Agent-based modeling of competence phenotype switching in Bacillus subtilis

BackgroundIt is a fascinating phenomenon that in genetically identical bacteria populations of Bacillus subtilis, a distinct DNA uptake phenotype called the competence phenotype may emerge in 10–20% of the population. Many aspects of the phenomenon are believed to be due to the variable expression of critical genes: a stochastic occurrence termed “noise” which has made the phenomenon difficult to examine directly by lab experimentation.MethodsTo capture and model noise in this system and further understand the emergence of competence both at the intracellular and culture levels in B. subtilis, we developed a novel multi-scale, agent-based model. At the intracellular level, our model recreates the regulatory network involved in the competence phenotype. At the culture level, we simulated growth conditions, with our multi-scale model providing feedback between the two levels.ResultsOur model predicted three potential sources of genetic “noise”. First, the random spatial arrangement of molecules may influence the manifestation of the competence phenotype. In addition, the evidence suggests that there may be a type of epigenetic heritability to the emergence of competence, influenced by the molecular concentrations of key competence molecules inherited through cell division. Finally, the emergence of competence during the stationary phase may in part be due to the dilution effect of cell division upon protein concentrations.ConclusionsThe competence phenotype was easily translated into an agent-based model – one with the ability to illuminate complex cell behavior. Models such as the one described in this paper can simulate cell behavior that is otherwise unobservable in vivo, highlighting their potential usefulness as research tools

Genetic improvement of tomato by targeted control of fruit softening

Controlling the rate of softening to extend shelf life was a key target for researchers engineering genetically modified (GM) tomatoes in the 1990s, but only modest improvements were achieved. Hybrids grown nowadays contain 'non-ripening mutations' that slow ripening and improve shelf life, but adversely affect flavor and color. We report substantial, targeted control of tomato softening, without affecting other aspects of ripening, by silencing a gene encoding a pectate lyase

Differentially expressed genes (DEGs) identified from RNA-Seq by DESeq and Cuffdiff compared to microarray.

<p>Panel A. Number of DEGs observed with change ≥2-fold at p≤0.005 by DESeq, microarray and Cuffdiff analyses with Cufflinks assembled transcripts for all eight animals (4 Control, 4 AFB1) is shown. Total number of Cufflinks assembled transcripts and those that match an existing RefSeq annotation are displayed in the bar chart. Panel B. Venn diagram of DEGs from Panel A shows the number of common transcripts (overlapping circles) and unique transcripts (non-overlapping circles) for all three analyses. Panel C. Principal component (PC) analysis was performed for all samples using the gene expression values for DEGs found by microarray and DESeq (on Cufflinks assembled transcripts) analysis. The percentage variability captured by the first three principal components is displayed across PC#1, 2 and 3 represented on X, Y and Z axes. Panel D. Heatmap shows all DEGs at ≥2-fold change, p≤0.005 from microarray or DESeq analyses (on Cufflinks assembled transcripts). Gene expression data were log2 transformed and then quantile normalized prior to generating the Heatmap for direct comparison of data. DEGs (red or green are upregulated or downregulated, respectively) for each animal were mapped by lane for each of four animals in the CNTL or AFB1 treatment groups.</p

RNA-Seq Profiling Reveals Novel Hepatic Gene Expression Pattern in Aflatoxin B1 Treated Rats

<div><p>Deep sequencing was used to investigate the subchronic effects of 1 ppm aflatoxin B1 (AFB1), a potent hepatocarcinogen, on the male rat liver transcriptome prior to onset of histopathological lesions or tumors. We hypothesized RNA-Seq would reveal more differentially expressed genes (DEG) than microarray analysis, including low copy and novel transcripts related to AFB1’s carcinogenic activity compared to feed controls (CTRL). Paired-end reads were mapped to the rat genome (Rn4) with TopHat and further analyzed by DESeq and Cufflinks-Cuffdiff pipelines to identify differentially expressed transcripts, new exons and unannotated transcripts. PCA and cluster analysis of DEGs showed clear separation between AFB1 and CTRL treatments and concordance among group replicates. qPCR of eight high and medium DEGs and three low DEGs showed good comparability among RNA-Seq and microarray transcripts. DESeq analysis identified 1,026 differentially expressed transcripts at greater than two-fold change (p<0.005) compared to 626 transcripts by microarray due to base pair resolution of transcripts by RNA-Seq, probe placement within transcripts or an absence of probes to detect novel transcripts, splice variants and exons. Pathway analysis among DEGs revealed signaling of Ahr, Nrf2, GSH, xenobiotic, cell cycle, extracellular matrix, and cell differentiation networks consistent with pathways leading to AFB1 carcinogenesis, including almost 200 upregulated transcripts controlled by E2f1-related pathways related to kinetochore structure, mitotic spindle assembly and tissue remodeling. We report 49 novel, differentially-expressed transcripts including confirmation by PCR-cloning of two unique, unannotated, hepatic AFB1-responsive transcripts (HAfT’s) on chromosomes 1.q55 and 15.q11, overexpressed by 10 to 25-fold. Several potentially novel exons were found and exon refinements were made including AFB1 exon-specific induction of homologous family members, Ugt1a6 and Ugt1a7c. We find the rat transcriptome contains many previously unidentified, AFB1-responsive exons and transcripts supporting RNA-Seq’s capabilities to provide new insights into AFB1-mediated gene expression leading to hepatocellular carcinoma.</p></div

Exon specific expression among homologous transcripts in the Ugt1a gene family.

