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Genome-wide association study identifies human genetic variants associated with fatal outcome from Lassa fever.
Get PDFInfection with Lassa virus (LASV) can cause Lassa fever, a haemorrhagic illness with an estimated fatality rate of 29.7%, but causes no or mild symptoms in many individuals. Here, to investigate whether human genetic variation underlies the heterogeneity of LASV infection, we carried out genome-wide association studies (GWAS) as well as seroprevalence surveys, human leukocyte antigen typing and high-throughput variant functional characterization assays. We analysed Lassa fever susceptibility and fatal outcomes in 533 cases of Lassa fever and 1,986 population controls recruited over a 7 year period in Nigeria and Sierra Leone. We detected genome-wide significant variant associations with Lassa fever fatal outcomes near GRM7 and LIF in the Nigerian cohort. We also show that a haplotype bearing signatures of positive selection and overlapping LARGE1, a required LASV entry factor, is associated with decreased risk of Lassa fever in the Nigerian cohort but not in the Sierra Leone cohort. Overall, we identified variants and genes that may impact the risk of severe Lassa fever, demonstrating how GWAS can provide insight into viral pathogenesis
Dominant NARS1 mutations causing axonal Charcot-Marie-Tooth disease expand NARS1-associated diseases
Get PDFPathogenic variants in six aminoacyl-tRNA synthetase (ARS) genes are implicated in neurological disorders, most notably inherited per- ipheral neuropathies. ARSs are enzymes that charge tRNA molecules with cognate amino acids. Pathogenic variants in asparaginyl- tRNA synthetase (NARS1) cause a neurological phenotype combining developmental delay, ataxia and demyelinating peripheral neur- opathy. NARS1 has not yet been linked to axonal Charcot–Marie–Tooth disease. Exome sequencing of patients with inherited periph- eral neuropathies revealed three previously unreported heterozygous NARS1 variants in three families. Clinical and electrophysiological details were assessed. We further characterized all three variants in a yeast complementation model and used a knock-in mouse model to study variant p.Ser461Phe. All three variants (p.Met236del, p.Cys342Tyr and p.Ser461Phe) co-segregate with the sensorimotor axonal neuropathy phenotype. Yeast complementation assays show that none of the three NARS1 variants support wild-type yeast growth when tested in isolation (i.e. in the absence of a wild-type copy of NARS1), consistent with a loss-of-function effect. Similarly, the homo- zygous knock-in mouse model (p.Ser461Phe/Ser472Phe in mouse) also demonstrated loss-of-function characteristics. We present three previously unreported NARS1 variants segregating with a sensorimotor neuropathy phenotype in three families. Functional studies in yeast and mouse support variant pathogenicity. Thus, NARS1 is the seventh ARS implicated in dominant axonal Charcot–Marie–Tooth disease, further stressing that all dimeric ARSs should be evaluated for Charcot–Marie–Tooth disease
Biallelic variants in CSMD1 are implicated in a neurodevelopmental disorder with intellectual disability and variable cortical malformations.
Get PDFCSMD1 (Cub and Sushi Multiple Domains 1) is a well-recognized regulator of the complement cascade, an important component of the innate immune response. CSMD1 is highly expressed in the central nervous system (CNS) where emergent functions of the complement pathway modulate neural development and synaptic activity. While a genetic risk factor for neuropsychiatric disorders, the role of CSMD1 in neurodevelopmental disorders is unclear. Through international variant sharing, we identified inherited biallelic CSMD1 variants in eight individuals from six families of diverse ancestry who present with global developmental delay, intellectual disability, microcephaly, and polymicrogyria. We modeled CSMD1 loss-of-function (LOF) pathogenesis in early-stage forebrain organoids differentiated from CSMD1 knockout human embryonic stem cells (hESCs). We show that CSMD1 is necessary for neuroepithelial cytoarchitecture and synchronous differentiation. In summary, we identified a critical role for CSMD1 in brain development and biallelic CSMD1 variants as the molecular basis of a previously undefined neurodevelopmental disorder
RNA expression of 6 genes from metastatic mucosal gastric cancer serves as the global prognostic marker for gastric cancer with functional validation.
Get PDFBACKGROUND: Molecular analysis of advanced tumors can increase tumor heterogeneity and selection bias. We developed a robust prognostic signature for gastric cancer by comparing RNA expression between very rare early gastric cancers invading only mucosal layer (mEGCs) with lymph node metastasis (Npos) and those without metastasis (Nneg).
METHODS: Out of 1003 mEGCs, all Npos were matched to Nneg using propensity scores. Machine learning approach comparing Npos and Nneg was used to develop prognostic signature. The function and robustness of prognostic signature was validated using cell lines and external datasets.
RESULTS: Extensive machine learning with cross-validation identified the prognostic classifier consisting of four overexpressed genes (HDAC5, NPM1, DTX3, and PPP3R1) and two downregulated genes (MED12 and TP53), and enabled us to develop the risk score predicting poor prognosis. Cell lines engineered to high-risk score showed increased invasion, migration, and resistance to 5-FU and Oxaliplatin but maintained sensitivity to an HDAC inhibitor. Mouse models after tail vein injection of cell lines with high-risk score revealed increased metastasis. In three external cohorts, our risk score was identified as the independent prognostic factor for overall and recurrence-free survival.
