Biology subjects

Robinson, M.

Publications and source records attributed to Robinson, M..

At least 37 records · Page 2Linked to original sources

bioRxiv2022

Filter inference: A scalable nonlinear mixed effects inference approach for snapshot time series data

Variability is an intrinsic property of biological systems and is often at the heart of their complex behaviour. Examples range from cell-to-cell variability in cell signalling pathways to variability in the response to treatment across patients. A popular approach to model and understand this variability is nonlinear mixed effects (NLME) modelling. However, estimating the parameters of NLME models from measurements quickly becomes computationally expensive as the number of measured individuals grows, making NLME inference intractable for datasets with thousands of measured individuals. This shortcoming is particularly limiting for snapshot datasets, common e.g. in cell biology, where high-throughput measurement techniques provide large numbers of single cell measurements. We extend earlier work by Hasenauer et al (2011) to introduce a novel approach for the estimation of NLME model parameters from snapshot measurements, which we call filter inference. Filter inference is a new variant of approximate Bayesian computation, with dominant computational costs that do not increase with the number of measured individuals, making efficient inferences from snapshot measurements possible. Filter inference also scales well with the number of model parameters, using state-of-the-art gradient-based MCMC algorithms, such as the No-U-Turn Sampler (NUTS). We demonstrate the properties of filter inference using examples from early cancer growth modelling and from epidermal growth factor signalling pathway modelling. Author summaryNonlinear mixed effects (NLME) models are widely used to model differences between individuals in a population. In pharmacology, for example, they are used to model the treatment response variability across patients, and in cell biology they are used to model the cell-to-cell variability in cell signalling pathways. However, NLME models introduce parameters, which typically need to be estimated from data. This estimation becomes computationally intractable when the number of measured individuals - be they patients or cells - is too large. But, the more individuals are measured in a population, the better the variability can be understood. This is especially true when individuals are measured only once. Such snapshot measurements are particularly common in cell biology, where high-throughput measurement techniques provide large numbers of single cell measurements. In clinical pharmacology, datasets consisting of many snapshot measurements are less common but are easier and cheaper to obtain than detailed time series measurements across patients. Our approach can be used to estimate the parameters of NLME models from snapshot time series data with thousands of measured individuals.

bioinformatics↗

bioRxiv2022

scTagger: Fast and accurate matching of cellular barcodes across short- and long-reads of single-cell RNA-seq experiments

Single-cell RNA sequencing allows for characterizing the gene expression landscape at the cell type level. However, because of its use of short-reads, it is severely limited at detecting full-length features of transcripts such as alternative splicing. New library preparation techniques attempt to extend single-cell sequencing by utilizing both long-and short-reads. These techniques split the library material, after it is tagged with cellular barcodes, into two pools: one for short-read sequencing and one for long-read sequencing. However, the challenge of utilizing these techniques is that they require matching the cellular barcodes sequenced by the erroneous long-reads to the cellular barcodes detected by the short-reads. To overcome this challenge, we introduce scTagger, a computational method to match cellular barcodes data from long-and short-reads. We tested scTagger against another state-of-the-art tool on both real and simulated datasets and we demonstrate that scTagger has both significantly better accuracy and time efficiency.

bioinformatics↗

bioRxiv2022

CTLA-4 regulates PD-L1-PD-1 interactions via transendocytosis of CD80

CTLA-4 and PD-1 are key immune checkpoints that are targeted in the treatment of cancer. Recently it has emerged that there is a physical interaction between the ligands of these pathways (CD80 and PD-L1), which can prevent PD-L1 inhibitory functions. Since CTLA-4 captures and degrades its ligands via transendocytosis, we investigated how transendocytosis of CD80 is impacted by PD-L1 interaction. We find that transendocytosis of CD80 results in a time-dependent recovery of PD-L1 availability that correlates with CD80 removal. Moreover, CD80 transendocytosis is highly specific in that only CD80 is removed, and its heterodimeric PD-L1 partner remains on the APC. We found no evidence that CTLA-4 interactions with CD80 were inhibited by PD-L1, however efficient removal of CD80 required an intact CTLA-4 cytoplasmic domain, distinguishing this process from more general trogocytosis. We also show that simple binding of CTLA-4 to the CD80-PD-L1 heterodimer is insufficient to liberate PD-L1-PD-1 interactions, suggesting that transendocytosis of CD80 is required to effectively control PD-L1-PD-1 interactions.

immunology↗

bioRxiv2022

Treatment response prediction: Is model selection unreliable?

