cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.7429928
title: Supplementary Material for: Microbial Intervention as a Novel Target in Treatment of Non-Alcoholic Fatty Liver Disease Progression

description: <b><i>Background/Aims:</i></b> Emerging evidence suggests a close link between gut microbiota and non-alcoholic fatty liver disease (NAFLD). In this study, we aimed to investigate the association between gut microbiota and the DNA methylation of adiponectin (an adipocyte-specific adipocytokine) in rats, following diet-induced NAFLD. <b><i>Methods:</i></b> 50 male SD rats were randomly divided into five groups with or without a high fat diet (HFD), antibiotics, and probiotics, in order to establish an imbalanced gut microbiota and probiotic treatment model in NAFLD rats. After 13 weeks of treatment, blood, liver, and cecal tissue samples were collected. Serum lipids, liver function indexes by biochemical analyzers, and changes in liver pathology with hematoxylin-eosin (HE) and masson staining were detected. Furthermore, the serum adiponectin by enzyme-linked immunosorbent assay (ELISA) and liver adiponectin methylation levels in the promoter regions by pyrophosphate sequencing were determined. High throughput Illumina sequencing targeted microbial 16S genes, bioinformatics and statistical analysis identified cecal-associated gut microbiota. <b><i>Results:</i></b> HFD with antibiotic exposure showed the most severe steatohepatitis and a severe gut microbiota alteration. Reduced bacterial diversity was also seen and the abundances of Firmicutes, <i>Lactobacillus</i>, Cyanobacteria, Acidobacteria, Chlamydiae, Chlamydiales, Rubrobacteria, Verrucomicrobia, <i>Blautia, Shewanella, Bacteroides, Bacteroides acidifaciens</i>, and <i>Bacteroides uniformis</i>, were shown to be partly reversed by probiotic treatment. Decreased serum adiponectin levels and increased DNA methylation levels of adiponectin promoter regions were also markedly associated with the NAFLD progression during gut microbiota alteration. <b><i>Conclusion:</i></b> Our results suggested that both gut microbiota alteration and adiponectin variability may be drivers of NAFLD progression and that targeting the gut microbiota, such as via administration of a probiotic, may delay NAFLD progression via adiponectin.
DOI: 10.7284/133291
title: Expendable Probe data collected during research cruise NA109 using a Sippican MK21 instrument system onboard the platform EV Nautilus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7429910
title: Data and program for MSBN

description: MSBN tool for detecting list-colored graph motifs in biological networks created and released by Yiran Huang and Cheng Zhong
DOI: 10.6084/m9.figshare.7429928.v1
title: Supplementary Material for: Microbial Intervention as a Novel Target in Treatment of Non-Alcoholic Fatty Liver Disease Progression

description: <b><i>Background/Aims:</i></b> Emerging evidence suggests a close link between gut microbiota and non-alcoholic fatty liver disease (NAFLD). In this study, we aimed to investigate the association between gut microbiota and the DNA methylation of adiponectin (an adipocyte-specific adipocytokine) in rats, following diet-induced NAFLD. <b><i>Methods:</i></b> 50 male SD rats were randomly divided into five groups with or without a high fat diet (HFD), antibiotics, and probiotics, in order to establish an imbalanced gut microbiota and probiotic treatment model in NAFLD rats. After 13 weeks of treatment, blood, liver, and cecal tissue samples were collected. Serum lipids, liver function indexes by biochemical analyzers, and changes in liver pathology with hematoxylin-eosin (HE) and masson staining were detected. Furthermore, the serum adiponectin by enzyme-linked immunosorbent assay (ELISA) and liver adiponectin methylation levels in the promoter regions by pyrophosphate sequencing were determined. High throughput Illumina sequencing targeted microbial 16S genes, bioinformatics and statistical analysis identified cecal-associated gut microbiota. <b><i>Results:</i></b> HFD with antibiotic exposure showed the most severe steatohepatitis and a severe gut microbiota alteration. Reduced bacterial diversity was also seen and the abundances of Firmicutes, <i>Lactobacillus</i>, Cyanobacteria, Acidobacteria, Chlamydiae, Chlamydiales, Rubrobacteria, Verrucomicrobia, <i>Blautia, Shewanella, Bacteroides, Bacteroides acidifaciens</i>, and <i>Bacteroides uniformis</i>, were shown to be partly reversed by probiotic treatment. Decreased serum adiponectin levels and increased DNA methylation levels of adiponectin promoter regions were also markedly associated with the NAFLD progression during gut microbiota alteration. <b><i>Conclusion:</i></b> Our results suggested that both gut microbiota alteration and adiponectin variability may be drivers of NAFLD progression and that targeting the gut microbiota, such as via administration of a probiotic, may delay NAFLD progression via adiponectin.
DOI: 10.7284/133287
title: INS (Inertial Navigation System) data collected during research cruise NA109 using a Kongsberg Seatex MRU-5 instrument system onboard the platform EV Nautilus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/133292
title: INS (Inertial Navigation System) data collected during research cruise NA109 using a Kongsberg Seapath 330+ instrument system onboard the platform EV Nautilus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7429838
title: Supplementary Material for: Gliquidone Alleviates Diabetic Nephropathy by Inhibiting Notch/Snail Signaling Pathway

