Twelve C57BL6J female mice (aged 8 weeks old) were bred in a specific pathogen-free laboratory (temperature, 20–24°C; humidity, 40–70%) at Shandong University (Jinan, China). Mice were acclimated to the feed for 1 week prior to the initiation of experimental intervention. All mice were housed in standard cages and had free access to food and water. Following this, six mice were randomly selected and labeled as control group. The other 6 mice were fasted for 12 h, then injected with 1% streptozotocin (Sigma-Aldrich; Merck KGaA, Darmstadt, Germany) solution which was dissolved in 0.05 mol/l sodium citrate (pH 4.5; Beyotime Institute of Biotechnology, Haimen, China), at a dose of 60 mg/kg and injected for 5 days continuously. The control group had intraperitoneal injections of buffer solution at the same dose. After 10 days, the tail vein blood glucose level was detected through a blood glucose meter (Bayer, Newbury, UK). All six mice injected with 1% streptozotocin solution exhibited high blood glucose levels (≥16.7 mmol/l) and were considered to be diabetic model mice (25). The control group and the diabetes group were administered daily 10 ml/kg intragastric normal saline treatments for a total of 12 weeks. Mice were weighed and were subjected to tail vein blood glucose testing once each week.

Following the final saline treatment, the mice were anaesthetized by intraperitoneal injection of 0.8% pentobarbital (Sigma-Aldrich; Merck KGaA). The mouse limbs were fixed and the abdomen was exposed, then the abdominal skin, peritoneum and diaphragm were cut to expose the heart. Left ventricular blood was withdrawn through a 1 ml syringe then this injected into a coagulation + separation tube for extraction by 10 min centrifugation at 900 × g at 20°C. Extracted serum was kept frozen at −80°C within a cryopreservation tube. The brain tissue was then obtained and then the hippocampus tissue was isolated and subsequently stored at −80°C. The protocol for the present study was approved by the Laboratory Animal Ethics committee of the Qilu Hospital of Shandong University.

TRIzol^® Reagent (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA; cat. no. 15596-026); NucleoSpin^® RNA clean-up (cat. no. 740.948.250); Nucleospin^® Extract II (cat. no. 740.609.250; both Machery-Nagel, GmbH, Düren, Germany); GeeDom bio-chip universal label reagent (Capital Biotechnology, Co., Ltd., Beijing, China; cat. no. 360069); Cy3-dCTP (GE Healthcare, Chicago, IL, USA; cat. no. PA53031); Cy5-dCTP (GE Healthcare; cat. no. PA55031); 10% SDS; 20 × sodium citrate buffer (both Capital Biotechnology, Co., Ltd.); Agilent one-color RNA Spike-in kit (cat. no. 5188-5282); Agilent two-color RNA Spike-in kit (cat. no. 5188-5279; both Agilent Technologies Inc., Santa Clara, CA, USA); RNAiso Plus kit (Takara Biotechnology Co., Ltd., Dalian, China); TransScript One-step gDNA Removal and cDNA Synthesis SuperMix kits; TransStart Tip Green qPCR Super MixSYBR; Reverse Transcriptase kit; and TaqMan MGB probes (all Beijing Transgen Biotech Co., Ltd., Beijing, China) were purchased for use in the present study.

A 5810R centrifuge was purchased from Eppendorf (Hamburg, Germany); the spectrophotometer ND-1000 was from NanoDrop; Thermo Fisher Scientific, Inc. (Wilmington, DE, USA); the concentrator (instrument model 5301) was from Eppendorf. The Peltier Thermal Cycler PTC-225 (MJ Research, Inc., Waltham, MA, USA). The hybridization Oven (G2545A) was from Agilent Technologies, Inc., as was the Agilent G2565CA Microarray Scanner.

Total RNA samples were extracted from three healthy mice (group A) and three type 2 diabetic mice (group B). Total RNA was isolated using TRIzol^® reagent (Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer's protocol. The first and second strand cDNA were synthesized with a PrimeScript™ Double Strand cDNA Synthesis kit according to the manufacturer's protocol (Takara Biotechnology Co., Ltd.). Complementary RNA (cRNA) was transcribed via the T7 Enzyme Mix kit using the second strand cDNA as a template and subsequently purified using NucleoSpin^® RNA clean-up (740.948.250; Machery-Nagel, GmbH) to eliminate reagents like salt and enzymes according to the manufacturer's protocol. In addition, the aforementioned dNTPs and fluorophores were added to the mixture. Following this, the purified RNA was reverse transcribed using a Reverse Transcriptase kit (Beijing Transgen Biotech Co., Ltd.).

cDNA containing fluorescent labels Cy3-dCTP (GE Healthcare; cat. nos. PA53031) or Cy5-dCTP (GE Healthcare; cat. no. PA55031) or Cy3-dCTP and Cy5-dCTP together were hybridized on the GeeDom human lncRNA+mRNA V4.0 array with GeeDom bio-chip universal label reagent. The microarrays were washed and then scanned using an Agilent G2565CA Microarray Scanner (Agilent Technologies, Inc.) The resulting expression hybridization chip picture was analyzed using Feature Extraction software 10.7 (Agilent Technologies, Inc.). Data normalization and quality control analysis was performed for each sample using GeneSpring GX 11.0 (Agilent Technologies, Inc.). Gene expression differences and P-values were assessed by GeneSpring GX 11.0. Cluster analysis was performed using Cluster 3.0 software (Cluster Software, Inc.). Gene Ontology Enrichment Analysis (GO analysis; http://geneontology.org/) of the target genes, Protein ANalysis THrough Evolutionary Relationships (PANTHER; http://www.pantherdb.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/pathway.html) pathway analyses, as well as BioCarta (https://cgap.nci.nih.gov/Pathways/BioCarta_Pathways), Pathway Interaction Database (PID; http://pid.nci.nih.gov/), BioCyc (https://biocyc.org/) and Reactome (https://reactome.org/cgi-bin/frontpage?DB=gk_current) analyses, were performed on differential mRNAs. lncRNA-mRNA co-expression analysis was addressed along with target gene prediction and transcription factor prediction for each biological repeat.