Quantitative modelling has become an essential part of the drug development pipeline. In particular, pharmacokinetic and pharmacodynamic models are used to predict treatment responses in order to optimise clinical trials and assess the safety and efficacy of dosing regimens across patients. It is therefore crucial that treatment response predictions are reliable. However, the data available to fit models are often limited, which can leave considerable uncertainty about the best model to use. Common practice is to select the model that is most consistent with the observed data based on the Akaike information criterion (AIC). Another popular approach is to average the predictions across the subset of models consistent with the data. In this article, we argue that both approaches can lead to unreliable predictions, as treatment responses typically display nonlinear dynamics, so models can be consistent with the observed dynamics, whilst predicting incorrect treatment responses. This is especially the case when predicting treatment responses for either times or dosing regimens that go beyond the observed dynamics. Across a range of experiments on both real laboratory data and synthetically derived data on Neisseria gonorrhoeae response to ciprofloxacin, we show that probabilistic averaging of models results in more reliable treatment response predictions.

systems biology↗

bioRxiv2021

Deep Learning-based Automated Rare Sperm Identification from Testes Biopsies

Non-obstructive azoospermia (NOA), the most severe form of male infertility, is currently treated using microsurgical sperm extraction (microTESE) to retrieve sperm cells for in vitro fertilization via intracytoplasmic sperm injection (IVF-ICSI). The success rate of this procedure for NOA patients is currently limited by the ability of andrologists to identify a few rare sperm cells among millions of background testis cells. To improve this success rate, we developed a convolution neural network (CNN) to detect rare sperm from low-resolution microscopy images of microTESE samples. Our CNN uses the U-Net architecture to perform pixel-based classification on image patches from brightfield microscopy, which is followed by morphological analysis to detect individual sperm instances. This CNN is trained using microscopy images of fluorescently labeled sperm, which is fixed to eliminate their motility, and doped into testis biopsies obtained from NOA patients. We initially tested this algorithm using purified sperm samples at different imaging magnifications in order to determine the upper bounds of performance. We then tested this algorithm by doping rare sperm cells into testis biopsy samples from NOA patients and found a sperm detection F1 score of 85.2%. These results demonstrate the potential to use automated microscopy to dramatically increase the amount of testis biopsy tissue that could be comprehensively examined, which greatly increases the chance of finding rare viable sperm, and thereby increases the success rates of IVF-ICSI for couples with NOA.

bioengineering↗

bioRxiv2021

Metabotropic group II glutamate receptors mediate cue-triggered increases in incentive motivation for reward