CONCLUSION: The risk score from the 6-gene classifier can successfully predict the prognosis of gastric cancer
Nuclear factor kappa B-dependent persistence of
Get PDFSalmonella serovars Typhi and Paratyphi cause a prolonged illness known as enteric fever, whereas other serovars cause acute gastroenteritis. Mechanisms responsi ble for the divergent clinical manifestations of nontyphoidal and enteric fever Salmonella infections have remained elusive. Here, we show that S. Typhi and S. Paratyphi A can persist within human macrophages, whereas S. Typhimurium rapidly induces apoptotic macrophage cell death that is dependent on Salmonella pathogenicity island 2 (SPI2). S. Typhi and S. Paratyphi A lack 12 specific SPI2 effectors with pro-apoptotic functions, including nine that target nuclear factor κB (NF-κB). Pharmacologic inhibition of NF-κB or heterologous expression of the SPI2 effectors GogA or GtgA restores apoptosis of S. Typhi-infected macrophages. In addition, the absence of the SPI2 effector SarA results in deficient signal transducer and activator of transcription 1 (STAT1) activation and interleukin 12 production, leading to impaired TH1 responses in macrophages and humanized mice. The absence of specific nontyphoidal SPI2 effectors may allow S. Typhi and S. Paratyphi A to cause chronic infections
GPATCH8 modulates mutant SF3B1 mis-splicing and pathogenicity in hematologic malignancies.
Get PDFMutations in the RNA splicing factor gene SF3B1 are common across hematologic and solid cancers and result in widespread alterations in splicing, yet there is currently no therapeutic means to correct this mis-splicing. Here, we utilize synthetic introns uniquely responsive to mutant SF3B1 to identify trans factors required for aberrant mutant SF3B1 splicing activity. This revealed the G-patch domain-containing protein GPATCH8 as required for mutant SF3B1-induced splicing alterations and impaired hematopoiesis. GPATCH8 is involved in quality control of branchpoint selection, interacts with the RNA helicase DHX15, and functionally opposes SURP and G-patch domain containing 1 (SUGP1), a G-patch protein recently implicated in SF3B1-mutant diseases. Silencing of GPATCH8 corrected one-third of mutant SF3B1-dependent splicing defects and was sufficient to improve dysfunctional hematopoiesis in SF3B1-mutant mice and primary human progenitors. These data identify GPATCH8 as a novel splicing factor required for mis-splicing by mutant SF3B1 and highlight the therapeutic impact of correcting aberrant splicing in SF3B1-mutant cancers
A Pan-Cancer Patient-Derived Xenograft Histology Image Repository with Genomic and Pathologic Annotations Enables Deep Learning Analysis
Get PDFPatient-derived xenografts (PDX) model human intra- and intertumoral heterogeneity in the context of the intact tissue of immunocompromised mice. Histologic imaging via hematoxylin and eosin (H&E) staining is routinely performed on PDX samples, which could be harnessed for computational analysis. Prior studies of large clinical H&E image repositories have shown that deep learning analysis can identify intercellular and morphologic signals correlated with disease phenotype and therapeutic response. In this study, we developed an extensive, pan-cancer repository of \u3e1,000 PDX and paired parental tumor H&E images. These images, curated from the PDX Development and Trial Centers Research Network Consortium, had a range of associated genomic and transcriptomic data, clinical metadata, pathologic assessments of cell composition, and, in several cases, detailed pathologic annotations of neoplastic, stromal, and necrotic regions. The amenability of these images to deep learning was highlighted through three applications: (i) development of a classifier for neoplastic, stromal, and necrotic regions; (ii) development of a predictor of xenograft-transplant lymphoproliferative disorder; and (iii) application of a published predictor of microsatellite instability. Together, this PDX Development and Trial Centers Research Network image repository provides a valuable resource for controlled digital pathology analysis, both for the evaluation of technical issues and for the development of computational image–based methods that make clinical predictions based on PDX treatment studies. Significance: A pan-cancer repository of \u3e1,000 patient-derived xenograft hematoxylin and eosin–stained images will facilitate cancer biology investigations through histopathologic analysis and contributes important model system data that expand existing human histology repositories
Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline.
Get PDFTo standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics
Assessment of Patient-Derived Xenograft Growth and Antitumor Activity: The NCI PDXNet Consensus Recommendations.
Get PDFAlthough patient-derived xenografts (PDX) are commonly used for preclinical modeling in cancer research, a standard approach to in vivo tumor growth analysis and assessment of antitumor activity is lacking, complicating the comparison of different studies and determination of whether a PDX experiment has produced evidence needed to consider a new therapy promising. We present consensus recommendations for assessment of PDX growth and antitumor activity, providing public access to a suite of tools for in vivo growth analyses. We expect that harmonizing PDX study design and analysis and assessing a suite of analytical tools will enhance information exchange and facilitate identification of promising novel therapies and biomarkers for guiding cancer therapy