Bioinformatics analysis of transcriptional regulation of circadian genes in rat liver.

BACKGROUND: The circadian clock is a critical regulator of biological functions controlling behavioral, physiological and biochemical processes. Because the liver is the primary regulator of metabolites within the mammalian body and the disruption of circadian rhythms in liver is associated with severe illness, circadian regulators would play a strong role in maintaining liver function. However, the regulatory structure that governs circadian dynamics within the liver at a transcriptional level remains unknown. To explore this aspect, we analyzed hepatic transcriptional dynamics in Sprague-Dawley rats over a period of 24 hours to assess the genome-wide responses. RESULTS: Using an unsupervised consensus clustering method, we identified four major gene expression clusters, corresponding to central carbon and nitrogen metabolism, membrane integrity, immune function, and DNA repair, all of which have dynamics which suggest regulation in a circadian manner. With the assumption that transcription factors (TFs) that are differentially expressed and contain CLOCK:BMAL1 binding sites on their proximal promoters are likely to be clock-controlled TFs, we were able to use promoter analysis to putatively identify additional clock-controlled TFs besides PARF and RORA families. These TFs are both functionally and temporally related to the clusters they regulate. Furthermore, we also identified significant sets of clock TFs that are potentially transcriptional regulators of gene clusters. CONCLUSIONS: All together, we were able to propose a regulatory structure for circadian regulation which represents alternative paths for circadian control of different functions within the liver. Our prediction has been affirmed by functional and temporal analyses which are able to extend for similar studies.

Deciphering Bacterial and Archaeal Transcriptional Dark Matter and Its Architectural Complexity.

Transcripts are potential therapeutic targets, yet bacterial transcripts remain biological dark matter with uncharacterized biodiversity. We developed and applied an algorithm to predict transcripts for Escherichia coli K12 and E2348/69 strains (Bacteria:gamma-Proteobacteria) with newly generated ONT direct RNA sequencing data while predicting transcripts for Listeria monocytogenes strains Scott A and RO15 (Bacteria:Firmicute), Pseudomonas aeruginosa strains SG17M and NN2 strains (Bacteria:gamma-Proteobacteria), and Haloferax volcanii (Archaea:Halobacteria) using publicly available data. From >5 million E. coli K12 ONT direct RNA sequencing reads, 2,484 mRNAs are predicted and contain more than half of the predicted E. coli proteins. While the number of predicted transcripts varied by strain based on the amount of sequence data used for the predictions, across all strains examined, the average size of the predicted mRNAs is 1.6-1.7 kbp while the median size of the predicted bacterial 5'- and 3'- UTRs are 30-90 bp. Given the lack of bacterial and archaeal transcript annotation, most predictions are of novel transcripts, but we also predicted many previously characterized mRNAs and ncRNAs, including post-transcriptionally generated transcripts and small RNAs associated with pathogenesis in the E. coli E2348/69 LEE pathogenicity islands. We predicted small transcripts in the 100-200 bp range as well as >10 kbp transcripts for all strains, with the longest transcript for two of the seven strains being the nuo operon transcript, and for another two strains it was a phage/prophage transcript. This quick, easy, inexpensive, and reproducible method will facilitate the presentation of operons, transcripts, and UTR predictions alongside CDS and protein predictions in bacterial genome annotation as important resources for the research community.

Dynamics of hepatic gene expression profile in a rat cecal ligation and puncture model.