RationaleReward-associated cues can acquire incentive motivational properties and invigorate reward-seeking actions via Pavlovian-to-instrumental transfer (PIT). Glutamatergic neurotransmission mediates the appetitive effects of reward-associated cues. We characterized the expression of PIT and its mediation by metabotropic group II glutamate (mGlu2/3) receptor activity in female and male rats. ObjectivesAcross the sexes, we used PIT procedures to determine i) cue-triggered increases in incentive motivation for water reward (Experiment 1), ii) the respective influences of the mGlu2/3 receptor agonist LY379268 and reward devaluation by satiation on this effect (Experiment 2). MethodsWater-restricted male and female Sprague-Dawley rats learned to lever press for water. Separately, they learned that one of two auditory stimuli predicts free water (CS+ vs CS-). On PIT test days, the CS+ and CS- were presented non-contingently, and we measured effects on lever pressing under extinction (no water). In Experiment 1, we characterized PIT across the sexes. In Experiment 2, we measured PIT after systemic LY379268 administration (0, 0.3 and 1 mg/kg), and water satiation, respectively. ResultsFemale and male rats showed similar PIT, with CS+ but not CS- presentations potentiating water-seeking behaviour. LY379268 (1 mg/kg) attenuated CS+ evoked increases in both water-associated lever pressing and conditioned approach to the water port. Reward devaluation attenuated both water-seeking and CS+ evoked conditioned approach behaviour. ConclusionsThe sexes show similar cue-triggered increases in reward wanting, and water devaluation suppresses both water seeking and cue-triggered anticipation of water reward. Finally, across the sexes, mGlu2/3 receptor activity mediates cue-triggered increases in reward wanting.

pharmacology and toxicology↗

bioRxiv2021

Microglia show differential transcriptomic response to Aβ peptide aggregates ex vivo and in vivo

Aggregation and accumulation of amyloid-{beta} (A{beta}) is a defining feature of Alzheimers disease (AD) pathology. To study microglial responses to A{beta}, we applied exogenous A{beta} peptide, in either oligomeric or fibrillar conformation, to primary mouse microglial cultures and evaluated system level transcriptional changes and then compared these to transcriptomic changes in the brains of CRND8 APP mice. We find that primary microglial cultures have rapid and massive transcriptional change to in response to A{beta}. Transcriptomic responses to oligomeric or fibrillar A{beta} in primary microglia, though partially overlapping, are distinct and are not recapitulated in vivo where A{beta} progressively accumulates. Furthermore, though classic immune mediators show massive transcriptional changes in the primary microglial cultures, these changes are not observed in the mouse model. Together, these data extend previous studies which demonstrate that microglia responses ex vivo are poor proxies for in vivo responses. Finally, these data demonstrate the potential utility of using microglia as biosensors of different aggregate conformation, as the transcriptional responses to oligomeric and fibrillar A{beta} can be distinguished.

neuroscience↗

bioRxiv2020

censcyt: censored covariates in differential abundance analysis in cytometry

Innovations in single cell technologies have lead to a flurry of datasets and computational tools to process and interpret them, including analyses of cell composition changes and transition in cell states. The diffcyt workflow for differential discovery in cytometry data consist of several steps, including preprocessing, cell population identification and differential testing for an association with a binary or continuous covariate. However, the commonly measured quantity of survival time in clinical studies often results in a censored covariate where classical differential testing is inapplicable. To overcome this limitation, multiple methods to directly include censored covariates in differential abundance analysis were examined with the use of simulation studies and a case study. Results show high error control and decent sensitivity for a subset of the methods. The tested methods are implemented in the R package censcyt as an extension of diffcyt and are available at https://github.com/retogerber/censcyt. Methods for the direct inclusion of a censored variable as a predictor in GLMMs are a valid alternative to classical survival analysis methods, such as the Cox proportional hazard model, while allowing for more flexibility in the differential analysis.

bioinformatics↗

bioRxiv2020

New water and air pollution sensors added to the Sonic Kayak citizen science system for low cost environmental mapping

Sonic Kayaks are low-cost open-source systems for gathering fine-scale environmental data. The system is designed to be simple to fit on to standard kayaks or canoes, and full instructions have been made available for anyone wishing to build their own. The first version included temperature sensors and a hydrophone for recording underwater sound. Here we outline the design and integration of two new sensors, for underwater turbidity and above water air particulate pollution. All sensors record continually, and the GPS location, time and date are also stored for every data point, allowing fine-scale environmental data mapping. The data being collected by the sensors is sonified (turned into sound) in real-time, allowing the paddler to hear the data as they are collecting it, making it possible to locate and follow interesting occurrences. We present proof-of principle data for the first time for all the sensors, demonstrating how the system can be used for environmental mapping, and discuss potential future applications and adaptations. We believe the Sonic Kayak system offers particular promise for citizen science and environmental activism, as well as allowing professional researchers to gather data that was previously difficult or impossible to obtain.