description: <b><i>Background/Aims:</i></b><b><i></i></b> Diabetic nephropathy is a common complication of diabetes. This study explored the renal protective effect and possible mechanism of gliquidone in mice with diabetic nephropathy. <b><i>Methods:</i></b> Animal model of diabetic nephropathy was established in KKAy mice. The renal protective effect of gliquidone was studied by evaluating the kidney function through measures of urinary protein, blood urea nitrogen (BUN), serum creatinine (Scr) and serum triglyceride (TG) that were performed using an automatic biochemical analyzer. The levels of oxidative stress indicators, such as nitric oxide (NO), superoxide dismutase (SOD) and malondialdehyde (MDA), were evaluated in renal tissue homogenates using the automatic biochemical analyzer. The inhibitory effect of gliquidone on renal interstitial fibrosis and its association with Notch / Snail1 signaling pathway in diabetic nephropathy was investigated using molecular biological techniques. <b><i>Results:</i></b><b><i></i></b> It was found that low-, medium- and high-dose gliquidone improved the mice’s general health condition, such as mental status, fur condition, eating, and drinking. Gliquidone reduced the body weight and the kidney weight /body weight ratio of mice. Gliquidone improved the kidney function, indicated by reductions in urinary protein, blood urea nitrogen, and serum creatinine and triglyceride. Gliquidone treatment increased levels of nitric oxide and superoxide dismutase, but decreased level of malondialdehyde. The expression of Jagged1/Notch1/hes1/Snail1/α-SMA decreased, while the expression of E-cadherin increased in gliquidone-treated kidneys. High dose gliquidone showed the best effect, one that was similar to that of the positive control drug irbesartan. <b><i>Conclusion:</i></b> Taken together, our results suggested that gliquidone can ameliorate the diabetic symptoms of diabetic nephropathy through inhibiting Notch / Snail1 signaling pathway, improving anti -oxidative response and delaying renal interstitial fibrosis. The efficacy of gliquidone is dose-dependent.
DOI: 10.6084/m9.figshare.7429910.v1
title: Data and program for MSBN

description: MSBN tool for detecting list-colored graph motifs in biological networks created and released by Yiran Huang and Cheng Zhong
DOI: 10.6084/m9.figshare.7429838.v1
title: Supplementary Material for: Gliquidone Alleviates Diabetic Nephropathy by Inhibiting Notch/Snail Signaling Pathway

