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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

Phase-amplitude coupling of neural oscillations can be effectively probed with concurrent TMS-EEG

Despite the widespread use of transcranial magnetic stimulation (TMS), knowledge of its neurophysiological mode of action is still incomplete. Recently, TMS has been proposed to synchronise neural oscillators, and to thereby increase the detectability of corresponding oscillations at the population level. As oscillations in the human brain are known to interact within nested hierarchies via phase-amplitude coupling, TMS might also be able to increase the macroscopic detectability of such coupling. In a concurrent TMS-electroencephalography study, we therefore examined the techniques influence on theta-gamma, alpha-gamma and beta-gamma phase-amplitude coupling by delivering single-pulse TMS (sTMS) and repetitive TMS (rTMS) over the left motor cortex and right visual cortex of healthy participants. The rTMS pulse trains were of 5 Hz, 11 Hz and 23 Hz for the three coupling variations, respectively. Relative to sham stimulation, all conditions showed transient but significant increases in phase-amplitude coupling at the stimulation site. In addition, we observed enhanced coupling over various other cortical sites, with a more extensive propagation during rTMS than during sTMS. By indicating that scalp-recorded phase-amplitude coupling can be effectively probed with TMS, these findings open the door to the techniques application in manipulative dissections of such coupling during human cognition and behaviour in healthy and pathological conditions.

neuroscience↗

bioRxiv2018

Molecular Genetic Analysis of Rbm45/Drbp1: Genomic Structure, Expression, and Evolution

RNA recognition motif-type RNA-binding domain containing proteins (RBDPs) participate in RNA metabolism including regulating mRNA stability, nuclear-cytoplasmic shuttling, and splicing. Rbm45 is an RBDP first cloned from rat brain and expressed spatiotemporally during rat neural development. More recently, RBM45 has been associated with pathological aggregates in the human neurological disorders amyotrophic lateral sclerosis, frontotemporal lobar degeneration, and Alzheimers. Rbm45 and the neural developmental protein musashi-1 are in the same family of RDBPs and have similar expression patterns. In contrast to Musashi-1, which is upregulated during colorectal carcinogenesis, we found no association of RBM45 overexpression in human colon cancer tissue. In order to begin characterizing RNA-binding partners of Rbm45, we have successfully cloned and expressed human RBM45 in an Intein fusion-protein expression system. Furthermore, to gain a better understanding of the molecular genetics and evolution of Rbm45, we used an in silico approach to analyze the gene structure of the human and mouse Rbm45 homologues and explored the evolutionary conservation of Rbm45 in metazoans. Human RBM45 and mouse Rbm45 span {bsim}17 kb and 13 kb, respectively, and contain 10 exons, one of which is non-coding. Both genes have TATA-less promoters with an initiator and a GC-rich element. Downstream of exon 10, both homologues have canonical polyadenylation signals and an embryonic cytoplasmic polyadenylation element. Moreover, our data indicate Rbm45 is conserved across all metazoan taxa from sponges (phylum Porifera) to humans (phylum Chordata), portending a fundamental role in metazoan development.

genetics↗

bioRxiv2018

Fractionated follow-up chemotherapy delays the onset of resistance in bone metastatic prostate cancer

Prostate cancer to bone metastases are almost always lethal. This results from the ability of metastatic prostate cancer cells to co-opt bone remodeling leading to what is known as the vicious cycle. Understanding how tumor cells can disrupt bone homeostasis through their interactionswith the stroma and how metastatic tumors respond to treatment is key to the development of new treatments for what remains an incurable disease. Here we describe an evolutionary game theoretical model of both the homeostatic bone remodeling and its co-option by prostate cancer metastases. This model extends past the evolutionary aspects typically considered in game theoretical models by also including ecological factors such as the physical microenvironment of the bone. Our model recapitulates the current paradigm of the \"vicious cycle\" driving tumor growth and sheds light on the interactions of heterogeneous tumor cells with the bone microenvironment and treatment response. Our results show that resistant populations naturally become dominant in the metastases under conventional cytotoxic treatment and that novel schedules could be used to better control the tumor and the associated bone disease compared to the current standard of care. Specifically, we introduce fractionated follow up therapy - chemotherapy where dosage is administered initially in one solid block followed by alternating smaller doeses and holidays - and argue that it is better than either a continuous application or a periodic one. Furthermore, we also show that different regimens of chemotherapy can lead to different amounts of pathological bone that are known to correlate with poor quality of life for bone metastatic prostate cancer patients.