ecology↗

bioRxiv2020

Characterization of SOX2, OCT4 and NANOG in ovarian cancer tumor-initiating cells

Identification of tumor initiating cells (TICs) has traditionally relied on expression of surface markers such as CD133, CD44, and CD117 and enzymes such as aldehyde dehydrogenase (ALDH). Unfortunately, these markers are often cell type specific and not reproducible across patient samples. A more reliable indication of TICs may include elevated expression of stem cell transcription factors such as SOX2, OCT4, and NANOG that function to support long-term self-renewal, multipotency, and quiescence. RNA-sequencing studies presented here highlight a potential role for SOX2 in cell cycle progression in cells grown as 3-D spheroids, which are more tumorigenic and contain higher numbers of TICs than their 2-D monolayer cultured counterparts. SOX2, OCT4, and NANOG have not been comprehensively evaluated in ovarian cancer cell lines, although their expression is often associated with tumorigenic cells. We hypothesize that SOX2, OCT4, and NANOG will be enriched in ovarian TICs and will correlate with chemotherapy resistance, tumor initiation, and expression of traditional TIC markers. To investigate this hypothesis, we evaluated SOX2, OCT4, and NANOG in a panel of eight ovarian cancer cell lines grown as a monolayer in standard 2-D culture or as spheroids in TIC-enriching 3-D culture. Our data show that the high-grade serous ovarian cancer (HGSOC) lines CAOV3, CAOV4, OVCAR4, and OVCAR8 had longer doubling-times, greater resistance to chemotherapies, and significantly increased expression of SOX2, OCT4, and NANOG in TIC-enriching 3-D culture conditions. We also found that in vitro chemotherapy treatment enriches for cells with significantly higher expression of SOX2. We further show that the traditional TIC marker, CD117 identifies ovarian cancer cells with enhanced SOX2, OCT4, and NANOG expression. Tumor-initiation studies and analysis of The Cancer Genome Atlas (TCGA) suggest a stronger role for SOX2 in ovarian cancer relapse compared with OCT4 or NANOG. Overall, our study clarifies the expression of SOX2, OCT4, and NANOG in TICs from a variety of ovarian cancer cell lines. Our findings suggest that SOX2 expression is a stronger indicator of ovarian TICs with enhanced tumor-initiation capacity and potential for relapse. Improved identification of ovarian TICs will advance our understanding of TIC biology and facilitate the design of better therapies to eliminate TICs and overcome chemotherapy resistance and disease relapse.

cancer biology↗

bioRxiv2020

ReSeq simulates realistic Illumina high-throughput sequencing data

In high-throughput sequencing data, performance comparisons between computational tools are essential for making informed decisions in the data processing from raw data to the scientific result. Simulations are a critical part of method comparisons, but for standard Illumina sequencing of genomic DNA, they are often oversimplified, which leads to optimistic results for most tools. ReSeq improves the authenticity of synthetic data by extracting and reproducing key components from real data. Major advancements are the inclusion of systematic errors, a fragment-based coverage model and sampling-matrix estimates based on two-dimensional margins. These improvements lead to a better representation of the original k-mer spectrum and more faithful performance evaluations. ReSeq and all of its code are available at: https://github.com/schmeing/ReSeq