description: <b><i>Background/Aims:</i></b><b><i></i></b> Diabetic nephropathy is a common complication of diabetes. This study explored the renal protective effect and possible mechanism of gliquidone in mice with diabetic nephropathy. <b><i>Methods:</i></b> Animal model of diabetic nephropathy was established in KKAy mice. The renal protective effect of gliquidone was studied by evaluating the kidney function through measures of urinary protein, blood urea nitrogen (BUN), serum creatinine (Scr) and serum triglyceride (TG) that were performed using an automatic biochemical analyzer. The levels of oxidative stress indicators, such as nitric oxide (NO), superoxide dismutase (SOD) and malondialdehyde (MDA), were evaluated in renal tissue homogenates using the automatic biochemical analyzer. The inhibitory effect of gliquidone on renal interstitial fibrosis and its association with Notch / Snail1 signaling pathway in diabetic nephropathy was investigated using molecular biological techniques. <b><i>Results:</i></b><b><i></i></b> It was found that low-, medium- and high-dose gliquidone improved the mice’s general health condition, such as mental status, fur condition, eating, and drinking. Gliquidone reduced the body weight and the kidney weight /body weight ratio of mice. Gliquidone improved the kidney function, indicated by reductions in urinary protein, blood urea nitrogen, and serum creatinine and triglyceride. Gliquidone treatment increased levels of nitric oxide and superoxide dismutase, but decreased level of malondialdehyde. The expression of Jagged1/Notch1/hes1/Snail1/α-SMA decreased, while the expression of E-cadherin increased in gliquidone-treated kidneys. High dose gliquidone showed the best effect, one that was similar to that of the positive control drug irbesartan. <b><i>Conclusion:</i></b> Taken together, our results suggested that gliquidone can ameliorate the diabetic symptoms of diabetic nephropathy through inhibiting Notch / Snail1 signaling pathway, improving anti -oxidative response and delaying renal interstitial fibrosis. The efficacy of gliquidone is dose-dependent.
DOI: 10.7284/133293
title: Meteorological Station data collected during research cruise NA109 using a Coastal Weatherpak-2000 instrument system onboard the platform EV Nautilus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7429829
title: Supplementary Material for: Clinical Drug Response Prediction by Using a Lq Penalized Network-Constrained Logistic Regression Method

description: <b><i>Background/Aims:</i></b> One of the most important impacts of personalized medicine is the connection between patients’ genotypes and their drug responses. Despite a series of studies exploring this relationship, the predictive ability of such analyses still needs to be strengthened. <b><i>Methods:</i></b> Here we present the L<i>q</i> penalized network-constrained logistic regression (L<i>q</i>-NLR) method to meet this need, in which the predictors are integrated into the gene expression data and biological network knowledge and are combined with a more aggressive penalty function. Response prediction models for two cancer targeting drugs (erlotinib and sorafenib) were developed from gene expression data and IC50 values from a large panel of cancer cell lines by utilizing the proposed approach. Then the drug responders were tested with the baseline tumor gene expression data, yielding an <i>in vivo</i> drug sensitivity prediction. <b><i>Results:</i></b> These results demonstrated the high effectiveness of this approach. One of the best results achieved by our method was a correlation of 0.841 between the cell line <i>in vitro</i> drug response and patient’s <i>in vivo</i> drug response. We then applied these two drug prediction models to develop a personalized medicine approach in which the subsequent treatment depends on each patient’s gene-expression profile. <b><i>Conclusion:</i></b> The proposed method is much better than the existing approach and can capture a more accurate reflection of the relationship between genotypes and phenotypes.
DOI: 10.6084/m9.figshare.7429910.v2
title: Data and program for MSBN

description: MSBN tool for detecting list-colored graph motifs in biological networks created and released by Yiran Huang and Cheng Zhong
DOI: 10.6084/m9.figshare.7429760
title: Whole genome sequencing, Amblyseius swirskii pooled population, ONT MinION -- ONT data I2 run 1 restart 2 no basecalling fast5

