Dietary restriction (DR) is widely regarded as a viable intervention to extend lifespan and healthspan in diverse organisms. The precise molecular regulatory mechanisms are largely unknown. Epigenetic modifications are not stable upon DR and also keep changing with age. Here, we employed whole genome bisulfite sequencing to determine the DNA methylation changes upon DR in adult Drosophila. Our results indicate that although a low level of DNA methylation exists in the adult Drosophila genome, there is no significant difference in DNA methylation levels upon DR when compared to unrestricted flies. This suggests that other epigenetic components such as histone modifications might be altered by DR.

Pleiotropy, the effect of one variant on multiple traits, is widespread in complex diseases. Joint analysis of multiple traits can improve statistical power to detect genetic variants and uncover the underlying genetic mechanism. Currently, a large number of existing methods target one common variant or only rare variants. Increasing evidence shows that complex diseases are caused by common and rare variants. Here we propose a region-based method to test both rare and common variant associated multiple traits based on variable reduction method (abbreviated as MULVR). However, in the presence of noise traits, the MULVR method may lose power, so we propose the MULVR-O method, which jointly analyses the optimal number of traits associated with genetic variants by the MULVR method, to guard against the effect of noise traits. Extensive simulation studies show that our proposed method (MULVR-O) is applied to not only multiple quantitative traits but also qualitative traits, and is more powerful than several other comparison methods in most scenarios. An application to the two genes (SHBG and CHRM3) and two phenotypes (systolic blood pressure and diastolic blood pressure) from the GAW19 dataset illustrates that our proposed methods (MULVR and MULVR-O) are feasible and efficient as a region-based method.

Maternal gestational diabetes mellitus (GDM) is considered to be an important factor that epigenetically predisposes offspring to metabolic and cardiovascular diseases. However, the mechanisms of how intrauterine hyperglycaemia affects offspring have not been thoroughly studied. The mammalian tribbles homologue 1 (TRIB1) gene is associated with plasma lipid concentrations and coronary artery disease (CAD). Our aim was to study the effect of GDM and its treatment terms on the level of TRIB1 gene expression in human umbilical vein endothelial cells (HUVECs) of newborns from women with and without GDM. The study included 50 women with GDM and 25 women without GDM (control group). Women with GDM were divided into three groups according to their gestational age when the treatment of GDM started: 24–28 weeks (GDM1, N = 16), 29–32 weeks (GDM2, N = 25) and >34 weeks (GDM3, N = 9). The levels of TRIB1 gene expression in GDM3, GDM2, GDM1 and control groups were 2.8 ± 1.1, 4.2 ± 2.4, 6.0 ± 3.4 and 8.1 ± 6.1, respectively (p = 0.001). After comparison in pairs the difference was significant for the following pairs: GDM2–control (p = 0.004), GDM3–control (p = 0.002), GDM1–GDM3 (p = 0.012). Notably, if treatment had been started before the 28th week of gestation, the difference in TRIB1 gene expression in HUVECs was not significant (p = 0.320 for comparison between GDM1 and control groups). Our findings support the hypothesis that TRIB1 gene expression in HUVECs depends on the duration of intrauterine exposure to hyperglycaemia.

Previous studies have generated controversial findings regarding the correlation between DNA methylation in the human genome and gene expression. Some reports have indicated that promoter methylation is negatively correlated with gene expression levels; however, in some cases, a poor or positive correlation was reported. Most previous findings were based on general trends observed with whole-genome data analysis. Here, we present a novel chromosome-specific statistical analysis design of empirical Bayes differential tests for five phases of erythroid development. To better understand the common methylation patterns of differentially methylated regions (DMRs) during specific stages, we defined differential phases for each CpG locus, based on a maximum log2 fold change. Analyzing hypermethylated and hypomethylated CpG loci separately showed variations in methylation patterns during erythropoiesis in the gene body, promoter and enhancer regions. Hypomethylated DMRs showed stronger associations with erythroid-specific enhancers at the differentiation start phase and with exons in the intermediate phase. To investigate the hypomethylated DMRs further, transcription factor binding site-enrichment analysis was conducted. This analysis highlighted novel transcription factors during each differentiation stage that were not detected by previous differential methylation data analysis. In contrast, hypermethylated DMRs showed a consistent methylation pattern over the different genomic regions. Thus, a closer examination of DNA methylation patterns in a single chromosome during each developmental stage can contribute to verify the association nature between gene expression and DNA methylation.

