BACKGROUND: Acute kidney injury (AKI) occurs frequently in critically ill patients and is associated with adverse outcomes. Cellular mechanisms underlying AKI and kidney cell responses to injury remain incompletely understood. METHODS: We performed single-nuclei transcriptomics, bulk transcriptomics, molecular imaging studies, and conventional histology on kidney tissues from 8 individuals with severe AKI (stage 2 or 3 according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria). Specimens were obtained within 1-2 h after individuals had succumbed to critical illness associated with respiratory infections, with 4 of 8 individuals diagnosed with COVID-19. Control kidney tissues were obtained post-mortem or after nephrectomy from individuals without AKI. RESULTS: High-depth single cell-resolved gene expression data of human kidneys affected by AKI revealed enrichment of novel injury-associated cell states within the major cell types of the tubular epithelium, in particular in proximal tubules, thick ascending limbs, and distal convoluted tubules. Four distinct, hierarchically interconnected injured cell states were distinguishable and characterized by transcriptome patterns associated with oxidative stress, hypoxia, interferon response, and epithelial-to-mesenchymal transition, respectively. Transcriptome differences between individuals with AKI were driven primarily by the cell type-specific abundance of these four injury subtypes rather than by private molecular responses. AKI-associated changes in gene expression between individuals with and without COVID-19 were similar. CONCLUSIONS: The study provides an extensive resource of the cell type-specific transcriptomic responses associated with critical illness-associated AKI in humans, highlighting recurrent disease-associated signatures and inter-individual heterogeneity. Personalized molecular disease assessment in human AKI may foster the development of tailored therapies.

Erratum in Genome Med. 2022 Nov 16;14(1):128.

BACKGROUND: Genomic copy number alterations commonly occur in prostate cancer and are one measure of genomic instability. The clinical implication of copy number change in advanced prostate cancer, which defines a wide spectrum of disease from high-risk localised to metastatic, is unknown. METHODS: We performed copy number profiling on 688 tumour regions from 300 patients, who presented with advanced prostate cancer prior to the start of long-term androgen deprivation therapy (ADT), in the control arm of the prospective randomised STAMPEDE trial. Patients were categorised into metastatic states as follows; high-risk non-metastatic with or without local lymph node involvement, or metastatic low/high volume. We followed up patients for a median of 7 years. Univariable and multivariable Cox survival models were fitted to estimate the association between the burden of copy number alteration as a continuous variable and the hazard of death or disease progression. RESULTS: The burden of copy number alterations positively associated with radiologically evident distant metastases at diagnosis (P=0.00006) and showed a non-linear relationship with clinical outcome on univariable and multivariable analysis, characterised by a sharp increase in the relative risk of progression (P=0.003) and death (P=0.045) for each unit increase, stabilising into more modest increases with higher copy number burdens. This association between copy number burden and outcome was similar in each metastatic state. Copy number loss occurred significantly more frequently than gain at the lowest copy number burden quartile (q=4.1 × 10-6). Loss of segments in chromosome 5q21-22 and gains at 8q21-24, respectively including CHD1 and cMYC occurred more frequently in cases with higher copy number alteration (for either region: Kolmogorov-Smirnov distance, 0.5; adjusted P<0.0001). Copy number alterations showed variability across tumour regions in the same prostate. This variance associated with increased risk of distant metastases (Kruskal-Wallis test P=0.037). CONCLUSIONS: Copy number alteration in advanced prostate cancer associates with increased risk of metastases at diagnosis. Accumulation of a limited number of copy number alterations associates with most of the increased risk of disease progression and death. The increased likelihood of involvement of specific segments in high copy number alteration burden cancers may suggest an order underlying the accumulation of copy number changes. TRIAL REGISTRATION: ClinicalTrials.gov NCT00268476 , registered on December 22, 2005. EudraCT  2004-000193-31 , registered on October 4, 2004.