Total RNA was isolated using TRIzol^® reagent (Invitrogen; Thermo Fisher Scientific, Inc.) according to the manufacturer's protocol. RNA was reverse transcribed to cDNA with TransScript One-step gDNA Removal and cDNA Synthesis SuperMix kits according to the manufacturer's protocol (Transgen Biotechnology Co., Ltd.). RT-qPCR was performed using TransStart Tip Green qPCR Super MixSYBR according to the manufacturer's protocol (Transgen Biotechnology Co., Ltd.). The thermocycling conditions for qPCR were as follows: Initial denaturation at 95°C for 30 sec, followed by 40 cycles at 95°C for 5 sec, 60°C annealing for 30 sec. Sequences of primers used for qPCR are presented in Table I. Relative gene expression data was analyzed using the 2^−∆∆Cq method (26).

Statistical analysis was performed using SPSS v20 software (IBM Corp., Armonk, NY, USA). All data are expressed as the mean ± standard deviation. A T-test indicated consistency between microarray data and RT-qPCR results. P<0.05 was considered to indicate a statistically significant difference. All experiments were repeated in triplicate.

A total of 36,793 lncRNAs were detected within the hippocampus of type 2 diabetic mice. LncRNA and mRNA data revealed differential expression of lncRNA between healthy and diabetic mice captured through data analysis, with differential expression criteria set as fold-change ≥2.0 and P<0.05. Compared with the control mice (group A), diabetic mice (group B) exhibited differential expression of 130 lncRNAs, where 126 lncRNAs were demonstrated to be upregulated and 4 lncRNAs were downregulated. The expression levels of lncRNAs were visualized using clustering analysis (Fig. 1A). The differential expression ratios and P-values of lncRNAs in the two groups were also compared using volcano (Fig. 1B) and scatter plots (Fig. 1C). The results revealed a marked difference in the expression levels of lncRNAs exhibited by groups A and B (red points), which suggested that the regulation of lncRNAs is associated with the occurrence and development of type 2 diabetes.

A total of 30,551 mRNAs were detected within the hippocampus of type 2 diabetic mice. mRNA data was collected from image and data analysis, where the differential expression criteria was set as fold-change ≥2.0 and P<0.05. Compared with group A, group B presented 49 mRNAs with altered expression, where 45 exhibited upregulation and 4 displayed downregulation. The expression levels of mRNAs in groups A and B were visualized using clustering analysis (Fig. 2A). The differential expression ratios and P-values of mRNAs in the two groups were also compared using volcano (Fig. 2B) and scatter plots (Fig. 2C). Fig. 2 demonstrates mRNA differential expression association between different groups.

RT-qPCR was performed in order to verify the reliability of microarray data. A total of three differentially expressed mRNAs (lmx1a, slc5a7 and prr32) with high expression in the central nervous system were selected from the 49 differentially expressed mRNAs as revealed in Fig. 2A. Alterations in expression levels of lmx1a (Fig. 3A), prr32 (Fig. 3B) and slc5a7 (Fig. 3C) revealed by RT-PCR were similar to those observed in microarray data. A T-test indicated consistency between microarray data and RT-qPCR results, and demonstrated that our microarray data is reliable and can be used for bioinformatics analysis in subsequent steps.

GO analysis is known as gene enrichment analysis which can be used to confirm gene and gene production associated bio-processes, cellular composition and molecular functions. GO and KEGG analyses can be used to further evaluate the function of significantly expressing trend genes. As presented in Fig. 4, GO enrichment analysis (identification of the biological processes involved in the enrichment of transcripts) indicated that cognitive dysfunction in type 2 diabetes was correlated with transport, cell adhesion, ion transport, metabolic processes and other important biological processes. In addition, KEGG and Reactome enrichment analysis, which were performed to validate important module functions, demonstrated that the pathology of cognitive dysfunction in type 2 diabetes is associated with the regulation of cholinergic synapses, the NF-kB pathway, the Toll like receptor 4 (TLR4) cascade, and the zinc transporter (Fig. 5).

In order to identify important molecular mechanisms of lncRNA associated with cognitive dysfunction in type 2 diabetes, a dynamic lncRNA-mRNA network (Fig. 6) was constructed, which contained 123 lncRNA and 48 mRNA. To address interactions between genes, a threshold of ≥0.997 was implemented to evaluate the coexpression of lncRNA and mRNA via quantization of the degree and the size of the circle. From the figure, crucial lncRNAs were demonstrated, which may serve an important role in the pathology of type 2 diabetes, including lncRNA ENSMUST00000188753, lncRNA gi/755552777/ref/XR_875577.1/ and lncRNA gi/755494846/ref/XR_387234.2/. In this figure, the size of a circle indicates the ability of a gene to interact according to its degree of quantification.

A co-expression network based on differential lncRNAs and mRNA of healthy and type 2 diabetic mice is presented. Green nodes represent mRNAs and yellow nodes represent lncRNAs. Red lines express positive correlation and blue lines express negative correlation.