description: ReadMe: ONT data I2 runKim Ferguson, kim.ferguson@wur.nl or kfergy@gmail.comWageningen University and Research<br>This ReadMe file is for all files associated with this project, and is present in each data package.<br>There are five .tar file sets in total available on figshare:&gt; 20171130_1637_FAH35464_I2_run 1 part I fast5.tar&gt; 20171130_1637_FAH35464_I2_run 1 part II fast5.tar&gt; 20171201_0934_FAH35464_I2_run 1 restart 1 fast5.tar&gt; 20171201_0954_FAH35464_I2_run 1 restart 2 nobasecalling fast5.tar&gt; ONT data I2 run fastq.tar<br>Read creation dates: 30-11-2017 and 01-12-2017Albacore run date: 04-12-2017<br>Organism: Amblyseius swirskiiPopulation: Koppert biocontrol population, inbred for 10 generationsCollected: &gt;1000 individuals for DNA extraction, males, females, juveniles, placed in 96% EtOHContamination: Collected with food source, pollen from Typha latifolia, and likely was part of DNA extractionExtraction protocol: QIAGEN MagAttract HMW DNA Kit (QIAGEN) according to the manual, while the final DNA product was eluted with nuclease free waterSequenced using ONT MinION sequencing technology<br>MinION model: MinION Mk1BFlowcell: FAH35464 FLO-MIN107 R9.5Ligation kit: Ligation Sequencing Kit version 108 (SQK-LSK108) (no genomic shearing)MinKNOW: version 1.10.16Reads that were basecalled in the cloud: &gt; /20171130_1635_FAH35464_I2_run 1 mux&gt; /20171130_1635_FAH35464_I2_run 1 full&gt; /20171130_1635_FAH35464_I2_run 1 restart 1 mux&gt; /20171130_1635_FAH35464_I2_run 1 restart 1 fullReads that were basecalled using Albacore (version 2.1.3):&gt; /20171201_0952_FAH35464_I2_run 1 restart 2 nobasecalling mux&gt; /20171201_0954_FAH35464_I2_run 1 restart 2 nobasecalling fullAlbacore results:&gt; /basecalled_reads 20171201_0952_FAH35464_I2_run 1 restart 3 nobasecalling mux&gt; /basecalled_reads 20171201_0954_FAH35464_I2_run 1 restart 3 nobasecalling full<br>Data further used in Paspati et al, in prep<br>Fun facts:<br>Files have been separated into fast5 and fastq files, with additional splitting into parts I and II for "/20171130_1635_FAH35464_I2_run 1 full" due to size. Each fast5 folder has been uploaded separateley<br>fast5 folders are often separated into Fail, Pass, and Skip, this is related to the cloud basecalling by the MinKNOW program, based on initial and ongoing quality assessments.<br>fastq folders and files would be considered raw and uncorrected, and were either basecalled in th cloud or using Albacore (see below). All fastq folders have been compressed and uploaded together.<br>There are two restarts in this sequencing run, due to MinKNOW crashing, either after a few hours or overnight. After the second crash, we decided to switch to no basecalling in hope that this would solve the issues.
DOI: 10.7284/133288
title: Multibeam Echosounder data collected during research cruise NA109 using a Kongsberg EM302 instrument system onboard the platform EV Nautilus

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.7429760.v1
title: Whole genome sequencing, Amblyseius swirskii pooled population, ONT MinION -- ONT data I2 run 1 restart 2 no basecalling fast5

description: ReadMe: ONT data I2 runKim Ferguson, kim.ferguson@wur.nl or kfergy@gmail.comWageningen University and Research<br>This ReadMe file is for all files associated with this project, and is present in each data package.<br>There are five .tar file sets in total available on figshare:&gt; 20171130_1637_FAH35464_I2_run 1 part I fast5.tar&gt; 20171130_1637_FAH35464_I2_run 1 part II fast5.tar&gt; 20171201_0934_FAH35464_I2_run 1 restart 1 fast5.tar&gt; 20171201_0954_FAH35464_I2_run 1 restart 2 nobasecalling fast5.tar&gt; ONT data I2 run fastq.tar<br>Read creation dates: 30-11-2017 and 01-12-2017Albacore run date: 04-12-2017<br>Organism: Amblyseius swirskiiPopulation: Koppert biocontrol population, inbred for 10 generationsCollected: &gt;1000 individuals for DNA extraction, males, females, juveniles, placed in 96% EtOHContamination: Collected with food source, pollen from Typha latifolia, and likely was part of DNA extractionExtraction protocol: QIAGEN MagAttract HMW DNA Kit (QIAGEN) according to the manual, while the final DNA product was eluted with nuclease free waterSequenced using ONT MinION sequencing technology<br>MinION model: MinION Mk1BFlowcell: FAH35464 FLO-MIN107 R9.5Ligation kit: Ligation Sequencing Kit version 108 (SQK-LSK108) (no genomic shearing)MinKNOW: version 1.10.16Reads that were basecalled in the cloud: &gt; /20171130_1635_FAH35464_I2_run 1 mux&gt; /20171130_1635_FAH35464_I2_run 1 full&gt; /20171130_1635_FAH35464_I2_run 1 restart 1 mux&gt; /20171130_1635_FAH35464_I2_run 1 restart 1 fullReads that were basecalled using Albacore (version 2.1.3):&gt; /20171201_0952_FAH35464_I2_run 1 restart 2 nobasecalling mux&gt; /20171201_0954_FAH35464_I2_run 1 restart 2 nobasecalling fullAlbacore results:&gt; /basecalled_reads 20171201_0952_FAH35464_I2_run 1 restart 3 nobasecalling mux&gt; /basecalled_reads 20171201_0954_FAH35464_I2_run 1 restart 3 nobasecalling full<br>Data further used in Paspati et al, in prep<br>Fun facts:<br>Files have been separated into fast5 and fastq files, with additional splitting into parts I and II for "/20171130_1635_FAH35464_I2_run 1 full" due to size. Each fast5 folder has been uploaded separateley<br>fast5 folders are often separated into Fail, Pass, and Skip, this is related to the cloud basecalling by the MinKNOW program, based on initial and ongoing quality assessments.<br>fastq folders and files would be considered raw and uncorrected, and were either basecalled in th cloud or using Albacore (see below). All fastq folders have been compressed and uploaded together.<br>There are two restarts in this sequencing run, due to MinKNOW crashing, either after a few hours or overnight. After the second crash, we decided to switch to no basecalling in hope that this would solve the issues.
DOI: 10.6084/m9.figshare.7429736
title: Whole genome sequencing, Amblyseius swirskii pooled population, ONT MinION -- ONT data I2 run 1 restart 1 fast5