cancer biology↗

bioRxiv2018

Weakly-bound Dimers that Underlie the Crystal Nucleation Precursors in Lysozyme Solutions

Protein crystallization is central to understanding of molecular structure in biology, a vital part of processes in the pharmaceutical industry, and a crucial component of numerous disease pathologies. Crystallization starts with nucleation and how nucleation proceeds determines the crystallization rate and essential properties of the resulting crystal population. Recent results with several proteins indicate that crystals nucleate within preformed mesoscopic protein-rich clusters. The origin of the mesoscopic clusters is poorly understood. In the case of lysozyme, a common model of protein biophysics, earlier findings suggest that clusters exist owing to the dynamics of formation and decay of weakly-bound transient dimers. Here we present evidence of a weakly bound lysozyme dimer in solutions of this protein. We employ two electrospray mass spectrometry techniques, a combined ion mobility separation mass spectrometry and a high-resolution implementation. To enhance the weak but statistically-significant dimer signal we develop a method based on the residuals between the maxima of the isotope peaks in Fourier space and their Gaussian envelope. We demonstrate that these procedures sensitively detect the presence of a non-covalently bound dimer and distinguish its signal from other polypeptides, noise, and sampling artefacts. These findings contribute essential elements of the crystal nucleation mechanism of lysozyme and other proteins and suggest pathways to control nucleation and crystallization by enhancing or suppressing weak oligomerization.

biophysics↗

bioRxiv2018

Network-based prediction of protein interactions

As biological function emerges through interactions between a cells molecular constituents, understanding cellular mechanisms requires us to catalogue all physical interactions between proteins [1-4]. Despite spectacular advances in high-throughput mapping, the number of missing human protein-protein interactions (PPIs) continues to exceed the experimentally documented interactions [5, 6]. Computational tools that exploit structural, sequence or network topology information are increasingly used to fill in the gap, using the patterns of the already known interactome to predict undetected, yet biologically relevant interactions [7-9]. Such network-based link prediction tools rely on the Triadic Closure Principle (TCP) [10-12], stating that two proteins likely interact if they share multiple interaction partners. TCP is rooted in social network analysis, namely the observation that the more common friends two individuals have, the more likely that they know each other [13, 14]. Here, we offer direct empirical evidence across multiple datasets and organisms that, despite its dominant use in biological link prediction, TCP is not valid for most protein pairs. We show that this failure is fundamental - TCP violates both structural constraints and evolutionary processes. This understanding allows us to propose a link prediction principle, consistent with both structural and evo-lutionary arguments, that predicts yet uncovered protein interactions based on paths of length three (L3). A systematic computational cross-validation shows that the L3 principle significantly outperforms existing link prediction methods. To experimentally test the L3 predictions, we perform both large-scale high-throughput and pairwise tests, finding that the predicted links test positively at the same rate as previously known interactions, suggesting that most (if not all) predicted interactions are real. Combining L3 predictions with experimen-tal tests provided new interaction partners of FAM161A, a protein linked to retinitis pigmentosa, offering novel insights into the molecular mechanisms that lead to the disease. Because L3 is rooted in a fundamental biological principle, we expect it to have a broad applicability, enabling us to better understand the emergence of biological function under both healthy and pathological conditions.\n\nSummaryWe unveil a fundamental organizing principle of biological networks and demonstrate its predictive power for uncovering novel protein interactions.

