleus RNA sequencing (snRNA seq) was employed to analyze the responses of various cell subpopulations in the silkworm larval midgut to B. mori nucleopolyhedrovirus (BmNPV) infection. We identified 22 distinct clusters representing enteroendocrine cells (EEs), enterocytes (ECs), intestinal stem cells (ISCs), Goblet cell-like and muscle cell types in the BmNPV-infected and uninfected silkworm larvae midgut at 72 h post infection. Further, our results revealed that the strategies for immune escape of BmNPV in the midgut at the late stage of infection include (1) inhibiting the response of antiviral pathways; (2) inhibiting the expression of antiviral host factors; (3) stimulating expression levels of genes promoting BmNPV replication. These findings suggest that the midgut, as the first line of defense against the invasion of the baculovirus, has dual characteristics of "resistance" and "tolerance". Our single-cell dataset reveals the diversity of silkworm larval midgut cells, and the transcriptome analysis provides insights into the interaction between host and virus infection at the single-cell level.

Genomic Analysis of Rwandan G9P[8] Rotavirus Strains Pre- and Post-RotaTeq® Vaccine Reveals Significant Distinct Sub-Clustering in a Post-Vaccination Cohort.

Although the introduction of rotavirus vaccines has substantially contributed to the reduction in rotavirus morbidity and mortality, concerns persist about the re-emergence of variant strains that might alter vaccine effectiveness in the long term. The G9 strains re-emerged in Africa during the mid-1990s and have more recently become predominant in some countries, such as Ghana and Zambia. In Rwanda, during the 2011 to 2015 routine surveillance period, G9P[8] persisted during both the pre- and post-vaccine periods. The pre-vaccination cohort was based on the surveillance period of 2011 to 2012, and the post-vaccination cohort was based on the period of 2013 to 2015, excluding 2014. The RotaTeq® vaccine that was first introduced in Rwanda in 2012 is genotypically heterologous to Viral Protein 7 (VP7) G9. This study elucidated the whole genome of Rwandan G9P[8] rotavirus strains pre- and post-RotaTeq® vaccine introduction. Fecal samples from Rwandan children under the age of five years (pre-vaccine n = 23; post-vaccine n = 7), conventionally genotyped and identified as G9P[8], were included. Whole-genome sequencing was then performed using the Illumina® MiSeq platform. Phylogenetic analysis and pair-wise sequence analysis were performed using MEGA6 software. Distinct clustering of three post-vaccination study strains was observed in all 11 gene segments, compared to the other Rwandan G9P[8] study strains. Specific amino acid differences were identified across the gene segments of these three 2015 post-vaccine strains. Important amino acid differences were identified at position N242S in the VP7 genome segment of the three post-vaccine G9 strains compared to the other G9 strains. This substitution occurs at a neutralization epitope site and may slightly affect protein interaction at that position. These findings indicate that the Rwandan G9P[8] strains revealed a distinct sub-clustering pattern among post-vaccination study strains circulating in Rwanda, with changes at neutralization epitopes, which may play a role in neutralization escape from vaccine candidates. This emphasizes the need for continuous whole-genome surveillance to better understand the evolution and epidemiology of the G9P[8] strains post-vaccination.

SNP heterozygosity, relatedness and inbreeding of whole genomes from the isolated population of the Faroe Islands

BackgroundThe population of the Faroe Islands is an isolated population but very little is known about it from whole genome sequencing. The population of about 50000 people has a high incidence of rare diseases e.g., 1:300 for Primary Carnitine Deficiency. A screening programme was implemented, and eleven persons were also whole genome sequenced at x37 coverage for diagnostic purposes of those cases that were not affected by the known mutations. The purpose of our study is to utilize the high coverage data to explore the genomic variation and the ancestral history of the population. We study the SNP heterozygosity, the pairwise relatedness from kinship, the inbreeding from runs of homozygosity ROH, and we find the minor allele frequency distribution. We estimate the population ancestry and the timing of the founding event by using the whole genomes from eight consenting individuals.ResultsWe find the number of SNPs and the heterozygosity for the eight individual samples, and for merged samples, for which we also study the relatedness. We find close relatedness between the supposedly unrelated individuals. From ROH, we interpret the high relatedness as an ancient property of the isolated population. A bottleneck event is estimated starting between years sim 50-300 with a maximum consanguineous population in year sim 600 and similarly consanguineous between years 500-700. The ancestry analysis shows the population descends from founders of >99% European and <1% Admixed American ancestry. A distinct clustering near the central European and British populations of the 1000 Genome Project is likely the result of the population isolation and genetic drift. The minor allele frequency distribution suggests many rare variants.ConclusionsThe ancestry is mainly European while the inbreeding is higher compared to European populations and population isolates. The Faroese population has inbreeding more like ancient Europeans. We discovered a bottlenecked and consanguineous population event and estimated it starting in the 1st-4th century as compared to the oldest archaeological findings from the 4th-6th century.

PATZ1-Rearranged Tumors of the Central Nervous System: Characterization of a Pediatric Series of Seven Cases

PATZ1-rearranged sarcomas are well-recognized tumors as part of the family of round cell sarcoma with EWSR1-non-ETS fusions. Whether PATZ1-rearranged central nervous system (CNS) tumors are a distinct tumor type is debatable. We thoroughly characterized a pediatric series of PATZ1-rearranged CNS tumors by chromosome microarray analysis (CMA), DNA methylation analysis, gene expression profiling and, when frozen tissue is available, optical genome mapping (OGM). The series consisted of 7 cases (M:F=1.3:1, 1-17 years, median 12). On MRI, the tumors were supratentorial in close relation to the lateral ventricles (intraventricular or iuxtaventricular), preferentially located in the occipital lobe. Two major histologic groups were identified: one (4 cases) with an overall glial appearance, indicated as “neuroepithelial” (NET) by analogy with the corresponding methylation class (MC); the other (3 cases) with a predominant spindle cell sarcoma morphology, indicated as “sarcomatous” (SM). A single distinct methylation cluster encompassing both groups was identified by multidimensional scaling analysis. Despite the epigenetic homogeneity, unsupervised clustering analysis of gene expression profiles revealed 2 distinct transcriptional subgroups correlating with the histologic phenotypes. Interestingly, genes implicated in epithelial–mesenchymal transition and extracellular matrix composition were enriched in the subgroup associated to the SM phenotype. The combined use of CMA and OGM enabled the identification of chromosome 22 chromothripsis in all cases suitable for the analyses, explaining the physical association of PATZ1 to EWSR1 or MN1. Six patients are currently disease-free (median follow-up 30 months, range 12-92). One patient of the SM group developed spinal metastases at 26 months from diagnosis and is currently receiving multimodal therapy (42 months). Our data suggest that PATZ1-CNS tumors are defined by chromosome 22 chromothripsis as causative of PATZ1 fusion, show peculiar MRI features (eg, relation to lateral ventricles, supratentorial frequently posterior site), and, although epigenetically homogenous, encompass 2 distinct histologic and transcriptional subgroups.