Knockout of the Amino Acid Transporter SLC6A19 and Autoimmune Diabetes Incidence in Female Non-Obese Diabetic (NOD) Mice

Waters, Matthew F.; Delghingaro-Augusto, Viviane; Javed, Kiran; Dahlstrom, Jane E.; Burgio, Gaétan; Bröer, Stefan; Nolan, Christopher

doi:10.3390/metabo11100665

Cited by 7 publications

(5 citation statements)

References 25 publications

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“…S100A12 [324], CDH1 [325], S100A9 [326], ANK1 [327], KCNH2 [328], HP (haptoglobin) [329], FRMD4A [330], SNCA (synuclein alpha) [331], FBXO7 [332], PINK1 [333], B2M [334], KCNH3 [335], CA2 [336], FUZ (fuzzy planar cell polarity protein) [337], UBB (ubiquitin B) [338], C5AR1 [339], CBS (cystathionine beta-synthase) [340], F12 [341], TSPAN5 [342], NQO2 [343], NDUFA1 [344], SRXN1 [345], BASP1 [346], GPX1 [347], OLIG2 [348], EFHC2 [349], FOXA1 [350], PAX4 [351], BGN (biglycan) [352], IL33 [353], LRIG3 [354], GJA1 [355], SHANK3 [356], ARC (activity regulated cytoskeleton associated protein) [357], GFAP (glial fibrillary acidic protein) [358], UNC13A [359], MSTN (myostatin) [360], HSPG2 [361], TERT (telomerase reverse transcriptase) [362], GNB3 [363], L1CAM [364], CALB1 [365], RYR2 [366], MYO15A [367], MYH11 [368], GDF15 [369], CAV1 [370], KIRREL3 [371], COBL (cordon-bleu WH2 repeat protein) [372], LOX (lysyl oxidase) [373], SPARCL1 [374], FLNC (filamin C) [375], TMPRSS4 [376], VWA2 [377], OGDHL (oxoglutarate dehydrogenase L) [378], CYP3A5 [379] and FBXO40 [380] were revealed to be associated with cognitive dysfunction. S100A12 [381], SLC6A19 [382], TXN (thioredoxin) [383], TLR9 [384], S100P [385], S100A9 [386], ANK1 [387], CA1 [388], HP (haptoglobin) [389], ARG1 [390], SNCA (synuclein alpha) [391], STARD10 [392], PINK1 [393], TFR2 [394], B2M [395...…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Biological Pathways in Bipolar Disorder by Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad¹,

Vastrad²

2022

Preprint

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Bipolar disorder (BD), also known as psychiatric disorder, affects millions of people all over the world. The aim of this investigation was to screen and verify hub genes involved in BD as well as to explore potential molecular mechanisms. The next generation sequencing (NGS) dataset GSE124326 was downloaded from the Gene Expression Omnibus (GEO) database, which contained 480 samples, including 240 BD and 240 normal controls. Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. A protein–protein interaction (PPI) network and module analysis were used to identify hub genes. The miRNA-hub gene regulatory network and TF-hub gene regulatory network were also constructed. Receiver operating characteristic curve (ROC) analysis was used to validate hub genes. In this work, 957 DEGs, including 477 up regulated and 480 down regulated, were obtained from NGS data. The GO and pathway enrichment analyses of the DEGs showed that the up regulated genes were enriched in the neutrophil degranulation, immune system, transport, cytoplasm and enzyme regulator activity, and the down regulated genes were enriched in extracellular matrix organization, diseases of metabolism, multicellular organismal process, cell periphery and metal ion binding. Finally, through analyzing the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, we screened hub genes UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11 by the Cytoscape software. The current investigation illustrates a characteristic NGS data in BD, which might contribute to the interpretation of the progression of BD and provide novel biomarkers and therapeutic targets for BD.

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Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Biological Pathways in Bipolar Disorder by Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad¹,

Vastrad²

2022

Preprint

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“…These anemia responsible genes might be candidate biomarkers or therapeutic targets for CD. CXCR1 [679], CXCL1 [680], C2 [681], MMP3 [682], TAP1 [683], NOS2 [684], CXCL10 [685], SLC11A1 [686], GDF15 [687], IL1RN [688], PYGL (glycogen phosphorylase L) [689], CYP27B1 [690], SOCS3 [691], CD55 [692], CXCR2 [693], CCL3 [694], CCL2 [695], SOD2 [696], CD14 [697], IGFBP2 [698], PCSK9 [699], [700], IDO1 [701], FOXP3 [702], CD163 [703], LPL (lipoprotein lipase) [704], TLR2 [705], CCR2 [706], VWF (von Willebrand factor) [707], NOD2 [708], ATF3 [709], TIMP1 [682], ICAM1 [710], FFAR2 [711], IFNG (interferon gamma) [712], APOE (apolipoprotein E) [713], IL6 [714], CCL4 [715], CA2[716], MMP12 [717], MMP2 [718], CCL1 [719], CD24 [720], PLAU (plasminogen activator, urokinase) [721], CTLA4 [722], MASP1 [723], MPO (myeloperoxidase) [724], HP (haptoglobin) [725], MMP10 [726], CD300E [727], CUBN (cubilin) [728], SLC19A3 [729], APOB (apolipoprotein B) [730], APOC3 [731], HMGCS2 [732], OTC (ornithine transcarbamylase) [733], ACE2 [734], SLC22A4 [735], SLC22A5 [735], DPP4 [736], ACE (angiotensin I converting enzyme) [737], SLC6A19 [738], BTNL2 [739], FCRL3 [451], BCHE (butyrylcholinesterase) [740] and APOM (apolipoprotein M) [741] might be involved in type 1 diabetes mellitus. These type 1 diabetes mellitus responsible genes might be important participant in CD.…”