systems biology↗

bioRxiv2018

Deep Genomic Signature for early metastasis prediction in prostate cancer

For prostate cancer patients, timing and intensity of therapy are adjusted based on their prognosis. Clinical and pathological factors, and recently, gene expression-based signatures have been shown to predict metastatic prostate cancer. Previous studies used labelled datasets, i.e. those with information on the metastasis outcome, to discover gene signatures to predict metastasis. Due to steady progression of prostate cancer, datasets for this cancer have a limited number of labelled samples but more unlabelled samples. In addition to this issue, the high dimensionality of the gene expression data also poses a significant challenge to train a classifier and predict metastasis accurately. In this study, we aim to boost the prediction accuracy by utilizing both labelled and unlabelled datasets together. We propose Deep Genomic Signature (DGS), a method based on Denoising Auto-Encoders (DAEs) and transfer learning. DGS has the following steps: first, we train a DAE on a large unlabelled gene expression dataset to extract the most salient features of its samples. Then, we train another DAE on a small labelled dataset for a similar purpose. Since the labelled dataset is small, we employ a transfer learning approach and use the parameters learned from the first DAE in the second one. This approach enables us to train a large DAE on a small dataset. After training the second DAE, we obtain the list of genes with high weights by applying a standard deviation filter on the transferred and learned weights. Finally, we train an elastic net logistic regression model on the expression of the selected genes to predict metastasis. Because of the elastic net regularization, some of the selected genes have non-zero coefficients in the classifier which we consider as the DGS gene signature for metastasis. We apply DGS to six labelled and one large unlabelled prostate cancer datasets. Results on five validation datasets indicate that DGS outperforms state-of-the-art gene signatures (obtained from only labelled datasets) in terms of prediction accuracy. Survival analyses demonstrate the potential clinical utility of our gene signature that adds novel prognostic information to the well-established clinical factors and the state-of-the-art gene signatures. Finally, pathway analysis reveals that the DGS gene signature captures the hallmarks of prostate cancer metastasis. These results suggest that our method helps to identify a robust gene signature that may improve patient management.

bioinformatics↗

bioRxiv2018

Aberrant expression of collagen family genes in the brain regions developing under agonistic interactions in male mice

As previously established, chronic agonistic interactions lead to the development of depression-like state under social defeat stress in the defeated mice and pathology of aggressive behavior in the winning mice. According to the numerous research data, these psychopathological states are accompanied by tremendous molecular and cellular changes in the brain. The paper aimed to study the influence of 20-day period of agonistic interactions on the expression mode of collagen family genes, encoding the proteins, which are basic components of extracellular matrix (ECM), in the different brain regions of mice using the RNA-Seq database. Most of the differentially expressed collagen genes were upregulated in the hypothalamus and striatum of chronically aggressive and defeated mice and in the hippocampus of the defeated mice. In the ventral tegmental area the most genes were downregulated in both experimental groups. It has been assumed that aberrant expression of collagen genes induced by long experience of agonistic interactions can indicate defects of ECM specific for brain regions in mice with alternative social experiences. This study first shows remodeling of molecular base in the ECM under development of experimental psychoneuropathologies.\n\nCorresponding authors: Kudryavtseva N.N., n.n.kudryavtseva@gmail.com; Babenko V.N., bob@bionet.nsc.ru

neuroscience↗

bioRxiv2018

Non-canonical Wnt signalling initiates scarring in biliary disease

Cholangiopathies, or biliary diseases, account for a significant proportion of adult and paediatric liver disease. In these pathologies, iterative cycles of damage and repair result in the development of a regenerative microenvironment surrounding the bile ducts, which orchestrates both epithelial proliferation and also biliary fibrosis. Ultimately, fibrosis at the cost of repair results in cholestasis and liver failure, necessitating liver transplantation. Whilst the fibrogenic mechanisms in hepatocellular disease have been widely studied, little is known about the processes that regulate biliary scarring. We sought to determine how the injured biliary epithelium communicates to adjacent stromal cells to regulate scar formation, and to identify therapeutically targetable pathways that could be inhibited to reduce biliary scarring, whilst maintaining the pro-regenerative stroma. Using human tissue, bile duct organoids and animal models of biliary disease, we show that non-canonical Wnt signalling is important in initiating biliary scarring. This process is driven by myeloid Wnt5a and acts through epithelial Vangl2, which is upstream of Jnk/cJun signalling. Activation of this pathway drives a pro-fibrotic signalling process which instructs portal fibroblasts to synthesise collagen. Finally, we determine that therapeutic Wnt ligand inhibition reduces biliary scarring, identifying non-canonical Wnt signalling as a novel target for anti-fibrotic therapy in cholestatic biliary disease.

physiology↗

bioRxiv2018

ALDH1-positive intratumoral stromal cells indicate epithelial differentiation and good prognosis in prostate cancer