BACKGROUND: In medical genetics, discovery and characterization of disease trait contributory genes and alleles depends on genetic reasoning, study design, and patient ascertainment; we suggest a segmental haploid genetics approach to enhance gene discovery and molecular diagnostics. METHODS: We constructed a genome-wide map for nonallelic homologous recombination (NAHR)-mediated recurrent genomic deletions and used this map to estimate population frequencies of NAHR deletions based on large-scale population cohorts and region-specific studies. We calculated recessive disease carrier burden using high-quality pathogenic or likely pathogenic variants from ClinVar and gnomAD. We developed a NIRD (NAHR deletion Impact to Recessive Disease) score for recessive disorders by quantifying the contribution of NAHR deletion to the overall allele load that enumerated all pairwise combinations of disease-causing alleles; we used a Punnett square approach based on an assumption of random mating. Literature mining was conducted to identify all reported patients with defects in a gene with a high NIRD score; meta-analysis was performed on these patients to estimate the representation of NAHR deletions in recessive traits from contemporary human genomics studies. Retrospective analyses of extant clinical exome sequencing (cES) were performed for novel rare recessive disease trait gene and allele discovery from individuals with NAHR deletions. RESULTS: We present novel genomic insights regarding the genome-wide impact of NAHR recurrent segmental variants on recessive disease burden; we demonstrate the utility of NAHR recurrent deletions to enhance discovery in the challenging context of autosomal recessive (AR) traits and biallelic variation. Computational results demonstrate new mutations mediated by NAHR, involving recurrent deletions at 30 genomic regions, likely drive recessive disease burden for over 74% of loci within these segmental deletions or at least 2% of loci genome-wide. Meta-analyses on 170 literature-reported patients implicate that NAHR deletions are depleted from the ascertained pool of AR trait alleles. Exome reanalysis of personal genomes from subjects harboring recurrent deletions uncovered new disease-contributing variants in genes including COX10, ERCC6, PRRT2, and OTUD7A. CONCLUSIONS: Our results demonstrate that genomic sequencing of personal genomes with NAHR deletions could dramatically improve allele and gene discovery and enhance clinical molecular diagnosis. Moreover, results suggest NAHR events could potentially enable human haploid genetic screens as an approach to experimental inquiry into disease biology.

Author information: (1)Department of Human Genetics, The University of Chicago, 928 E. 58th St. CLSC 507C, Chicago, IL, 60637, USA. (2)Department of Medicine, Johns Hopkins University, Baltimore, MD, USA. (3)Department of Medicine, University of Colorado Denver, Aurora, CO, USA. (4)Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA. (5)Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. (6)Division of Asthma Research, Cincinnati Children's Hospital, Cincinnati, OH, USA. (7)Department of Medicine, Division of Pulmonary, Allergy, Sleep, and Critical Care Medicine, Boston University School of Medicine, Boston, MA, USA. (8)Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA. (9)University of Texas Southwestern Medical Center, Dallas, TX, USA. (10)Children's Hospital Colorado and University of Colorado School of Medicine, Aurora, CO, USA. (11)Children's National Hospital and George Washington University School of Medicine and Health Sciences, Washington, DC, USA. (12)University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. (13)NIH/NIAID, Bethesda, MD, USA. (14)Rho Federal Systems Division, Inc, Durham, NC, USA. (15)The Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA. (16)Department of Medicine, Vanderbilt University School of Medicine, Nashville, TN, USA. (17)Department of Public Health Sciences, Henry Ford Health Systems, Detroit, MI, USA. (18)Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA. (19)Asthma and Airway Disease Research Center, University of Arizona, Tucson, AZ, USA. (20)Department of Medicine, Division of Clinical Immunology Icahn School of Medicine at Mount Sinai, New York, NY, USA. (21)Department of Medicine, Henry Ford Health Systems, Detroit, MI, USA. (22)Department of Pediatrics, Monroe Carell Jr Children's Hospital at Vanderbilt University Medical Center, Nashville, TN, USA. (23)Department of Pediatrics, Columbia University Medical Center, New York, NY, USA. (24)Pulmonary Center, Boston University School of Medicine, Boston, MA, USA. (25)Department of Pediatrics, Johns Hopkins University, Baltimore, MD, USA. (26)Immunology Division, Benaroya Research Institute Systems, Seattle, WA, USA. (27)Department of Medicine, University of Washington, Seattle, WA, USA. (28)Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA. (29)Department of Human Genetics, The University of Chicago, 928 E. 58th St. CLSC 507C, Chicago, IL, 60637, USA. c-ober@genetics.uchicago.edu.

