Intestinal microbiota is considered to play an integral role in maintaining health of host by modulating several physiological functions including nutrition, metabolism and immunity. Accumulated data from human and animal studies indicate that intestinal microbes can affect lipid metabolism in host through various direct and indirect biological mechanisms. These mechanisms include the production of various signalling molecules by the intestinal microbiome, which exert a strong effect on lipid metabolism, bile secretion in the liver, reverse transport of cholesterol and energy expenditure and insulin sensitivity in peripheral tissues. This review discusses the findings of recent studies suggesting an emerging role of intestinal microbiota and its metabolites in regulating lipid metabolism and the association of intestinal microbiota with obesity. Additionally, we discuss the controversies and challenges in this research area. However, intestinal micro-organisms are also affected by some external factors, which in turn influence the regulation of microbial lipid metabolism. Therefore, we also discuss the effects of probiotics, prebiotics, diet structure, exercise and other factors on intestinal microbiological changes and lipid metabolism regulation.
Melanin is an important antioxidant in food and has been used in medicine and cosmetology. Chicken meat with high melanin content from black-boned chickens have been considered a high nutritious food with potential medicinal properties. The molecular mechanism of melanogenesis of skeletal muscle in black-boned chickens remain poorly understood. This study investigated the biological gene-metabolite associations regulating the muscle melanogenesis pathways in Wuliangshan black-boned chickens with two normal boned chicken breeds as control.We identified 25 differentially expressed genes and 11 transcription factors in the melanogenesis pathways. High levels of the meat flavor compounds inosine monophosphate, hypoxanthine, lysophospholipid, hydroxyoctadecadienoic acid, and nicotinamide mononucleotide were found in Wuliangshan black-boned chickens.Integrative analysis of transcriptomics and metabolomics revealed the dual physiological functions of the PDZK1 gene, involved in pigmentation and/or melanogenesis and regulating the phospholipid signaling processes in muscle of black boned chickens.
The coupling mechanism between depletion of Ca(2+) stores in the endoplasmic reticulum and plasma membrane store-operated ion channels is fundamental to Ca(2+) signaling in many cell types and has yet to be completely elucidated. Using Ca(2+) release-activated Ca(2+) (CRAC) channels in RBL-2H3 cells as a model system, we have shown that CRAC channels are maintained in the closed state by an inhibitory factor rather than being opened by the inositol 1,4,5-trisphosphate receptor. This inhibitory role can be fulfilled by the Drosophila protein INAD (inactivation-no after potential D). The action of INAD requires Ca(2+) and can be reversed by a diffusible Ca(2+) influx factor. Thus the coupling between the depletion of Ca(2+) stores and the activation of CRAC channels may involve a mammalian homologue of INAD and a low-molecular-weight, diffusible store-depletion signal.
Abstract Motivation Recent breakthroughs of single-cell RNA sequencing (scRNA-seq) technologies offer an exciting opportunity to identify heterogeneous cell types in complex tissues. However, the unavoidable biological noise and technical artifacts in scRNA-seq data as well as the high dimensionality of expression vectors make the problem highly challenging. Consequently, although numerous tools have been developed, their accuracy remains to be improved. Results Here, we introduce a novel clustering algorithm and tool RCSL (Rank Constrained Similarity Learning) to accurately identify various cell types using scRNA-seq data from a complex tissue. RCSL considers both local similarity and global similarity among the cells to discern the subtle differences among cells of the same type as well as larger differences among cells of different types. RCSL uses Spearman’s rank correlations of a cell’s expression vector with those of other cells to measure its global similarity, and adaptively learns neighbour representation of a cell as its local similarity. The overall similarity of a cell to other cells is a linear combination of its global similarity and local similarity. RCSL automatically estimates the number of cell types defined in the similarity matrix, and identifies them by constructing a block-diagonal matrix, such that its distance to the similarity matrix is minimized. Each block-diagonal submatrix is a cell cluster/type, corresponding to a connected component in the cognate similarity graph. When tested on 16 benchmark scRNA-seq datasets in which the cell types are well-annotated, RCSL substantially outperformed six state-of-the-art methods in accuracy and robustness as measured by three metrics. Availability The RCSL algorithm is implemented in R and can be freely downloaded at https://github.com/QinglinMei/RCSL . Contact guojunsdu@gmail.com , zcsu@uncc.edu Supplementary information Supplementary data are available at Bioinformatics online.
ABSTRACT Annotating all cis-regulatory modules (CRMs) in genomes is essential to understand genome functions, however, it remains an uncompleted task despite great progresses made since the development of ChIP-seq techniques. As a continued effort, we developed a new algorithm dePCRM2 for predicting CRMs and constituent transcription factor (TF) binding sites (TFBSs) by integrating numerous TF ChIP-seq datasets based on a new ultra-fast, accurate motif-finding algorithm and an efficient combinatory motif pattern discovery method. dePCRM2 partitions genome regions covered by extended binding peaks in the datasets into a CRM candidates (CRMCs) set and a non-CRMCs set, and predicts CRMs and constituent TFBSs by evaluating each CRMC using a novel score. Applying dePCRM2 to 6,092 datasets covering 77.47% of the human genome, we predicted 201 unique TF binding motif families and 1,404,973 CRMCs. Intriguingly, the CRMCs are under stronger evolutionary constraints than the non-CRMCs, and the higher a CRMC can score, the stronger evolutionary constraint it receives and the more likely it is a full-length enhancer. When evaluated on functionally validated VISTA enhancers and causal ClinVar mutants, dePCRM2 achieves 97.43~100.00% sensitivity at p-value ≤ 0.05. dePCRM2 also largely outperforms existing methods in sensitivity and specificity, as well as by the evaluation of evolution constraints. Based on our predictions and evolutionary behaviors of the genome, we estimated that about 21.95% and 54.87% of the genome might code for TFBSs and CRMs, respectively, for which we predicted 80.21%.