BACKGROUND: Sepsis remains a major clinical challenge in intensive care units. The difficulty in developing new and more effective treatments for sepsis exemplifies our incomplete understanding of the underlying pathophysiology of it. One of the more widely used rodent models for studying polymicrobial sepsis is cecal ligation and puncture (CLP). While a number of CLP studies investigated the ensuing systemic inflammatory response, they usually focus on a single time point post-CLP and therefore fail to describe the dynamics of the response. Furthermore, previous studies mostly use surgery without infection (herein referred to as sham CLP, SCLP) as a control for the CLP model, however, SCLP represents an aseptic injurious event that also stimulates a systemic inflammatory response. Thus, there is a need to better understand the dynamics and expression patterns of both injury- and sepsis-induced gene expression alterations to identify potential regulatory targets. In this direction, we characterized the response of the liver within the first 24 h in a rat model of SCLP and CLP using a time series of microarray gene expression data. METHODS: Rats were randomly divided into three groups: sham, SCLP, and CLP. Rats in SCLP group are subjected to laparotomy, cecal ligation, and puncture while those in CLP group are subjected to the similar procedures without cecal ligation and puncture. Animals were saline resuscitated and sacrificed at defined time points (0, 2, 4, 8, 16, and 24 h). Liver tissues were explanted and analyzed for their gene expression profiles using microarray technology. Unoperated animals (Sham) serve as negative controls. After identifying differentially expressed probesets between sham and SCLP or CLP conditions over time, the concatenated data sets corresponding to these differentially expressed probesets in sham and SCLP or CLP groups were combined and analyzed using a "consensus clustering" approach. Promoters of genes that share common characteristics were extracted and compared with gene batteries comprised of co-expressed genes to identify putatative transcription factors, which could be responsible for the co-regulation of those genes. RESULTS: The SCLP/CLP genes whose expression patterns significantly changed compared with sham over time were identified, clustered, and finally analyzed for pathway enrichment. Our results indicate that both CLP and SCLP triggered the activation of a proinflammatory response, enhanced synthesis of acute-phase proteins, increased metabolism, and tissue damage markers. Genes triggered by CLP, which can be directly linked to bacteria removal functions, were absent in SCLP injury. In addition, genes relevant to oxidative stress induced damage were unique to CLP injury, which may be due to the increased severity of CLP injury versus SCLP injury. Pathway enrichment identified pathways with similar functionality but different dynamics in the two injury models, indicating that the functions controlled by those pathways are under the influence of different transcription factors and regulatory mechanisms. Putatively identified transcription factors, notably including cAMP response element-binding (CREB), nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), and signal transducer and activator of transcription (STAT), were obtained through analysis of the promoter regions in the SCLP/CLP genes. Our results show that while transcription factors such as NF-κB, homeodomain transcription factor (HOMF), and GATA transcription factor (GATA) were common in both injuries for the IL-6 signaling pathway, there were many other transcription factors associated with that pathway which were unique to CLP, including forkhead (FKHD), hairy/enhancer of split family (HESF), and interferon regulatory factor family (IRFF). There were 17 transcription factors that were identified as important in at least two pathways in the CLP injury, but only seven transcription factors with that property in the SCLP injury. This also supports the hypothesis of unique regulatory modules that govern the pathways present in both the CLP and SCLP response. CONCLUSIONS: By using microarrays to assess multiple genes in a high throughput manner, we demonstrate that an inflammatory response involving different dynamics and different genes is triggered by SCLP and CLP. From our analysis of the CLP data, the key characteristics of sepsis are a proinflammatory response, which drives hypermetabolism, immune cell activation, and damage from oxidative stress. This contrasts with SCLP, which triggers a modified inflammatory response leading to no immune cell activation, decreased detoxification potential, and hyper metabolism. Many of the identified transcription factors that drive the CLP-induced response are not found in the SCLP group, suggesting that SCLP and CLP induce different types of inflammatory responses via different regulatory pathways.

Long-term gene expression profile dynamics following cecal ligation and puncture in the rat.

BACKGROUND: Despite the fact that the treatment options for septic patients have been significantly improved, the pathophysiologic changes caused by various septic cases have not been well understood. One commonly observed clinical phenomenon is the onset of a polymicrobial infection caused by bacteria that originate in the intestine but enter the peritoneum via translocation from the gut. This triggers a systemic inflammatory response via the innate immune system, which needs to be well characterized. Cecal ligation and puncture (CLP) is considered to be the gold-standard animal model by establishing infection with mixed bacterial flora and necrotic tissue to induce an inflammatory response. The aim of this study is to analyze the long-term gene expression dynamics in the rats subject to CLP in order to characterize the impact of sepsis upon liver function over an 8-d time period. METHODS: Rats received CLP or its control, sham CLP (SCLP), and then they were sacrificed at 9 am on days 0 (no treatment), 1, 2, 5, and 8 post injury to collect liver samples for microarray analysis. Differentially expressed probe sets in CLP versus SCLP (q value <0.001 and P value <0.001) were combined to form one single matrix, which was then clustered using the approach of "consensus clustering" to identify subsets of transcripts with coherent expression patterns. Finally, the gene expression patterns of the clusters were further transformed into principal components, which account for 65% of the total data. RESULTS: Three major clusters were obtained. The first cluster, which is mainly related to genes of anti-inflammatory response and antioxidative properties, is suppressed early in the CLP condition and later upregulated compared to the SCLP condition. Cluster 2 represents pro-inflammatory responses and signaling, along with amino acid metabolism. Cluster 3 is also associated with pro-inflammatory response. The genes of toll-like receptor signaling and hypermetabolism were identified in this cluster as well. Clusters 2 and 3 are both suppressed in the long-term response following CLP. Clusters 1 and 2 acting in concert return to the time 0 baseline in both groups, indicating resolution of both the anti-inflammatory and pro-inflammatory response; however, the SCLP response in cluster 3 shows persistent downregulation. CONCLUSIONS: Characterization of long-term hepatic responses to injury is critical to understanding the dynamics of transcriptional changes following the induction of the inflammatory response, and to monitoring its effective resolution. These results showed that each condition has unique dynamics that indicate fundamental differences in the response. Furthermore, the gene ontologies suggest a link to oxidative stress over the long term that may be able to be explored for clinical treatments.

FADU: a Quantification Tool for Prokaryotic Transcriptomic Analyses.