<p>Panel A. The genomic region for Ugt1a transcripts is displayed on the X-axis in UCSC browser format where the Y-axis represents mapped reads in RPM units. Placements of microarray probes for specific transcripts are indicated by rose-colored boxes with probe names below. There are a total of four microarray probes, some of which correspond to shared exons of the Ugta1 gene family (A_44_P432355, A_44_P402641) or to specific exons defining Ugt1a1 (A_44_P446578) and Ugt1a6 (A_44_P432358) isoforms. RefSeq transcripts and Cufflinks assembled transcripts are displayed under the RNA-Seq tracks. Exons are shown as light blue blocks or bands; introns are lines between exons; arrows at the end of each transcript indicate direction of transcription. Panel B. Bar graph shows mean fold changes (AFB1/Control) on the Y axis for the entire RNA-Seq transcript (blue), the microarray probe (red) and the isoform-specific RNA-Seq_exon (green). For some exons, there was no corresponding microarray probe − ‘No Probe’ (e.g. Ugt1a5, Ugta1a7c). Exon-specific, RNA-Seq ratios were labeled by exon number. Ugt1a-Common consists of four exons (common to all Ugt1a isoforms) for which two microarray probes exist. Exon-specific ratios from RNA-Seq reads were calculated for Exons 1, 2, 3 and 4. RNA-Seq exon-specific reads were measured to calculate AFB1/Control ratios for Ugt1a1, Utgt1a5, both exons of Ugt1a6, and Ugt1a7c.</p

Analysis for Differentially Expressed Genes (DEGs)^a.

a<p>Transcripts were grouped by various combinations of analysis into three columns: Total Assembled transcripts; RefSeq - transcripts matching or partially matching RefSeq genes; and Novel transcripts. In the first row, Total CuffCompare transcripts included all transcripts (57,076); RefSeq transcripts (complete or partial match) were 45,144 (14,257+30,887 = 45,144); and 11,932 potentially novel transcripts. Note, that a set of 1,496 Cufflinks assembled transcripts referred to as, ‘Others’, contained significant repeat sequences and so this small set of transcripts was excluded (e.g. –‘Others’) from Total Assembled transcripts. In row 2, to enable comparison with available Microarray data, we identified Cufflinks assembled transcripts that overlapped MA probes (total 44,469 transcripts). From this group of transcripts, we determined differential expression (DEGs) using student’s t-test in the case of MA (microarray) data in row 4, or for RNA-Seq data we performed DESeq analysis in row 3 or Cuffdiff analysis in row 5 (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0061768#s4" target="_blank">Materials and Methods</a> for details).</p

Reference-Guided Assembly of RNA-Seq Reads into Transcripts by Cufflinks^a.

a<p>Cufflinks was employed to assemble reads into transcripts from each animal sample and Cuffcompare was used to obtain a union of transcripts from all 8 animals as summarized in this panel. ‘Total Transcripts Assembled’ was the sum of Complete, Partial and Novel transcripts. ‘Complete Match’ transcripts were identical to RefSeq transcripts (intron chain matches RefSeq gene); transcripts in the ‘Partially Match’ category include transcripts that overlap RefSeq transcripts but do not completely match (these could include potentially novel isoforms of an existing RefSeq transcript); and potentially ‘Novel’ transcripts are those that did not overlap with any RefSeq transcripts at all and may therefore involve intergenic transcripts.</p

Test and validation of differential expression between qPCR, RNA-Seq and microarray by select transcripts.

<p>The mean fold changes for each group were compared in the bar chart for eight high to medium fold change transcripts and three low expression change transcripts (inset). qPCR data were normalized to β-actin expression for each sample. Means of significant (p≤0.05) fold changes from control were computed for qPCR, DESeq and microarray using RNA from the same 4 animals in each analysis.</p

Differing types of DEGs found by RNA-Seq and microarray data (Panels A–D).

<p>Numbers of reads in RPM (reads per million mapped reads) are shown on the Y-axis and the genomic region is displayed on the X-axis using a common NCBI scale for representative AFB1 (blue reads) and CTRL (black reads) sample tracks in UCSC browser format. Placements of microarray probes for specific transcripts are indicated by small rose-colored squares with probe names below. Numbered Cufflinks assembled transcripts and the corresponding RefSeq (gene abbreviation) or Ensembl annotated transcripts (ENSRNOT) identifiers are displayed under the RNA-Seq tracks. Exons are represented as blocks or bands; introns are lines between exons. Arrows at the end of assembled or annotated transcripts show the direction of transcription. Open arrows in specific panels point out a new Cufflinks identified exon. Bar graphs to the right show mean fold changes (AFB1/CTRL) for specific microarray probes and the corresponding RNA-Seq transcripts (DESeq). At the bottom of Panels A and C, the entire transcript (red) is presented for which a portion of the transcript which has been enlarged (blue bracket) for the RNASeq tracks shown above. In Panel D, only spliced EST’s (parallel lines in the center of spliced EST represent gaps in the alignment) were available which were without RefSeq or Ensembl transcript annotation and for which no microarray probe had been assigned. In Panel D bar graph, fold changes for two separate Cufflinks transcripts are indicated while no microarray data were available (no probe).</p