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In this study, the authors use measures of carbon-14 in neuronal DNA from human stroke patient cortical tissue samples to show that, unlike previous studies done in rodents, they do not find any evidence of increased neurogenesis after an ischemic injury. In addition, DNA damage assays suggest that there is no increase in DNA rearrangement after this insult. It has been unclear whether ischemic stroke induces neurogenesis or neuronal DNA rearrangements in the human neocortex. Using immunohistochemistry; transcriptome, genome and ploidy analyses; and determination of nuclear bomb test–derived 14 C concentration in neuronal DNA, we found neither to be the case. A large proportion of cortical neurons displayed DNA fragmentation and DNA repair a short time after stroke, whereas neurons at chronic stages after stroke showed DNA integrity, demonstrating the relevance of an intact genome for survival.

RNA sequencing has become widely used in gene expression profiling experiments. Prior to any RNA sequencing experiment the quality of the RNA must be measured to assess whether or not it can be used for further downstream analysis. The RNA integrity number (RIN) is a scale used to measure the quality of RNA that runs from 1 (completely degraded) to 10 (intact). Ideally, samples with high RIN (> 8) are used in RNA sequencing experiments. RNA, however, is a fragile molecule which is susceptible to degradation and obtaining high quality RNA is often hard, or even impossible when extracting RNA from certain clinical tissues. Thus, occasionally, working with low quality RNA is the only option the researcher has. Here we investigate the effects of RIN on RNA sequencing and suggest a computational method to handle data from samples with low quality RNA which also enables reanalysis of published datasets. Using RNA from a human cell line we generated and sequenced samples with varying RINs and illustrate what effect the RIN has on the basic procedure of RNA sequencing; both quality aspects and differential expression. We show that the RIN has systematic effects on gene coverage, false positives in differential expression and the quantification of duplicate reads. We introduce 3' tag counting (3TC) as a computational approach to reliably estimate differential expression for samples with low RIN. We show that using the 3TC method in differential expression analysis significantly reduces false positives when comparing samples with different RIN, while retaining reasonable sensitivity.

Background Strand specific RNA sequencing is rapidly replacing conventional cDNA sequencing as an approach for assessing information about the transcriptome. Alongside improved laboratory protocols the development of bioinformatical tools is steadily progressing. In the current procedure the Illumina TruSeq library preparation kit is used, along with additional reagents, to make stranded libraries in an automated fashion which are then sequenced on Illumina HiSeq 2000. By the use of freely available bioinformatical tools we show, through quality metrics, that the protocol is robust and reproducible. We further highlight the practicality of strand specific libraries by comparing expression of strand specific libraries to non-stranded libraries, by looking at known antisense transcription of pseudogenes and by identifying novel transcription. Furthermore, two ribosomal depletion kits, RiboMinus and RiboZero, are compared and two sequence aligners, Tophat2 and STAR, are also compared. Results The, non-stranded, Illumina TruSeq kit can be adapted to generate strand specific libraries and can be used to access detailed information on the transcriptome. The RiboZero kit is very effective in removing ribosomal RNA from total RNA and the STAR aligner produces high mapping yield in a short time. Strand specific data gives more detailed and correct results than does non-stranded data as we show when estimating expression values and in assembling transcripts. Even well annotated genomes need improvements and corrections which can be achieved using strand specific data. Conclusions Researchers in the field should strive to use strand specific data; it allows for more confidence in the data analysis and is less likely to lead to false conclusions. If faced with analysing non-stranded data, researchers should be well aware of the caveats of that approach.

Chemotherapy-induced myelosuppression, including thrombocytopenia, is a recurrent problem during cancer treatments that may require dose alterations or cessations that could affect the antitumor effect of the treatment. To identify genetic markers associated with treatment-induced thrombocytopenia, we whole-exome sequenced 215 non-small cell lung cancer patients homogeneously treated with gemcitabine/carboplatin. The decrease in platelets (defined as nadir/baseline) was used to assess treatment-induced thrombocytopenia. Association between germline genetic variants and thrombocytopenia was analyzed at single-nucleotide variant (SNV) (based on the optimal false discovery rate, the severity of predicted consequence, and effect), gene, and pathway levels. These analyses identified 130 SNVs/INDELs and 25 genes associated with thrombocytopenia (P-value < 0.002). Twenty-three SNVs were validated in an independent genome-wide association study (GWAS). The top associations include rs34491125 in JMJD1C (P-value = 9.07 × 10−5), the validated variants rs10491684 in DOCK8 (P-value = 1.95 × 10−4), rs6118 in SERPINA5 (P-value = 5.83 × 10−4), and rs5877 in SERPINC1 (P-value = 1.07 × 10−3), and the genes CAPZA2 (P-value = 4.03 × 10−4) and SERPINC1 (P-value = 1.55 × 10−3). The SNVs in the top-scoring pathway “Factors involved in megakaryocyte development and platelet production” (P-value = 3.34 × 10−4) were used to construct weighted genetic risk score (wGRS) and logistic regression models that predict thrombocytopenia. The wGRS predict which patients are at high or low toxicity risk levels, for CTCAE (odds ratio (OR) = 22.35, P-value = 1.55 × 10−8), and decrease (OR = 66.82, P-value = 5.92 × 10−9). The logistic regression models predict CTCAE grades 3–4 (receiver operator characteristics (ROC) area under the curve (AUC) = 0.79), and large decrease (ROC AUC = 0.86). We identified and validated genetic variations within hematopoiesis-related pathways that provide a solid foundation for future studies using genetic markers for predicting chemotherapy-induced thrombocytopenia and personalizing treatments.