Quantification tools for RNA sequencing (RNA-Seq) analyses are often designed and tested using human transcriptomics data sets, in which full-length transcript sequences are well annotated. For prokaryotic transcriptomics experiments, full-length transcript sequences are seldom known, and coding sequences must instead be used for quantification steps in RNA-Seq analyses. However, operons confound accurate quantification of coding sequences since a single transcript does not necessarily equate to a single gene. Here, we introduce FADU (Feature Aggregate Depth Utility), a quantification tool designed specifically for prokaryotic RNA-Seq analyses. FADU assigns partial count values proportional to the length of the fragment overlapping the target feature. To assess the ability of FADU to quantify genes in prokaryotic transcriptomics analyses, we compared its performance to those of eXpress, featureCounts, HTSeq, kallisto, and Salmon across three paired-end read data sets of (i) Ehrlichia chaffeensis, (ii) Escherichia coli, and (iii) the Wolbachia endosymbiont wBm. Across each of the three data sets, we find that FADU can more accurately quantify operonic genes by deriving proportional counts for multigene fragments within operons. FADU is available at https://github.com/IGS/FADUIMPORTANCE Most currently available quantification tools for transcriptomics analyses have been designed for human data sets, in which full-length transcript sequences, including the untranslated regions, are well annotated. In most prokaryotic systems, full-length transcript sequences have yet to be characterized, leading to prokaryotic transcriptomics analyses being performed based on only the coding sequences. In contrast to eukaryotes, prokaryotes contain polycistronic transcripts, and when genes are quantified based on coding sequences instead of transcript sequences, this leads to an increased abundance of improperly assigned ambiguous multigene fragments, specifically those mapping to multiple genes in operons. Here, we describe FADU, a quantification tool for prokaryotic RNA-Seq analyses designed to assign proportional counts with the purpose of better quantifying operonic genes while minimizing the pitfalls associated with improperly assigning fragment counts from ambiguous transcripts.

Distinguishing potential bacteria-tumor associations from contamination in a secondary data analysis of public cancer genome sequence data.

BACKGROUND: A variety of bacteria are known to influence carcinogenesis. Therefore, we sought to investigate if publicly available whole genome and whole transcriptome sequencing data generated by large public cancer genome efforts, like The Cancer Genome Atlas (TCGA), could be used to identify bacteria associated with cancer. The Burrows-Wheeler aligner (BWA) was used to align a subset of Illumina paired-end sequencing data from TCGA to the human reference genome and all complete bacterial genomes in the RefSeq database in an effort to identify bacterial read pairs from the microbiome. RESULTS: Through careful consideration of all of the bacterial taxa present in the cancer types investigated, their relative abundance, and batch effects, we were able to identify some read pairs from certain taxa as likely resulting from contamination. In particular, the presence of Mycobacterium tuberculosis complex in the ovarian serous cystadenocarcinoma (OV) and glioblastoma multiforme (GBM) samples was correlated with the sequencing center of the samples. Additionally, there was a correlation between the presence of Ralstonia spp. and two specific plates of acute myeloid leukemia (AML) samples. At the end, associations remained between Pseudomonas-like and Acinetobacter-like read pairs in AML, and Pseudomonas-like read pairs in stomach adenocarcinoma (STAD) that could not be explained through batch effects or systematic contamination as seen in other samples. CONCLUSIONS: This approach suggests that it is possible to identify bacteria that may be present in human tumor samples from public genome sequencing data that can be examined further experimentally. More weight should be given to this approach in the future when bacterial associations with diseases are suspected.

Branched-chain amino acid supplementation: impact on signaling and relevance to critical illness.

The changes that occur in mammalian systems following trauma and sepsis, termed systemic inflammatory response syndrome, elicit major changes in carbohydrate, protein, and energy metabolism. When these events persist for too long they result in a severe depletion of lean body mass, multiple organ dysfunction, and eventually death. Nutritional supplementation has been investigated to offset the severe loss of protein, and recent evidence suggests that diets enriched in branched-chain amino acids (BCAAs) may be especially beneficial. BCAAs are metabolized in two major steps that are differentially expressed in muscle and liver. In muscle, BCAAs are reversibly transaminated to the corresponding α-keto acids. For the complete degradation of BCAAs, the α-keto acids must travel to the liver to undergo oxidation. The liver, in contrast to muscle, does not significantly express the branched-chain aminotransferase. Thus, BCAA degradation is under the joint control of both liver and muscle. Recent evidence suggests that in liver, BCAAs may perform signaling functions, more specifically via activation of mTOR (mammalian target of rapamycin) signaling pathway, influencing a wide variety of metabolic and synthetic functions, including protein translation, insulin signaling, and oxidative stress following severe injury and infection. However, understanding of the system-wide effects of BCAAs that integrate both metabolic and signaling aspects is currently lacking. Further investigation in this respect will help rationalize the design and optimization of nutritional supplements containing BCAAs for critically ill patients.