A genome-wide survey of sRNAs in the symbiotic nitrogen-fixing alpha-proteobacterium Sinorhizobium meliloti

Schlüter J-P, Reinkensmeier J, Daschkey S, et al. A genome-wide survey of sRNAs in the symbiotic nitrogen-fixing alpha-proteobacterium Sinorhizobium meliloti. BMC Genomics. 2010;11(1): 245.BACKGROUND: Small untranslated RNAs (sRNAs) are widespread regulators of gene expression in bacteria. This study reports on a comprehensive screen for sRNAs in the symbiotic nitrogen-fixing alpha-proteobacterium Sinorhizobium meliloti applying deep sequencing of cDNAs and microarray hybridizations. RESULTS: A total of 1,125 sRNA candidates that were classified as trans-encoded sRNAs (173), cis-encoded antisense sRNAs (117), mRNA leader transcripts (379), and sense sRNAs overlapping coding regions (456) were identified in a size range of 50 to 348 nucleotides. Among these were transcripts corresponding to 82 previously reported sRNA candidates. Enrichment for RNAs with primary 5'-ends prior to sequencing of cDNAs suggested transcriptional start sites corresponding to 466 predicted sRNA regions. The consensus sigma70 promoter motif CTTGAC-N17-CTATAT was found upstream of 101 sRNA candidates. Expression patterns derived from microarray hybridizations provided further information on conditions of expression of a number of sRNA candidates. Furthermore, GenBank, EMBL, DDBJ, PDB, and Rfam databases were searched for homologs of the sRNA candidates identified in this study. Searching Rfam family models with over 1,000 sRNA candidates, re-discovered only those sequences from S. meliloti already known and stored in Rfam, whereas BLAST searches suggested a number of homologs in related alpha-proteobacteria. CONCLUSIONS: The screening data suggests that in S. meliloti about 3% of the genes encode trans-encoded sRNAs and about 2% antisense transcripts. Thus, this first comprehensive screen for sRNAs applying deep sequencing in an alpha-proteobacterium shows that sRNAs also occur in high number in this group of bacteria

PEDIA: prioritization of exome data by image analysis.

PURPOSE: Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. METHODS: Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. RESULTS: The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20-89% and the top 10 accuracy rate by more than 5-99% for the disease-causing gene. CONCLUSION: Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis

MicroRNAs Distinguish Cytogenetic Subgroups in Pediatric AML and Contribute to Complex Regulatory Networks in AML-Relevant Pathways

<div><h3>Background</h3><p>The role of microRNAs (miRNAs), important post-transcriptional regulators, in the pathogenesis of acute myeloid leukemia (AML) is just emerging and has been mainly studied in adults. First studies in children investigate single selected miRNAs, however, a comprehensive overview of miRNA expression and function in children and young adults is missing so far.</p> <h3>Methodology/Principal Findings</h3><p>We here globally identified differentially expressed miRNAs between AML subtypes in a survey of 102 children and adolescent. Pediatric samples with core-binding factor AML and promyelocytic leukemia could be distinguished from each other and from MLL-rearranged AML subtypes by differentially expressed miRNAs including miR-126, -146a, -181a/b, -100, and miR-125b. Subsequently, we established a newly devised immunoprecipitation assay followed by rapid microarray detection for the isolation of Argonaute proteins, the hallmark of miRNA targeting complexes, from cell line models resembling core-binding factor and promyelocytic leukemia. Applying this method, we were able to identify Ago-associated miRNAs and their targeted mRNAs.</p> <h3>Conclusions/Significance</h3><p>miRNAs as well as their mRNA-targets showed binding preferences for the different Argonaute proteins in a cell context-dependent manner. Bioinformatically-derived pathway analysis suggested a concerted action of all four Argonaute complexes in the regulation of AML-relevant pathways. For the first time, to our knowledge, a complete AML data set resulting from carefully devised biochemical isolation experiments and analysis of Ago-associated miRNAs and their target-mRNAs is now available.</p> </div

Clinical and cytogenetic characteristics of AML patients.

<p>Clinical and cytogenetic characteristics of AML patients.</p

Ago-associated miRNAs and - mRNAs using the PAR-CLIP-Array method.