Aldehyde dehydrogenase 1 (ALDH1) characterizes tumor-initiating cells in solid tumors, however little is known about its expression in intratumoral stromal cells. Herein, we aimed to dissect its potential dual relevance in prostate cancer (PCa).\n\nALDH1 expression was evaluated immunohistochemically in tumor and stromal cells in primary PCa and metastasis. It was correlated with clinico-pathological parameters, outcome of patients, and selected protein expression (CK5/6, CK14, CK8/18, CK19, EpCAM, Ki-67, E-cadherin, N-cadherin, and vimentin).\n\nALDH1 protein was detected in tumor and stromal cells in 16% and 67% of 348 primary PCa, respectively. Tumor cell ALDH1 expression was associated with advanced tumor (T) status (p=0.009), higher Gleason score (p=0.016), shorter time to biochemical recurrence (BR) (p=0.010) and CK14 expression (p=0.023). Stromal cell ALDH1 expression correlated with lower T status (p=0.008), N0 status (p=0.017), lower Gleason score (p=0.016) and longer time to BR (p=0.017). In the subgroup of dAmico high-risk patients it occurred even to be an independent predictor of good prognosis (multivariate analysis, p=0.050). ALDH1 was found in stroma of tumors characterized by CK8/18 (p=0.033) or EpCAM expression (p<0.001) and rarely by epithelial-mesenchymal transition defined as CK8/18(-)vimentin(+) phenotype (p=0.003). ALDH1 was detected in tumor cells and stroma of 33% and 41% of hormone naive lymph node metastases (n=63), 52% and 24% of castration resistant bone metastases, as well as 89% and 28% of castration resistant visceral metastases (n=21), respectively.\n\nWe have determined that contrary to tumor cell ALDH1, the presence of stromal ALDH1 is associated with a more differentiated tumor epithelial phenotype in primary PCa, improved clinical outcome, and is less frequent in PCa metastases.\n\nAbbreviations

cancer biology↗

bioRxiv2018

Cryo-EM structure of alpha-synuclein fibrils

Intracellular inclusions of alpha-synuclein are the neuropathological hallmark of progressive disorders called synucleinopathies. Alpha-synuclein fibrils are associated with transmissive cell-to-cell propagation of pathology. We report the structure of an alpha-synuclein fibril (residues 1-121) determined by cryo-electron microscopy at 3.4[A] resolution. Two protofilaments form a polar fibril composed of staggered {beta}-strands. The backbone of residues 38 to 95, including the fibril core and the non-amyloid component region, are well resolved in the EM map. Residues 50-57, containing three mutation sites associated with familial synucleinopathies, form the interface between the two protofilaments and contribute to fibril stability. A hydrophobic cleft may have implications for fibril elongation, and inform the rational design of molecules for diagnosis and treatment of synucleinopathies.

neuroscience↗

bioRxiv2018

Assessment of cognitive reserve using near infrared spectroscopy

Cognitive reserve (CR) is the ability to preserve cognitive functions in the presence of brain pathology. One commonly used proxy measure of CR is IQ. In the context of Alzheimers disease (AD), patients with higher CR show better cognitive performance relative to brain damage therefore higher CR reduces the risk of dementia. There is a strong need to develop a reliable biomarker of CR given the growing interest in understanding protective brain mechanisms in AD. Recent fMRI studies indicate that frontoparietal network may play an important role in the maintenance of cognitive reserve. The goal of this study was to measure functional connectivity (FC) of the prefrontal cortex using near infrared spectroscopy (NIRS) and to study the relationship of prefrontal FC with cognitive abilities and motoric skills.\n\nWe analyzed resting state optical data recorded from prefrontal cortex in 13 healthy individuals who were also assessed by Wechsler Abbreviated Scale of Intelligence (WASI) test and the Purdue Pegboard test (PPT). For each participant, activity of each prefrontal channel was correlated with all other channels and positive correlation coefficients were Fisher-transformed and averaged over all PFC channels giving the Global Functional Connectivity (GFC) of PFC. The resulting GFC number for each individual was then correlated with the corresponding IQ (WASI full score) and the PPT scores.\n\nPrefrontal connectivity was found to positively correlate with IQ while showing the lack of or negative correlation with the Purdue Pegboard subtests. These results demonstrate that the cost-effective and noninvasive NIRS technology can be used to evaluate prefrontal functional connectivity thus providing a neurophysiological measure of cognitive reserve.\n\nE-mail address for correspondence:\n\nam236@georgetown.edu

