Biology subjects

Schwartz, S.

Publications and source records attributed to Schwartz, S..

6 recordsLinked to original sources

bioRxiv2018

Long non-coding RNAs defining major subtypes of B cell precursor acute lymphoblastic leukemia

Recent studies implicated that long non-coding RNAs (lncRNAs) may play a role in the progression and development of acute lymphoblastic leukemia, however, this role is not yet clear. In order to unravel the role of lncRNAs associated with B-cell precursor Acute Lymphoblastic Leukemia (BCP-ALL) subtypes, we performed transcriptome sequencing and DNA methylation array across 82 BCP-ALL samples from three molecular subtypes (DUX4, Ph-like, and Near Haploid or High Hyperdiploidy). Unsupervised clustering of BCP-ALL samples on the basis of their lncRNAs on transcriptome and DNA methylation profiles revealed robust clusters separating three molecular subtypes. Using extensive computational analysis, we developed a comprehensive catalog of 1235 aberrantly dysregulated BCP-ALL subtype-specific lncRNAs with altered expression and methylation patterns from three subtypes of BCP-ALL. By analyzing the co-expression of subtype-specific lncRNAs and protein-coding genes, we inferred key molecular processes in BCP-ALL subtypes. A strong correlation was identified between the DUX4 specific lncRNAs and activation of TGF-{beta} and Hippo signaling pathways. Similarly, Ph-like specific lncRNAs were correlated with genes involved in activation of PI3K-AKT, mTOR, and JAK-STAT signaling pathways. Interestingly, the relapse-specific differentially expressed lncRNAs correlated with the activation of metabolic and signaling pathways. Finally, we showed a set of epigenetically altered lncRNAs facilitating the expression of tumor genes located at their cis location. Overall, our study provides a comprehensive set of novel subtype and relapse-specific lncRNAs in BCP-ALL. Our findings suggest a wide range of molecular pathways are associated with lncRNAs in BCP-ALL subtypes and provide a foundation for functional investigations that could lead to new therapeutic approaches.\n\nAuthor SummaryAcute lymphoblastic leukemia is a heterogeneous blood cancer, with multiple molecular subtypes, and with high relapse rate. We are far from the complete understanding of the rationale behind these subtypes and high relapse rate. Long non-coding (lncRNAs) has emerged as a novel class of RNA due to its diverse mechanism in cancer development and progression. LncRNAs does not code for proteins and represent around 70% of human transcripts. Recently, there are a number of studies used lncRNAs expression profile in the classification of various cancers subtypes and displayed their correlation with genomic, epigenetic, pathological and clinical features in diverse cancers. Therefore, lncRNAs can account for heterogeneity and has independent prognostic value in various cancer subtypes. However, lncRNAs defining the molecular subtypes of BCP-ALL are not portrayed yet. Here, we describe a set of relapse and subtype-specific lncRNAs from three major BCP-ALL subtypes and define their potential functions and epigenetic regulation. Our data uncover the diverse mechanism of action of lncRNAs in BCP-ALL subtypes defining how lncRNAs are involved in the pathogenesis of disease and the relevance in the stratification of BCP-ALL subtypes.

cancer biology↗

bioRxiv2018

M1A within cytoplasmic mRNAs at single nucleotide resolution: A reconciled transcriptome-wide map

Following synthesis, RNA can be modified with over 100 chemically distinct modifications, and in recent years it was shown that processing, localization, stability and translation of mRNAs can be impacted by an increasing number of these modifications. A modification that recently gained attention is N1-methyladenosine (m1A), which is present across all three domains of life. Recently, two studies - one of them ours - developed conceptually similar approaches to map m1A in a transcriptome-wide manner and at single nucleotide resolution. Surprisingly, the two studies diverged quite substantially in terms of their estimates of the abundance, whereabouts and stoichiometry of this modification within internal sites in cytosolic mRNAs: One study reported it to be a very rare modification, present at very low stoichiometries, and invariably catalyzed by TRMT6/61A. The other found it to be present at >470 sites, in dozens of which at relatively high levels, and in the vast majority of cases these sites were highly unlikely to be substrates of TRMT6/61A, suggesting that additional methyltransferases are active on cytosolic mRNAs. Here we aim to reconcile the contradictions between the two studies, primarily by reanalyzing and re-annotating the set of sites identified in the latter study. We find that the vast majority of sites detected in this study originate from duplications, misannotations, mismapping, SNPs, sequencing errors, and a set of sites originating from the very first transcribed base ( TSS sites). We raise concerns as to whether the TSS sites truly reflect m1A originating from the first transcribed base. We find that only 53 of the sites detected in this study likely reflect bona-fide internal modifications of cytoplasmically encoded mRNA molecules. The vast majority of these are likely to be TRMT6/TRMT61A substrates, and are typically modified at low to undetectable levels. We conclude that within cytosolic mRNAs, m1A is a rare internal modification where it is typically catalyzed at ultra-low stoichiometries via TRMT6/TRMT61A. Our findings offer a clear and consistent view on the abundance and whereabouts of this modification, and lays out key directions for future studies in the field.

molecular biology↗

bioRxiv2018

Imposing a curfew on the use of screen electronic devices improves sleep and daytime vigilance in adolescents.