Abstract Background Our current understanding of transcription factor binding sites (TFBSs) in sequenced prokaryotic genomes is very limited due to the lack of an accurate and efficient computational method for the prediction of TFBSs at a genome scale. In an attempt to change this situation, we have recently developed a comparative genomics based algorithm called GLECLUBS for de novo genome-wide prediction of TFBSs in a target genome. Although GLECLUBS has achieved rather high prediction accuracy of TFBSs in a target genome, it is still not efficient enough to be applied to all the sequenced prokaryotic genomes. Results Here, we designed a new algorithm based on GLECLUBS called extended GLECLUBS (eGLECLUBS) for simultaneous prediction of TFBSs in a group of related prokaryotic genomes. When tested on a group of γ -proteobacterial genomes including E. coli K12, a group of firmicutes genomes including B. subtilis and a group of cyanobacterial genomes using the same parameter settings, eGLECLUBS predicts more than 82% of known TFBSs in extracted inter-operonic sequences in both E. coli K12 and B. subtilis . Because each genome in a group is equally treated, it is highly likely that similar prediction accuracy has been achieved for each genome in the group. Conclusions We have developed a new algorithm for genome-wide de novo prediction of TFBSs in a group of related prokaryotic genomes. The algorithm has achieved the same level of accuracy and robustness as its predecessor GLECLUBS, but can work on dozens of genomes at the same time.
The Synechococcus WH8102 knowledge base (http://www.csbl.bmb.uga.edu/WH8J02) is a Web based relational database developed to facilitate computational effort to reconstruct regulatory pathways and serve as a gateway for biologist to access the data. It is the repertoire that integrates a variety of knowledge derived both from literature and computational prediction. Those data are organized in hierarchical fashion. The basic building blocks are functional annotation and structure prediction of individual molecule. Those data are then organized into clusters based on computationally predicted operon, regulon and molecular complexes. Finally all data are complied into pathways derived from combined efforts of literature mining and computational prediction. A number of tools have been developed to facilitate the data retrieval including a SQL query engineer and several viewers to browse genome, molecular complexes and pathways.
Abstract Oxygen pressure varies dramatically with altitudes on Earth; however, humans and animals thrive at almost all altitudes. To better understand genetic basis underlying adaptation of closely related species to varying altitudes, we annotated and compared the genome of a white eared pheasant (WT) ( Crossoptilon crossoptilon ) inhabiting high altitudes and the genome of a brown eared pheasant (BR) ( C. mantchuricum ) inhabiting low altitudes, and found that the WT genome harbors 967 more genes but 457 fewer pseudogenes than the BR genome. Moreover, we compared genetic variations in populations of WT and BR as well as of blue eared pheasants (BL) ( C. auritum ) inhabiting intermediate altitudes, and identified thousands of selective sweeps in each species. Intriguingly, the unique genes and pseudogenes in the two genomes converge on the same set of altitude adaptation-related pathways of four functional categories as genes in selective sweeps in each species. Thus, these species appear to adapt to highly varying altitudes by diverging selection on the same traits via loss-of-function mutations and fine-tuning genes in common pathways.
Finding orthologous genes among multiple sequenced genomes is a primary step in comparative genomics studies. With the number of sequenced genomes increasing exponentially, comparative genomics becomes more powerful than ever for genomic analysis. However, the very large number of genomes in need of analysis makes conventional orthology prediction methods incapable of this task. Thus, an ultrafast tool is urgently needed.Here, we present PorthoMCL, a fast tool for finding orthologous genes among a very large number of genomes. PorthoMCL can be run on a single machine or in parallel on computer clusters. We have demonstrated PorthoMCL's capability by identifying orthologs in 2,758 prokaryotic genomes. The results are available for download at: http://ehsun.me/go/porthomcl/.PorthoMCL is a fast and easy to run tool for identifying orthology among any number of genomes with minimal requirements. PorthoMCL will facilitate comparative genomics analysis with increasing number of available genomes thanks to the rapidly evolving sequencing technologies.
Detecting binding motifs of combinatorial transcription factors (TFs) from chromatin immunoprecipitation sequencing (ChIP-seq) experiments is an important and challenging computational problem for understanding gene regulations. Although a number of motif-finding algorithms have been presented, most are either time consuming or have sub-optimal accuracy for processing large-scale datasets. In this article, we present a fully parallelized algorithm for detecting combinatorial motifs from ChIP-seq datasets by using Fisher combined method and OpenMP parallel design. Large scale validations on both synthetic data and 350 ChIP-seq datasets from the ENCODE database showed that FisherMP has not only super speeds on large datasets, but also has high accuracy when compared with multiple popular methods. By using FisherMP, we successfully detected combinatorial motifs of CTCF, YY1, MAZ, STAT3 and USF2 in chromosome X, suggesting that they are functional co-players in gene regulation and chromosomal organization. Integrative and statistical analysis of these TF-binding peaks clearly demonstrate that they are not only highly coordinated with each other, but that they are also correlated with histone modifications. FisherMP can be applied for integrative analysis of binding motifs and for predicting cis-regulatory modules from a large number of ChIP-seq datasets.
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