neuroscience↗

bioRxiv2018

Multi-Link Analysis: Brain Network Comparison via Sparse Connectivity Analysis

The analysis of the brain from a connectivity perspective is unveiling novel insights into brain structure and function. Discovery is, however, hindered by the lack of prior knowledge used to make hypotheses. On the other hand, exploratory data analysis is made complex by the high dimensionality of data. Indeed, in order to assess the effect of pathological states on brain networks, neuroscientists are often required to evaluate experimental effects in case-control studies, with hundreds of thousand connections.\n\nIn this paper, we propose an approach to identify the multivariate relationships in brain connections that characterise two distinct groups, hence permitting the investigators to immediately discover sub-networks that contain information about the differences between experimental groups. In particular, we are interested in data discovery related to connectomics, where the connections that characterize differences between two groups of subjects are found. Nevertheless, those connections not necessarily maximize accuracy in classification since this does not guarantee reliable interpretation of specific differences between groups. In practice, our method exploits recent machine learning techniques employing sparsity to deal with weighted networks describing the whole-brain macro connectivity. We evaluated our technique on functional and structural connectomes from human and mice brain data. In our experiments, we automatically identified disease-relevant connections in datasets with supervised and unsupervised anatomy-driven parcellation approaches, and by using high-dimensional datasets.

neuroscience↗

bioRxiv2018

DeepQSM - Using Deep Learning to Solve the Dipole Inversion for MRI Susceptibility Mapping

Quantitative susceptibility mapping (QSM) aims to extract the magnetic susceptibility of tissue from magnetic resonance imaging (MRI) phase measurements. The mapping of magnetic susceptibility in vivo has gained broad interest in several fields of science and medicine because it yields relevant information on biological tissue properties, predominantly myelin, iron and calcium. Thereby, QSM can also reveal pathological changes of these key components in devastating diseases such as Parkinsons disease, Multiple Sclerosis, or hepatic iron overload. As QSM requires the solution of an ill-posed field-to-source-inversion, current techniques utilize manual optimization of regularization parameters to balance between smoothing, artifacts and quantification accuracy. We trained a fully convolutional deep neural network - DeepQSM - to invert the magnetic dipole kernel convolution. This network is capable of solving the ill-posed field-to-source inversion on real-world in vivo MRI phase data without the need for manual parameter tuning, which proves that this network has generalized the underlying mathematical principle of the dipole inversion. We demonstrate that DeepQSMs susceptibility maps enable identification of deep brain substructures that are not visible in MRI phase data and provide information on their respective magnetic tissue properties. We illustrate DeepQSMs clinical relevance in a patient with multiple sclerosis showing its sensitivity to white matter lesions. In summary, DeepQSM can be used to determine the composition of myelin sheets of nerve fibers in the brain, and to assess quantitative information on iron homeostasis and its dysregulation, and will subsequently contribute to a better understanding of these biological processes in health and disease.

neuroscience↗

bioRxiv2018

PKD2 influence uric acid levels and gout risk by interacting with ABCG2

BackgroundUric acid is the final product of purine metabolism and elevated serum urate levels can cause gout. Conflicting results were reported for the effect of PKD2 on serum urate levels and gout risk. Therefore, our study attempted to state the important role of PKD2 in influencing the pathogenesis of gout.\n\nMethodSNPs in PKD2 (rs2725215 and rs2728121) and ABCG2 (rs2231137 and rs1481012) were tested in approximately 5,000 Chinese individuals.\n\nResultsTwo epistatic interactions between loci in PKD2 (rs2728121) and ABCG2 (rs1481012 and rs2231137) showed distinct contributions to uric acid levels with P int values of 0.018 and 0.004, respectively, and the associations varies by gender and BMI. The SNP pair of rs2728121 and rs1481012 justly played roles in uric acid in females (P int = 0.006), while the other pair did in males (P int = 0.017). Regarding BMI, the former SNP pair merely contributed in overweigh subjects (P int = 0.022) and the latter one did in both normal and overweigh individuals (P int = 0.013 and 0.047, respectively). Furthermore, the latter SNP pair was also associated with gout pathology (P int = 0.001), especially in males (P int = 0.001). Finally, functional analysis showed potential epistatic interactions in those genes region and PKD2 mRNA expression had a positive correlation with ABCG2s (r = 0.743, P = 5.83e-06).\n\nConclusionOur study for the first time identified that epistatic interactions between PKD2 and ABCG2 influenced serum urate concentrations and gout risk, and PKD2 might affect the pathogenesis from elevated serum urate to hyperuricemia to gout by modifying ABCG2.

genetics↗

bioRxiv2018

The neuronal ceroid lipofuscinosis protein, Cln7, regulates neural development from the post-synaptic cell.