The use of screen electronic devices (SED) in the evening negatively affects sleep. Yet, sleep is known to be essential for brain maturation and a key factor for good academic performance, and thus is particularly critical during childhood and adolescence. While previous studies reported correlations between SED use and sleep impairments, the causal relationship between SED use and sleep in adolescents remains unclear. Using actigraphy and daily questionnaires in a large sample of students (12 to 19 years old), we assessed SED use and sleep habits over one month, including a two-week baseline phase and a two-week interventional phase, where participants were asked to stop screen use after 9 pm during pre-school nights. During the interventional phase, we found that reduction in time spent on SED after 9 pm correlated with earlier sleep onset time and increased total sleep duration. The latter led to improved daytime vigilance. We also observed that the beneficial impact of the intervention on sleep was influenced by catechol-O-methyltransferase gene (COMT) Val158Met polymorphism, which is implicated in the dopaminergic modulation of human behaviors, including wake and sleep regulation. These findings provide evidence that restricting SED use in the evening represents a valid and promising approach for improving sleep duration in adolescents, with potential implications for daytime functioning and health.\n\nSTATEMENT OF SIGNIFICANCEWith the emergence of smartphones and other connected devices, adolescents spend a lot of time on screen electronic devices (SED), especially during the evening. We report that time spent on SED after 9 pm negatively correlates with sleep onset time, sleep duration as well as mood, body weight, and academic performance. Such observable correlations urge for educational strategies to address the chronic lack of sleep observed in todays adolescent populations. Here we also show that limiting the use of SED after 9 pm improves sleep duration and daytime vigilance in most adolescents. This simple education recommendation pertaining to sleep hygiene can be implemented by every household, yielding direct positive effects on sleep, and presumed benefits for health and daytime functioning.

neuroscience↗

bioRxiv2017

Detecting pathogen exposure during the non-symptomatic incubation period using physiological data

Early pathogen exposure detection allows better patient care and faster implementation of public health measures (patient isolation, contact tracing). Existing exposure detection most frequently relies on overt clinical symptoms, namely fever, during the infectious prodromal period. We have developed a robust machine learning based method to better detect asymptomatic states during the incubation period using subtle, sub-clinical physiological markers. Starting with high-resolution physiological waveform data from non-human primate studies of viral (Ebola, Marburg, Lassa, and Nipah viruses) and bacterial (Y. pestis) exposure, we processed the data to reduce short-term variability and normalize diurnal variations, then provided these to a supervised random forest classification algorithm and post-classifier declaration logic step to reduce false alarms. In most subjects detection is achieved well before the onset of fever; subject cross-validation across exposure studies (varying viruses, exposure routes, animal species, and target dose) lead to 51h mean early detection (at 0.93 area under the receiver-operating characteristic curve [AUCROC]). Evaluating the algorithm against entirely independent datasets for Lassa, Nipah, and Y. pestis exposures un-used in algorithm training and development yields a mean 51h early warning time (at AUCROC=0.95). We discuss which physiological indicators are most informative for early detection and options for extending this capability to limited datasets such as those available from wearable, non-invasive, ECG-based sensors.

physiology↗

bioRxiv2017

Acute physical exercise improves memory consolidation in humans via BDNF and endocannabinoid signaling

Regular physical exercise enhances memory functions and neurogenesis in the hippocampus, an effect partially mediated by BDNF (Brain Derived Neurotrophic Factor). Acute exercise promotes the release of endocannabinoids (especially anandamide, AEA), which enhance BDNF release and improve hippocampal plasticity in rodents. How acute exercise affects BDNF and AEA levels and influences memory performance in humans remains to date unknown. Here we combined blood biomarkers, behavioral and fMRI measurements to assess the impact of acute physical exercise on associative memory and underlying neurophysiological mechanisms. For each participant, memory was tested after three conditions: rest, moderate or high exercise intensity. A long-term memory retest took place 3 months later. At both test and retest, memory performance increased after moderate but not high intensity exercise or rest. We also show that memory benefited from exercise-related increases in both AEA and BNDF levels: AEA boosted hippocampal activity during memory recall, while BDNF enhanced hippocampal memory representations and long-term performance.

neuroscience↗

bioRxiv2017

Fast P(RMNE): Fast Forensic DNA Probability of Random Man Not Excluded Calculation

High throughput sequencing (HTS) of DNA forensic samples is expanding from the sizing of short tandem repeats (STRs) to massively parallel sequencing (MPS). HTS panels are expanding from the FBI 20 core Combined DNA Index System (CODIS) loci to include SNPs. The calculation of random man not excluded, P(RMNE), is used in DNA mixture analysis to estimate the probability that a person is present in a DNA mixture. This calculation encounters calculation artifacts with expansion to larger panel sizes. Increasing the floating-point precision of the calculations allows for increased panel sizes but with a corresponding increase in computation time. The Taylor series higher precision libraries used fail on some input data sets leading to algorithm unreliability. Herein, a new formula is introduced for calculating P(RMNE) that scales to larger SNP panel sizes while being computationally efficient (patent pending)[1].

bioinformatics↗