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      ANGPTL3 deficiency impairs lipoprotein production and produces adaptive changes in hepatic lipid metabolism

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          Abstract

          Angiopoietin-like protein 3 (ANGPTL3) is a hepatically secreted protein and therapeutic target for reducing plasma triglyceride-rich lipoproteins and low-density lipoprotein (LDL) cholesterol. Although ANGPTL3 modulates the metabolism of circulating lipoproteins, its role in triglyceride-rich lipoprotein assembly and secretion remains unknown. CRISPR-associated protein 9 (CRISPR/Cas9) was used to target ANGPTL3 in HepG2 cells ( ANGPTL3 −/− ) whereupon we observed ∼50% reduction of apolipoprotein B100 (ApoB100) secretion, accompanied by an increase in ApoB100 early presecretory degradation via a predominantly lysosomal mechanism. Despite defective particle secretion in ANGPTL3 −/− cells, targeted lipidomic analysis did not reveal neutral lipid accumulation in ANGPTL3 −/− cells; rather ANGPTL3 −/− cells demonstrated decreased secretion of newly synthesized triglycerides and increased fatty acid oxidation. Furthermore, RNA sequencing demonstrated significantly altered expression of key lipid metabolism genes, including targets of peroxisome proliferator-activated receptor α, consistent with decreased lipid anabolism and increased lipid catabolism. In contrast, CRISPR/Cas9 LDL receptor (LDLR) deletion in ANGPTL3 −/− cells did not result in a secretion defect at baseline, but proteasomal inhibition strongly induced compensatory late presecretory degradation of ApoB100 and impaired its secretion. Additionally, these ANGPTL3 −/− ;LDLR −/− cells rescued the deficient LDL clearance of LDLR −/− cells. In summary, ANGPTL3 deficiency in the presence of functional LDLR leads to the production of fewer lipoprotein particles due to early presecretory defects in particle assembly that are associated with adaptive changes in intrahepatic lipid metabolism. In contrast, when LDLR is absent, ANGPTL3 deficiency is associated with late presecretory regulation of ApoB100 degradation without impaired secretion. Our findings therefore suggest an unanticipated intrahepatic role for ANGPTL3, whose function varies with LDLR status.

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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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            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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              featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.

              Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.
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                Author and article information

                Contributors
                Journal
                J Lipid Res
                J Lipid Res
                Journal of Lipid Research
                American Society for Biochemistry and Molecular Biology
                0022-2275
                1539-7262
                14 January 2024
                February 2024
                14 January 2024
                : 65
                : 2
                : 100500
                Affiliations
                [1 ]Division of Cardiology, Department of Medicine, Center for Cardiovascular Research, Washington University School of Medicine, Saint Louis, MO, USA
                [2 ]Division of Gastroenterology, Department of Medicine, Washington University School of Medicine, Saint Louis, MO, USA
                [3 ]Division of Cardiology, Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
                [4 ]Division of Nutritional Science and Obesity Medicine, Department of Medicine, Center for Human Nutrition, Washington University School of Medicine, Saint Louis, MO, USA
                [5 ]Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA
                [6 ]Department of Genetics, Washington University School of Medicine, Saint Louis, MO, USA
                Author notes
                []For correspondence: Nathan O. Stitziel; Nicholas O. Davidson nod@ 123456wustl.edu nstitziel@ 123456wustl.edu
                Article
                S0022-2275(24)00005-1 100500
                10.1016/j.jlr.2024.100500
                10875267
                38219820
                e689506a-5be0-4e5c-a517-4b49f9343e83
                © 2024 The Authors

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

                History
                : 22 May 2023
                : 22 December 2023
                Categories
                Research Article

                Biochemistry
                apolipoproteins,dyslipidemias,ldl/metabolism,lipase/endothelial,ppars,triglycerides,vldl
                Biochemistry
                apolipoproteins, dyslipidemias, ldl/metabolism, lipase/endothelial, ppars, triglycerides, vldl

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