BACKGROUND: Although anti-apoptotic proteins of the B-cell lymphoma-2 (BCL2) family have been utilized as therapeutic targets in acute myeloid leukaemia (AML), their complicated regulatory networks make individualized therapy difficult. This study aimed to discover the transcriptional signatures of BCL2 family genes that reflect regulatory dynamics, which can guide individualized therapeutic strategies. METHODS: From three AML RNA-seq cohorts (BeatAML, LeuceGene, and TCGA; n = 451, 437, and 179, respectively), we constructed the BCL2 family signatures (BFSigs) by applying an innovative gene-set selection method reflecting biological knowledge followed by non-negative matrix factorization (NMF). To demonstrate the significance of the BFSigs, we conducted modelling to predict response to BCL2 family inhibitors, clustering, and functional enrichment analysis. Cross-platform validity of BFSigs was also confirmed using NanoString technology in a separate cohort of 47 patients. RESULTS: We established BFSigs labeled as the BCL2, MCL1/BCL2, and BFL1/MCL1 signatures that identify key anti-apoptotic proteins. Unsupervised clustering based on BFSig information consistently classified AML patients into three robust subtypes across different AML cohorts, implying the existence of biological entities revealed by the BFSig approach. Interestingly, each subtype has distinct enrichment patterns of major cancer pathways, including MAPK and mTORC1, which propose subtype-specific combination treatment with apoptosis modulating drugs. The BFSig-based classifier also predicted response to venetoclax with remarkable performance (area under the ROC curve, AUROC = 0.874), which was well-validated in an independent cohort (AUROC = 0.950). Lastly, we successfully confirmed the validity of BFSigs using NanoString technology. CONCLUSIONS: This study proposes BFSigs as a biomarker for the effective selection of apoptosis targeting treatments and cancer pathways to co-target in AML.

Author information: (1)Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany. (2)Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Kiel, Germany. (3)Institute of Genetics and Genomics of Geneva (iGE3), University of Geneva, Geneva, Switzerland. (4)Department of Genetics, University of Saarland, Saarbrücken, Germany. (5)Present address: Department of Bioinformatics and Genomics, Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, 08003, Barcelona, Spain. (6)The Molecular Diagnostics and Clinical Research Unit, University Hospital of Southern Denmark, Aabenraa, Denmark. (7)Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark. (8)Section for Biostatistics and Evidence-Based Research, the Parker Institute, Bispebjerg and Frederiksberg Hospital, Copenhagen, Denmark. (9)Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark. (10)Cambridge Institute of Therapeutic Immunology and Infectious Disease, Jeffrey Cheah Biomedical Centre, Cambridge Biomedical Campus, Cambridge, CB 0AW, UK. (11)Department of Medicine, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK. (12)Life &amp; Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany. (13)PRECISE Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases (DZNE), and University of Bonn, Bonn, Germany. (14)Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany. s.schreiber@mucosa.de. (15)Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Kiel, Germany. s.schreiber@mucosa.de. (16)Institute of Clinical Molecular Biology, Christian-Albrechts-University Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany. p.rosenstiel@mucosa.de. (17)Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Kiel, Germany. p.rosenstiel@mucosa.de. (#)Contributed equally

BACKGROUND: Primary central nervous system lymphoma (PCNSL) is a rare lymphoma of the central nervous system, usually of diffuse large B cell phenotype. Stereotactic biopsy followed by histopathology is the diagnostic standard. However, limited material is available from CNS biopsies, thus impeding an in-depth characterization of PCNSL. METHODS: We performed flow cytometry, single-cell RNA sequencing, and B cell receptor sequencing of PCNSL cells released from biopsy material, blood, and cerebrospinal fluid (CSF), and spatial transcriptomics of biopsy samples. RESULTS: PCNSL-released cells were predominantly activated CD19+CD20+CD38+CD27+ B cells. In single-cell RNA sequencing, PCNSL cells were transcriptionally heterogeneous, forming multiple malignant B cell clusters. Hyperexpanded B cell clones were shared between biopsy- and CSF- but not blood-derived cells. T cells in the tumor microenvironment upregulated immune checkpoint molecules, thereby recognizing immune evasion signals from PCNSL cells. Spatial transcriptomics revealed heterogeneous spatial organization of malignant B cell clusters, mirroring their transcriptional heterogeneity across patients, and pronounced expression of T cell exhaustion markers, co-localizing with a highly malignant B cell cluster. CONCLUSIONS: Malignant B cells in PCNSL show transcriptional and spatial intratumor heterogeneity. T cell exhaustion is frequent in the PCNSL microenvironment, co-localizes with malignant cells, and highlights the potential of personalized treatments.