Endometrial receptivity is crucial for implantation and establishment of a normal pregnancy. The shift from proliferative to receptive endometrium is still far from being understood. In this paper, we comprehensively present the transcriptome of the human endometrium by comparing endometrial biopsies from proliferative phase with consecutive biopsies 7–9 days after ovulation. The results show a clear difference in expression between the two time points using both total and small RNA sequencing. A total of 3,297 messenger RNAs (mRNAs), 516 long noncoding RNAs (lncRNAs), and 102 small noncoding RNAs were identified as statistically differentially expressed between the two time points. We show a thorough description of the change in mRNA between the two time points and display lncRNAs, small nucleolar RNAs, and small nuclear RNAs not previously reported in the healthy human endometrium. In conclusion, this paper reports in detail the shift in RNA expression from the proliferative to receptive endometrium. Summary Sentence Messenger RNA, sncRNA, and lncRNA show a clear difference in expression between proliferative phase and 7–9 days after ovulation, thoroughly described together with lncRNA, snoRNA, and snRNA not previously reported in healthy human endometrium.

Zebrafish (Danio rerio) are used extensively in sleep research; both to further understanding of sleep in general and also as a model of human sleep. To date, sleep studies have been performed in larval and adult zebrafish but no efforts have been made to document the ontogeny of zebrafish sleep-wake cycles. Because sleep differs across phylogeny and ontogeny it is important to validate the use of zebrafish in elucidating the neural substrates of sleep. Here we describe the development of sleep and wake across the zebrafish lifespan and how it compares to humans. We find power-law distributions to best fit wake bout data but demonstrate that exponential distributions, previously used to describe sleep bout distributions, fail to adequately account for the data in either species. Regardless, the data reveal remarkable similarities in the ontogeny of sleep cycles in zebrafish and humans. Moreover, as seen in other organisms, zebrafish sleep levels are highest early in ontogeny and sleep and wake bouts gradually consolidate to form the adult sleep pattern. Finally, sleep percentage, bout duration, bout number, and sleep fragmentation are shown to allow for meaningful comparisons between zebrafish and human sleep.

Abstract Endometrial receptivity is crucial for implantation and establishment of a normal pregnancy. The shift from proliferative to receptive endometrium is still far from being understood. In this paper, we comprehensively present the transcriptome of the human endometrium by comparing endometrial biopsies from proliferative phase with consecutive biopsies 7-9 days after ovulation. The results show a clear difference in expression between the two time points using both total and small RNA sequencing. A total of 3,297 messenger RNAs (mRNAs), 516 long noncoding RNAs (lncRNAs), and 102 small noncoding RNAs were identified as statistically differentially expressed between the two time points. We show a thorough description of the change in mRNA between the two time points and display lncRNAs, small nucleolar RNAs, and small nuclear RNAs not previously reported in the healthy human endometrium. In conclusion, this paper reports in detail the shift in RNA expression from the proliferative to receptive endometrium. Summary Sentence Messenger RNA, sncRNA, and lncRNA show a clear difference in expression between proliferative phase and 7-9 days after ovulation, thoroughly described together with lncRNA, snoRNA, and snRNA not previously reported in healthy human endometrium.

RNA sequencing has become widely used in gene expression profiling experiments. Prior to any RNA sequencing experiment the quality of the RNA must be measured to assess whether or not it can be used for further downstream analysis. The RNA integrity number (RIN) is a scale used to measure the quality of RNA that runs from 1 (completely degraded) to 10 (intact). Ideally, samples with high RIN (8) are used in RNA sequencing experiments. RNA, however, is a fragile molecule which is susceptible to degradation and obtaining high quality RNA is often hard, or even impossible when extracting RNA from certain clinical tissues. Thus, occasionally, working with low quality RNA is the only option the researcher has. Here we investigate the effects of RIN on RNA sequencing and suggest a computational method to handle data from samples with low quality RNA which also enables reanalysis of published datasets. Using RNA from a human cell line we generated and sequenced samples with varying RINs and illustrate what effect the RIN has on the basic procedure of RNA sequencing; both quality aspects and differential expression. We show that the RIN has systematic effects on gene coverage, false positives in differential expression and the quantification of duplicate reads. We introduce 3' tag counting (3TC) as a computational approach to reliably estimate differential expression for samples with low RIN. We show that using the 3TC method in differential expression analysis significantly reduces false positives when comparing samples with different RIN, while retaining reasonable sensitivity.