genomics↗

bioRxiv2020

ARPEGGIO: Automated Reproducible Polyploid EpiGenetic GuIdance workflOw

Whole genome duplication (WGD) events are common in the evolutionary history of many living organisms. For decades, researchers have been trying to understand the genetic and epigenetic impact of WGD and its underlying molecular mechanisms. Particular attention was given to allopolyploid study systems, species resulting from an hybridization event accompanied by WGD. Investigating the mechanisms behind the survival of a newly formed allopolyploid highlighted the key role of DNA methylation. With the improvement of high-throughput methods, such as whole genome bisulfite sequencing (WGBS), an opportunity opened to further understand the role of DNA methylation at a larger scale and higher resolution. However, only a few studies have applied WGBS to allopolyploids, which might be due to lack of genomic resources combined with a burdensome data analysis process. To overcome these problems, we developed the Automated Reproducible Polyploid EpiGenetic GuIdance workflOw (ARPEGGIO): the first workflow for the analysis of epigenetic data in polyploids. This workflow analyzes WGBS data from allopolyploid species via the genome assemblies of the allopolyploids parent species. ARPEGGIO utilizes an updated read classification algorithm (EAGLE-RC), to tackle the challenge of sequence similarity amongst parental genomes. ARPEGGIO offers automation, but more importantly, a complete set of analyses including spot checks starting from raw WGBS data: quality checks, trimming, alignment, methylation extraction, statistical analyses and downstream analyses. A full run of ARPEGGIO outputs a list of genes showing differential methylation. ARPEGGIOs design focuses on ease of use and reproducibility. ARPEGGIO was made simple to set up, run and interpret, and its implementation includes both package management and containerization. Here we discuss all the steps, challenges and implementation strategies; example datasets are provided to show how to use ARPEGGIO. In addition, we also test EAGLE-RC with publicly available datasets given a ground truth, and we show that EAGLE-RC decreases the error rate by 3 to 4 times compared to standard approaches. The goal of ARPEGGIO is to promote, support and improve polyploid research with a reproducible and automated set of analyses in a convenient implementation.

bioinformatics↗

bioRxiv2020

Highly multiplexed oligonucleotide probe-ligation testing enables efficient extraction-free SARS-CoV-2 detection and viral genotyping

The emergence of SARS-CoV-2 has caused the current COVID-19 pandemic with catastrophic societal impact. Because many individuals shed virus for days before symptom onset, and many show mild or no symptoms, an emergent and unprecedented need exists for development and deployment of sensitive and high throughput molecular diagnostic tests. RNA-mediated oligonucleotide Annealing Selection and Ligation with next generation DNA sequencing (RASL-seq) is a highly multiplexed technology for targeted analysis of polyadenylated mRNA, which incorporates sample barcoding for massively parallel analyses. Here we present a more generalized method, capture RASL-seq (“cRASL-seq”), which enables analysis of any targeted pathogen-(and/or host-) associated RNA molecules. cRASL-seq enables highly sensitive (down to ∼1-100 pfu/ml or cfu/ml) and highly multiplexed (up to ∼10,000 target sequences) detection of pathogens. Importantly, cRASL-seq analysis of COVID-19 patient nasopharyngeal (NP) swab specimens does not involve nucleic acid extraction or reverse transcription, steps that have caused testing bottlenecks associated with other assays. Our simplified workflow additionally enables the direct and efficient genotyping of selected, informative SARS-CoV-2 polymorphisms across the entire genome, which can be used for enhanced characterization of transmission chains at population scale and detection of viral clades with higher or lower virulence. Given its extremely low per-sample cost, simple and automatable protocol and analytics, probe panel modularity, and massive scalability, we propose that cRASL-seq testing is a powerful new surveillance technology with the potential to help mitigate the current pandemic and prevent similar public health crises.Competing Interest StatementJ.J.C. and H.B.L. are listed as inventors on a patent describing the cRASL-seq method. H.B.L. has founded a company to license and commercialize oligonucleotide probe ligation related technologies.View Full Text

pathology↗

bioRxiv2020

Identity-by-descent detection across 487,409 British samples reveals fine-scale population structure, evolutionary history, and trait associations