BACKGROUND: Emerging RNA viruses that target the central nervous system (CNS) lead to cognitive sequelae in survivors. Studies in humans and mice infected with West Nile virus (WNV), a re-emerging RNA virus associated with learning and memory deficits, revealed microglial-mediated synapse elimination within the hippocampus. Moreover, CNS-resident memory T (TRM) cells activate microglia, limiting synapse recovery and inducing spatial learning defects in WNV-recovered mice. The signals involved in T cell-microglia interactions are unknown. METHODS: Here, we examined immune cells within the murine WNV-recovered forebrain using single-cell RNA sequencing to identify putative ligand-receptor pairs involved in intercellular communication between T cells and microglia. Clustering and differential gene analyses were followed by protein validation and genetic and antibody-based approaches utilizing an established murine model of WNV recovery in which microglia and complement promote ongoing hippocampal synaptic loss. RESULTS: Profiling of host transcriptome immune cells at 25 days post-infection in mice revealed a shift in forebrain homeostatic microglia to activated subpopulations with transcriptional signatures that have previously been observed in studies of neurodegenerative diseases. Importantly, CXCL16/CXCR6, a chemokine signaling pathway involved in TRM cell biology, was identified as critically regulating CXCR6 expressing CD8+ TRM cell numbers within the WNV-recovered forebrain. We demonstrate that CXCL16 is highly expressed by all myeloid cells, and its unique receptor, CXCR6, is highly expressed on all CD8+ T cells. Using genetic and pharmacological approaches, we demonstrate that CXCL16/CXCR6 not only is required for the maintenance of WNV-specific CD8 TRM cells in the post-infectious CNS, but also contributes to their expression of TRM cell markers. Moreover, CXCR6+CD8+ T cells are required for glial activation and ongoing synapse elimination. CONCLUSIONS: We provide a comprehensive assessment of the role of CXCL16/CXCR6 as an interaction link between microglia and CD8+ T cells that maintains forebrain TRM cells, microglial and astrocyte activation, and ongoing synapse elimination in virally recovered animals. We also show that therapeutic targeting of CXCL16 in mice during recovery may reduce CNS CD8+ TRM cells.

Erratum in Genome Med. 2022 Oct 12;14(1):117.

BACKGROUND: Renal cell carcinoma (RCC) is a heterogeneous disease comprising histologically defined subtypes. For therapy selection, precise subtype identification and individualized prognosis are mandatory, but currently limited. Our aim was to refine subtyping and outcome prediction across main subtypes, assuming that a tumor is composed of molecular features present in distinct pathological subtypes. METHODS: Individual RCC samples were modeled as linear combination of the main subtypes (clear cell (ccRCC), papillary (pRCC), chromophobe (chRCC)) using computational gene expression deconvolution. The new molecular subtyping was compared with histological classification of RCC using the Cancer Genome Atlas (TCGA) cohort (n = 864; ccRCC: 512; pRCC: 287; chRCC: 65) as well as 92 independent histopathologically well-characterized RCC. Predicted continuous subtypes were correlated to cancer-specific survival (CSS) in the TCGA cohort and validated in 242 independent RCC. Association with treatment-related progression-free survival (PFS) was studied in the JAVELIN Renal 101 (n = 726) and IMmotion151 trials (n = 823). CSS and PFS were analyzed using the Kaplan-Meier and Cox regression analysis. RESULTS: One hundred seventy-four signature genes enabled reference-free molecular classification of individual RCC. We unambiguously assign tumors to either ccRCC, pRCC, or chRCC and uncover molecularly heterogeneous tumors (e.g., with ccRCC and pRCC features), which are at risk of worse outcome. Assigned proportions of molecular subtype-features significantly correlated with CSS (ccRCC (P = 4.1E - 10), pRCC (P = 6.5E - 10), chRCC (P = 8.6E - 06)) in TCGA. Translation into a numerical RCC-R(isk) score enabled prognosis in TCGA (P = 9.5E - 11). Survival modeling based on the RCC-R score compared to pathological categories was significantly improved (P = 3.6E - 11). The RCC-R score was validated in univariate (P = 3.2E - 05; HR = 3.02, 95% CI: 1.8-5.08) and multivariate analyses including clinicopathological factors (P = 0.018; HR = 2.14, 95% CI: 1.14-4.04). Heterogeneous PD-L1-positive RCC determined by molecular subtyping showed increased PFS with checkpoint inhibition versus sunitinib in the JAVELIN Renal 101 (P = 3.3E - 04; HR = 0.52, 95% CI: 0.36 - 0.75) and IMmotion151 trials (P = 0.047; HR = 0.69, 95% CI: 0.48 - 1). The prediction of PFS significantly benefits from classification into heterogeneous and unambiguous subtypes in both cohorts (P = 0.013 and P = 0.032). CONCLUSION: Switching from categorical to continuous subtype classification across most frequent RCC subtypes enables outcome prediction and fosters personalized treatment strategies.