Owing to the development of new technologies, the epigenome, a second dimensional method for genome analysis has emerged. Epigenetic mechanisms, including DNA methylation, histone modifications and noncoding RNAs, regulate gene expression without changing the genetic sequence. These epigenetic mechanisms normally modulate gene expression, trans-generational effects and inherited expression states in various biological processes. Abnormal epigenetic patterns typically cause pathological conditions, including cancers, age-related diseases, and specific cartilage and bone diseases. Facing the rapidly developing epigenetic field, we reviewed epigenetic mechanisms and their involvement with the skeletal system and their role in skeletal development, homeostasis and degeneration. Finally, we discuss the prospects for the future of epigenetics.

Primary microcephaly (MCPH) is an autosomal recessive sporadic neurodevelopmental ailment with a trivial head size characteristic that is below 3–4 standard deviations. MCPH is the smaller upshot of an architecturally normal brain; a significant decrease in size is seen in the cerebral cortex. At birth MCPH presents with non-progressive mental retardation, while secondary microcephaly (onset after birth) presents with and without other syndromic features. MCPH is a neurogenic mitotic syndrome nevertheless pretentious patients demonstrate normal neuronal migration, neuronal apoptosis and neural function. Eighteen MCPH loci (MCPH1–MCPH18) have been mapped to date from various populations around the world and contain the following genes: Microcephalin, WDR62, CDK5RAP2, CASC5, ASPM, CENPJ, STIL, CEP135, CEP152, ZNF335, PHC1, CDK6, CENPE, SASS6, MFSD2A, ANKLE2, CIT and WDFY3, clarifying our understanding about the molecular basis of microcephaly genetic disorder. It has previously been reported that phenotype disease is caused by MCB gene mutations and the causes of this phenotype are disarrangement of positions and organization of chromosomes during the cell cycle as a result of mutated DNA, centriole duplication, neurogenesis, neuronal migration, microtubule dynamics, transcriptional control and the cell cycle checkpoint having some invisible centrosomal process that can manage the number of neurons that are produced by neuronal precursor cells. Furthermore, researchers inform us about the clinical management of families that are suffering from MCPH. Establishment of both molecular understanding and genetic advocating may help to decrease the rate of this ailment. This current review study examines newly identified genes along with previously identified genes involved in autosomal recessive MCPH.

With the advancement of high-throughput sequencing technologies, the amount of available sequencing data is growing at a pace that has now begun to greatly challenge the data processing and storage capacities of modern computer systems. Removing redundancy from such data by clustering could be crucial for reducing memory, disk space and running time consumption. In addition, it also has good performance on reducing dataset noise in some analysis applications. In this study, we propose a high-performance short sequence classification algorithm (HSC) for next generation sequencing (NGS) data based on efficient hash function and text similarity. First, HSC converts all reads into k-mers, then it forms a unique k-mer set by merging the duplicated and reverse complementary elements. Second, all unique k-mers are stored in a hash table, where the k-mer string is stored in the key field, and the ID of the reads containing the k-mer are stored in the value field. Third, each hash unit is transformed into a short text consisting of reads. Fourth, texts that satisfy the similarity threshold are combined into a long text, the merge operation is executed iteratively until there is no text that satisfies the merge condition. Finally, the long text is transformed into a cluster consisting of reads. We tested HSC using five real datasets. The experimental results showed that HSC cluster 100 million short reads within 2 hours, and it has excellent performance in reducing memory consumption. Compared to existing methods, HSC is much faster than other tools, it can easily handle tens of millions of sequences. In addition, when HSC is used as a preprocessing tool to produce assembly data, the memory and time consumption of the assembler is greatly reduced. It can help the assembler to achieve better assemblies in terms of N50, NA50 and genome fraction.