<p>(A) Western Blot analysis of immunoprecipitates of human Ago1-4 from AML cell lines, KASUMI-1 with t(8;21) and NB4 carrying t(15;17). The immunoprecipitates show a specific band of the Argonaute protein (∼97 kDa; ←) in contrast to the isotype control antibody (rat IgG2a) and empty-bead control. A representative sample of the biological triplicate is shown. Please note that more material was loaded for Ago3 and Ago4 since these two Ago proteins are much lower expressed as was also validated by qRT-PCR (not shown). Antibodies were tested for specificity for detection of native and denatured protein prior to this experiment with cell lines overexpressing tagged Ago protein (not shown) (B) Validation of miRNA- and mRNA-enrichment in immunoprecipitation experiments. Argonaute proteins (black bar) are compared to the isotype (white bar) and empty bead controls (grey bar) using TaqMan qRT-PCR assays for microRNA- (upper panel) and SYBR Green qRT-PCR assays for mRNA-quantification (lower panel). Shown are the measured levels (2^−CT) of six and five miRNAs of KASUMI-1 cells (upper left panel) and NB4 cells (upper right panel), respectively. Immunoprecipitation experiments as well as cDNA synthesis were each done in triplicates and the mean value of the nine values as well as one standard deviation is depicted. miRNAs differentially expressed in patient samples between the t(8;21) and t(15;17) were selected together with ubiquitously expressed miR-16. Please note that calculation of ΔC_T-values is not possible due to the lack of a housekeeping gene bound to Argonaute proteins. Six Ago-associated mRNAs in the KASUMI-1 cells (lower left panel) and NB4 cells (lower right panel), covering the whole range from low to high enrichment over isotype control according to microarray data, were selected for qRT-PCR validation. Graphs are centered around a C_T-value of 29.9 cycles (2^−CT = 0⁻⁹).</p

Schematic overview over the PAR-CLIP-Array method.

<p>Schematic overview over the PAR-CLIP-Array method.</p

miRNAs expression changes in translocation t(8;21)- and t(15;17)-positive as well as MLL- rearranged pediatric AML patients.

<p>miRNAs distinguishing the two translocations from themselves or all other cytogenetic subtypes as well as miRNAs differentially expressed in MLL-rearranged samples compared to all other cytogenetic subtypes. Shown are those with at least 1.8-fold expression change (as indicated by the arrows) in a minimum of one comparison and fold changes of the comparisons are given. Mann-Whitney-U (MWU) statistical testing was performed for this comparison as indicated. Levels: <i>p</i><0.05 = *; <i>p</i><0.01 = **. miRNAs defined as class identifiers for the three cytogenetic aberrations using either patient samples of all cytogenetic subtypes (^#) or the t(8;21)-, t(15;17)- or MLL-rearranged subtypes alone (^§) using PAM are indicated as well.</p

miRNA expression in pediatric and adult AML patient samples and healthy controls.

<p>(A) miRNA expression profiles of 102 pediatric AML patient samples of different cytogenetic aberrations as color-coded above the heat-map, six adult samples (•) and two CD34+ cell fraction from healthy donors were generated by a quantitative microarray approach. The heatmap represents miRNAs detected in at least 3 samples, while all others were summarized in “all others”. Background corrected signal intensities were corrected for each miRNA using 1 fmol of a universal reference consisting of synthetic ribooligonucleotides corresponding to 493 known human miRNAs. Unsupervised clustering of samples based upon the expression profiles of all miRNAs, indicated above the heatmap, reveals four distinct clusters with pediatric AML samples with translocation t(8;21) and t(15;17) clustering together and separate from each other. In contrast, adult AML samples cluster closely together irrespective of t(8;21) and t(15;17). (B) Validation of seven miRNAs most differentially expressed between t(8;21) AML samples and all other cytogenetic subgroups and t(15;17) APL samples and all other cytogenetic subgroups using TaqMan qRT-PCR. Expression values for t(8;21)-positive, t(15;17)-positive samples as well as cytogenetic subtypes other then the aforementioned ones are depicted. Please note that the selected miRNAs are also differentially expressed between these two cytogenetic subtypes. 22 patient samples with translocation t(8;21), 12 patient samples with translocation t(15;17) and 24 patient samples of all other cytogenetic subgroups were randomly chosen for validation. Indicated are the ΔC_T-values relative to U6 snoRNA loading control. Expression differences were statistically analyzed using a Student's t-test as indicated; <i>p</i><0.05 = *; <i>p</i><0.01 = **; <i>p</i><0.001 = ***.</p