Section: Discussionmentioning

confidence: 99%

Identification of hub genes and key pathways in pediatric Crohn’s disease using next generation sequencing and bioinformatics analysis

Vastrad¹,

Vastrad²

2022

Preprint

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Pediatric Crohn's Disease (CD) also known as inflammatory bowel diseases, affects millions of people all over the world. The aim of this investigation is to identify the key genes in CD and uncover their potential functions. We downloaded the next generation sequencing (NGS) dataset GSE73374 from the Gene Expression Omnibus (GEO) database. The NGS dataset GSE73374 was used to screen differentially expressed genes (DEGs) between samples from patients with CD and healthy controls. Gene ontology (GO) and REACTOME pathway enrichment analyses were applied for the DEGs. Subsequently, a protein - protein interaction (PPI) network, modules, miRNA- hub gene regulatory network and TF - hub gene regulatory network were constructed to identify hub genes, miRNAs and TFs. Receiver operating characteristic curve (ROC) analysis was applied to validate the hub genes. A total of 957 DEGs were identified, including 478 up regulated genes and 479 down regulated genes. GO and REACTOME results suggested that several Go terms and pathways are involved in response to stimulus, extracellular region, signaling receptor binding, small molecule metabolic process, membrane, transporter activity, immune system and biological oxidations. The top centrality hub genes MDFI, MNDA, FBXO6, TFRC, STAT1, DPP4, MME, SLC39A4, APOA1 and TMEM25 were screened out as the critical genes among the DEGs from the PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network. This investigation identified key genes and signal pathways, which might help us improve our understanding of the molecular mechanisms of CD and identify some novel therapeutic targets for CD.

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“…The SLC6A19 gene can code the amino acid transporter B0AT1, which is crucial for transporting amino acids in the liver, kidney, and intestine [ 17 ]. It is well-known that the glomeruli in the renal cortex function to produce an ultrafiltrate.…”

Section: Discussionmentioning

confidence: 99%

“…Disturbance in renal reabsorption of glycine can result in hyperglycinuria. Moreover, a defect in oxalate transportation may increase the urine oxalate content and trigger the damage to the renal tubular epithelial cells, which in turn could promote the formation of calcium oxalate stones [ 17 ]. The patient in our report did not receive a high-glycine diet or total parenteral nutrition.…”

Section: Discussionmentioning

confidence: 99%

The SLC6A19 gene mutation in a young man with hyperglycinuria and nephrolithiasis: a case report and literature review

et al. 2022

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Background Hyperglycinuria is a rare disorder, with few reported cases, caused by either a defect in glycine metabolism or a disturbance in renal glycine reabsorption. Genetic findings of hyperglycinuria are rare and have not previously been reported in Chinese young men. Case presentation A 24-year-old man presented with a compliant of bilateral lumbago for 1 month. Abdominal computed tomography revealed bilateral kidney stones and right upper ureteral dilatation. The 24-h urine analysis showed high urine oxalate levels of 63 mg/day. Analysis of amino acids in urine revealed that his urinary glycine levels were abnormally high (2.38 µmol/mg creatinine). Whole-exome sequencing detected the SLC6A19 variant c.1278 C > T p. (Cys426). Flexible ureteroscopy with holmium laser lithotripsy was conducted twice to remove his bilateral nephrolithiasis. Postoperative stone biochemical composition analysis revealed that the stones were composed of approximately 70% calcium oxalate monohydrate and 30% calcium oxalate dihydrate. The patient was subsequently diagnosed with hyperglycinuria. Three months after the stone surgery, ultrasonography revealed one nodule under the right thyroid lobe during a health checkup. His serum parathyroid hormone (PTH) levels increased to 392.3 pg/mL. Resection of the right parathyroid nodule was performed, and the histopathological examination confirmed right parathyroid adenoma. During the 2-year follow-up period, nephrolithiasis did not relapse, and serum PTH, calcium, and phosphorus levels were normal. Conclusion The SLC6A19 gene may have been significant in the development of hyperglycinuria in a Chinese young man. Further evaluation for the possibility of a glycine excretion disorder could be considered when encountering nephrolithiasis.

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Knockout of the Amino Acid Transporter SLC6A19 and Autoimmune Diabetes Incidence in Female Non-Obese Diabetic (NOD) Mice

Cited by 7 publications

References 25 publications

Identification of Key Genes and Biological Pathways in Bipolar Disorder by Bioinformatics and Next Generation Sequencing Data Analysis

Identification of Key Genes and Biological Pathways in Bipolar Disorder by Bioinformatics and Next Generation Sequencing Data Analysis

Identification of hub genes and key pathways in pediatric Crohn’s disease using next generation sequencing and bioinformatics analysis

The SLC6A19 gene mutation in a young man with hyperglycinuria and nephrolithiasis: a case report and literature review

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