Detection of Identical-By-Descent (IBD) segments provides a fundamental measure of genetic relatedness and plays a key role in a wide range of genomic analyses. We developed a new method, called FastSMC, that enables accurate biobank-scale detection of IBD segments transmitted by common ancestors living up to several hundreds of generations in the past. FastSMC combines a fast heuristic search for IBD segments with accurate coalescent-based likelihood calculations and enables estimating the age of common ancestors transmitting IBD regions. We applied FastSMC to 487,409 phased samples from the UK Biobank and detected the presence of [~]214 billion IBD segments transmitted by shared ancestors within the past 1,500 years. We quantified time-dependent shared ancestry within and across 120 postcodes, obtaining a fine-grained picture of genetic relatedness within the past two millennia in the UK. Sharing of common ancestors strongly correlates with geographic distance, enabling the localization of a samples birth coordinates from genomic data. We sought evidence of recent positive selection by identifying loci with unusually strong shared ancestry within recent millennia and we detected 12 genome-wide significant signals, including 7 novel loci. We found IBD sharing to be highly predictive of the sharing of ultra-rare variants in exome sequencing samples from the UK Biobank. Focusing on loss-of-function variation discovered using exome sequencing, we devised an IBD-based association test and detected 29 associations with 7 blood-related traits, 20 of which were not detected in the exome sequencing study. These results underscore the importance of modelling distant relatedness to reveal subtle population structure, recent evolutionary history, and rare pathogenic variation.

genetics↗

bioRxiv2020

Melatonin alters fluid phase co-existence in POPC/DPPC/cholesterol membranes

The structure and biophysical properties of lipid biomembranes are important for normal function of plasma and organelle membranes, which is essential for proper functioning of living cells. In Alzheimers disease (AD) the structure of neuronal membranes becomes compromised by the toxic effect of amyloid-{beta} (A{beta}) protein which accumulates at neuron synapses, resulting in membrane perforation and dysfunction, oxidative stress and cell death. Melatonin is an important pineal gland hormone that has been shown to be protective against A{beta} toxicity in cellular and animal studies, but the molecular mechanism of this protection is not well understood. It has been shown that melatonin can interact with model lipid membranes and alter the membrane biophysical properties, such as membrane molecular order and dynamics. This effect of melatonin has been previously studied in simple model bilayers with one or two lipid components, we consider a more complex ternary lipid mixture as our membrane model. In this study, we used 2H-NMR to investigate the effect of melatonin on lipid phase behaviour of a three-component model lipid membranes composed of 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (DPPC), 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) and cholesterol. We used deuterium labelled palmitoyl-d31 in POPC-d31 and DPPC-d62 separately, to probe the changes in hydrocarbon chain order as a function of temperature and varying concentrations of melatonin. We found that melatonin concentration influences phase separation in these ternary mixtures somewhat differently depending on whether POPC-d31 or DPPC-d62was used. At 5 mol% melatonin we observed phase separation in samples with POPC-d31, but not with DPPC-d62. However, at 10 mol% melatonin phase separation was observed in both samples with either POPC-d31 or DPPC-d62. These results indicate that melatonin can have a strong effect on membrane structure and physical properties, which may provide some clues to understanding how melatonin protects against A{beta}. SIGNIFICANCEMelatonin has been shown to be protective against A{beta} pathology in animal and cellular studies. Although the mechanism of this protection is not well-understood, melatonins membrane-active properties may be important in this regard. In this work solid-state deuterium nuclear magnetic resonance was used to study the effect of melatonin on the POPC/DPPC/cholesterol model membranes. Specifically, we showed that melatonin modifies lipid hydrocarbon chain order to promote phase separation. This knowledge helps to explain the role of melatonin in lipid domain formation and may provide a deeper understanding of the mechanism of melatonin neuroprotection in AD.

biophysics↗

bioRxiv2020

Effective Assessments of a Short-duration Poor Posture on Upper Limb Muscle Fatigue before Physical Exercise