description: ReadMe: ONT data I2 runKim Ferguson, kim.ferguson@wur.nl or kfergy@gmail.comWageningen University and Research<br>This ReadMe file is for all files associated with this project, and is present in each data package.<br>There are five .tar file sets in total available on figshare:&gt; 20171130_1637_FAH35464_I2_run 1 part I fast5.tar&gt; 20171130_1637_FAH35464_I2_run 1 part II fast5.tar&gt; 20171201_0934_FAH35464_I2_run 1 restart 1 fast5.tar&gt; 20171201_0954_FAH35464_I2_run 1 restart 2 nobasecalling fast5.tar&gt; ONT data I2 run fastq.tar<br>Read creation dates: 30-11-2017 and 01-12-2017Albacore run date: 04-12-2017<br>Organism: Amblyseius swirskiiPopulation: Koppert biocontrol population, inbred for 10 generationsCollected: &gt;1000 individuals for DNA extraction, males, females, juveniles, placed in 96% EtOHContamination: Collected with food source, pollen from Typha latifolia, and likely was part of DNA extractionExtraction protocol: QIAGEN MagAttract HMW DNA Kit (QIAGEN) according to the manual, while the final DNA product was eluted with nuclease free waterSequenced using ONT MinION sequencing technology<br>MinION model: MinION Mk1BFlowcell: FAH35464 FLO-MIN107 R9.5Ligation kit: Ligation Sequencing Kit version 108 (SQK-LSK108) (no genomic shearing)MinKNOW: version 1.10.16Reads that were basecalled in the cloud: &gt; /20171130_1635_FAH35464_I2_run 1 mux&gt; /20171130_1635_FAH35464_I2_run 1 full&gt; /20171130_1635_FAH35464_I2_run 1 restart 1 mux&gt; /20171130_1635_FAH35464_I2_run 1 restart 1 fullReads that were basecalled using Albacore (version 2.1.3):&gt; /20171201_0952_FAH35464_I2_run 1 restart 2 nobasecalling mux&gt; /20171201_0954_FAH35464_I2_run 1 restart 2 nobasecalling fullAlbacore results:&gt; /basecalled_reads 20171201_0952_FAH35464_I2_run 1 restart 3 nobasecalling mux&gt; /basecalled_reads 20171201_0954_FAH35464_I2_run 1 restart 3 nobasecalling full<br>Data further used in Paspati et al, in prep<br>Fun facts:<br>Files have been separated into fast5 and fastq files, with additional splitting into parts I and II for "/20171130_1635_FAH35464_I2_run 1 full" due to size. Each fast5 folder has been uploaded separateley<br>fast5 folders are often separated into Fail, Pass, and Skip, this is related to the cloud basecalling by the MinKNOW program, based on initial and ongoing quality assessments.<br>fastq folders and files would be considered raw and uncorrected, and were either basecalled in th cloud or using Albacore (see below). All fastq folders have been compressed and uploaded together.<br>There are two restarts in this sequencing run, due to MinKNOW crashing, either after a few hours or overnight. After the second crash, we decided to switch to no basecalling in hope that this would solve the issues.
DOI: 10.7284/133289
title: Multibeam Echosounder data collected during research cruise NA109 using a Kongsberg EM302 instrument system onboard the platform EV Nautilus

description: Original (unprocessed) data collected during a Field_expedition