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      Immune activation during Paenibacillus brain infection in African infants with frequent cytomegalovirus co-infection


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          Inflammation during neonatal brain infections leads to significant secondary sequelae such as hydrocephalus, which often follows neonatal sepsis in the developing world. In 100 African hydrocephalic infants we identified the biological pathways that account for this response. The dominant bacterial pathogen was a Paenibacillus species, with frequent cytomegalovirus co-infection. A proteogenomic strategy was employed to confirm host immune response to Paenibacillus and to define the interplay within the host immune response network. Immune activation emphasized neuroinflammation, oxidative stress reaction, and extracellular matrix organization. The innate immune system response included neutrophil activity, signaling via IL-4, IL-12, IL-13, interferon, and Jak/STAT pathways. Platelet-activating factors and factors involved with microbe recognition such as Class I MHC antigen-presenting complex were also increased. Evidence suggests that dysregulated neuroinflammation propagates inflammatory hydrocephalus, and these pathways are potential targets for adjunctive treatments to reduce the hazards of neuroinflammation and risk of hydrocephalus following neonatal sepsis.

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          • There is a characteristic immune response to Paenibacillus brain infection

          • There is a characteristic immune response to CMV brain infection

          • The matching immune response validates pathogen genomic presence

          • The combined results support molecular infection causality


          Immunology; Proteomics; Transcriptomics

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

            Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.
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              Is Open Access

              xCell: digitally portraying the tissue cellular heterogeneity landscape

              Tissues are complex milieus consisting of numerous cell types. Several recent methods have attempted to enumerate cell subsets from transcriptomes. However, the available methods have used limited sources for training and give only a partial portrayal of the full cellular landscape. Here we present xCell, a novel gene signature-based method, and use it to infer 64 immune and stromal cell types. We harmonized 1822 pure human cell type transcriptomes from various sources and employed a curve fitting approach for linear comparison of cell types and introduced a novel spillover compensation technique for separating them. Using extensive in silico analyses and comparison to cytometry immunophenotyping, we show that xCell outperforms other methods. xCell is available at http://xCell.ucsf.edu/. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1349-1) contains supplementary material, which is available to authorized users.

                Author and article information

                23 March 2021
                23 April 2021
                23 March 2021
                : 24
                : 4
                : 102351
                [1 ]Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, USA
                [2 ]Department of Clinical Neurosciences, University of Calgary, Calgary, AB T2N 1N4, Canada
                [3 ]Division of Newborn Medicine, Boston Children's Hospital, Boston, MA 02115, USA
                [4 ]Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
                [5 ]Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
                [6 ]Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
                [7 ]Institute for Personalized Medicine, Pennsylvania State University, Hershey, PA 17033, USA
                [8 ]Department of Biochemistry and Molecular Biology, Pennsylvania State University, State College, PA 16801, USA
                [9 ]Department of Neurosurgery, Washington University School of Medicine, St. Louis, MO 63110, USA
                [10 ]Center for Neural Engineering, Pennsylvania State University, State College, PA 16801, USA
                [11 ]Department of Medicine, Pennsylvania State University College of Medicine, Hershey, PA 17033, USA
                [12 ]Department of Pediatrics, Pennsylvania State College of Medicine, Hershey, PA 17033, USA
                [13 ]CURE Children's Hospital of Uganda, Mbale, Uganda
                [14 ]Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY 10032, USA
                [15 ]Department of Neurosurgery, Harvard Medical School, Boston, MA 02115, USA
                [16 ]Department of Biostatistics, Product Development, Genentech Inc., South San Francisco, CA 94080, USA
                [17 ]Center for Infectious Disease Dynamics, Departments of Neurosurgery, Engineering Science and Mechanics, and Physics, The Pennsylvania State University, University Park, PA 16802, USA
                Author notes
                []Corresponding author: Steven J. Schiff, W311 Millennium Science Complex, The Pennsylvania State University, University Park, PA 16802, USA steven.j.schiff@ 123456gmail.com

                These authors contributed equally


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                Lead contact

                S2589-0042(21)00319-9 102351
                © 2021 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                : 10 November 2020
                : 24 February 2021
                : 19 March 2021

                immunology, proteomics, transcriptomics


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