A forward head and rounded shoulder posture is a poor posture that is widely seen in everyday life. It is known that sitting in such a poor posture with long hours will bring health issues such as muscle pain. However, it is not known whether sitting in this poor posture for a short period of time will affect human activities. This paper investigates the effects of a shortduration poor posture before some typical physical activities such as push-ups. The experiments are set up as follows. Fourteen male subjects are asked to do push-ups until fatigue with two surface electromyography (sEMG) at the upper limb. Two days later, they are asked to sit in this poor posture for 15 mins with 8 sEMG sensors located at given back muscles. Then they do the push-ups after the short-duration poor posture. The observations from the median frequency of sEMG signals at the upper limb indicate that the short-duration poor posture does affect the fatigue procedure of push-ups. A significant decreasing trend of the performance of push-ups is obtained after sitting in this poor posture. Such effects indicate that some parts of the back muscles indeed get fatigued with only 15 minutes sitting in this poor posture. By further investigating the time-frequency components of sEMG of back muscles, it is observed that the low and middle frequencies of sEMG signals from the infraspinatus muscle of the dominant side are demonstrated to be more prone to fatigue with the poor posture. Although this study focuses only on push-ups, similar experiments can be arranged for other physical exercises as well. This study provides new insights into the effect of a short-duration poor posture before physical activities. These insights can be used to guide athletes to pay attention to postures before physical activities to improve performance and reduce the risk of injury.

bioinformatics↗

bioRxiv2020

Gut Microbiome Pattern Reflects Healthy Aging and Predicts Extended Survival in Humans

The gut microbiome has important effects on human health, yet its importance in human aging remains unclear. Using two independent cohorts comprising 4582 individuals across the adult lifespan we demonstrate that, starting in mid-to-late adulthood, gut microbiomes become increasingly unique with age. This uniqueness pattern is strongly associated with gut microbial amino acid derivatives circulating within the bloodstream, many of which have been previously identified as longevity biomarkers. At the latest stages of human life, two distinct patterns emerge wherein individuals in good health show continued microbial drift toward a unique compositional state, while the same drift is absent in individuals who perform worse on a number of validated health measures. The identified healthy aging pattern is characterized by an overall depletion of core genera found across most humans - primarily a depletion in the nearly ubiquitous genus Bacteroides. Consistently, retaining a high Bacteroides dominance into extreme age, or, equivalently, having a low gut microbiome uniqueness score, predicts decreased survival in a four-year follow-up. Our comprehensive analysis identifies the gut microbiome as a novel component of healthy aging, with important implications for the worlds growing older population.

systems biology↗

bioRxiv2019

LincRNAs involved in DCS-induced fear extinction: Shedding light on the transcriptomic dark matter

There is a growing appreciation of the role of non-coding RNAs in the regulation of gene and protein expression. Long non-coding RNAs can modulate splicing by hybridizing with precursor messenger RNAs (pre-mRNAs) and influence RNA editing, mRNA stability, translation activation and microRNA-mRNA interactions by binding to mature mRNAs. LncRNAs are highly abundant in the brain and have been implicated in neurodevelopmental disorders. Long intergenic non-coding RNAs are the largest subclass of lncRNAs and play a crucial role in gene regulation. We used RNA sequencing and bioinformatic analyses to identify lincRNAs and their predicted mRNA targets associated with fear extinction that was induced by intra-hippocampally administered D-cycloserine in an animal model investigating the core phenotypes of PTSD. We identified 43 differentially expressed fear extinction related lincRNAs and 190 differentially expressed fear extinction related mRNAs. Eight of these lincRNAs were predicted to interact with and regulate 108 of these mRNAs and seven lincRNAs were predicted to interact with 22 of their pre-mRNA transcripts. On the basis of the functions of their target RNAs, we inferred that these lincRNAs bind to nucleotides, ribonucleotides and proteins and subsequently influence nervous system development, and morphology, immune system functioning, and are associated with nervous system and mental health disorders. Quantitative trait loci that overlapped with fear extinction related lincRNAs, included serum corticosterone level, neuroinflammation, anxiety, stress and despair related responses. This is the first study to identify lincRNAs and their RNA targets with a putative role in transcriptional regulation during fear extinction.

genomics↗