The neuronal ceroid lipofuscinoses (NCLs) are a group of fatal, monogenic neurodegenerative disorders with an early onset in infancy or childhood. Despite identification of the genes disrupted in each form of the disease, their normal cellular role and how their deficits lead to disease pathology is not fully understood. Cln7, a major facilitator superfamily domain-containing protein, is affected in a late infantile-onset form of NCL. Using the Drosophila larval neuromuscular junction as a model to study neural development, we demonstrate that Cln7 is required for the normal growth of synapses. In a Cln7 mutant, synapses fail to develop fully leading to reduced function and behavioral changes with dysregulation of TOR activity. Cln7 expression is restricted to the post-synaptic cell and the protein localizes to vesicles immediately adjacent to the post-synaptic membrane. Our data suggest an involvement for Cln7 in regulating trans-synaptic communication.

developmental biology↗

bioRxiv2018

Effect of 5-trans isomer of arachidonic acid on model liposomal membranes studied by a combined simulation and experimental approach

Unsaturated fatty acids are found in humans predominantly in the cis configuration. Fatty acids in the trans configuration are primarily the result of human processing (trans fats), but can also be formed endogenously by radical stress. The cis-trans isomerization of fatty acids by free radicals could be connected to several pathologies. Trans fats have been linked to an increased risk of coronary artery disease; however, the reasons for the resulting pathogenesis remain unclear. Here, we investigate the effect of a mono trans isomer of arachidonic acid (C20:4-5trans,8cis,11cis,14cis) produced by free radicals in physiological concentration on a model erythrocyte membrane using a combined experimental and theoretical approach. Molecular Dynamics (MD) simulations of two model lipid bilayers containing arachidonic acid and its 5-trans isomer in 3% mol. were carried out for this purpose. The 5-trans isomer formation in the phospholipids was catalyzed by HOCH2CH2S* radicals, generated from the corresponding thiol by {gamma}-irradiation, in multilamellar vesicles (MLVs) of SAPC. Large unilamellar vesicles were made by the extrusion method (LUVET) as a biomimetic model for cis-trans isomerization. Atomic Force Microscopy and Dynamic Light Scattering were used to measure the average size, morphology, and the z-potential of the liposomes. Both results from MD simulations and experiments are in agreement and indicate that the two model membranes display different physicochemical properties in that the bilayers containing the trans fatty acids were more ordered and more rigid than those containing solely the cis arachidonic acid. Correspondingly, the average size of the liposomes containing trans isomers was smaller than the ones without.

biophysics↗

bioRxiv2018

Automated Gleason grading of prostate cancer tissue microarrays via deep learning

The Gleason grading system remains the most powerful prognostic predictor for patients with prostate cancer since the 1960s. Its application requires highly-trained pathologists, is tedious and yet suffers from limited inter-pathologist reproducibility, especially for the intermediate Gleason score 7. Automated annotation procedures constitute a viable solution to remedy these limitations.\n\nIn this study, we present a deep learning approach for automated Gleason grading of prostate cancer tissue microarrays with Hematoxylin and Eosin (H&E) staining. Our system was trained using detailed Gleason annotations on a discovery cohort of 641 patients and was then evaluated on an independent test cohort of 245 patients annotated by two pathologists. On the test cohort, the inter-annotator agreements between the model and each pathologist, quantified via Cohens quadratic kappa statistic, were 0.75 and 0.71 respectively, comparable with the inter-pathologist agreement (kappa=0.71). Furthermore, the models Gleason score assignments achieved pathology expert-level stratification of patients into prognostically distinct groups, on the basis of disease-specific survival data available for the test cohort.\n\nOverall, our study shows promising results regarding the applicability of deep learning-based solutions towards more objective and reproducible prostate cancer grading, especially for cases with heterogeneous Gleason patterns.

bioinformatics↗

bioRxiv2018

Impulsive choice in mice lacking paternal expression of Grb10 provides evidence for intra-genomic conflict in behavior

Imprinted genes are expressed from one parental allele only as a consequence of epigenetic events that take place in the mammalian germ line and are thought to have evolved through intra-genomic conflict between parental alleles. We demonstrate, for the first time, oppositional effects of imprinted genes on brain and behavior. Specifically, here we show that mice lacking paternal Grb10 make fewer impulsive choices, with no dissociable effects on a separate measure of impulsive action. Taken together with previous work showing that mice lacking maternal Nesp55 make more impulsive choices this suggests that impulsive choice behavior is a substrate for the action of genomic imprinting. Moreover, the contrasting effect of these two genes suggests impulsive choices are subject to intra-genomic conflict and that maternal and paternal interests pull this behavior in opposite directions. Finally, these data may also indicate that an imbalance in expression of imprinted genes contributes to pathological conditions such as gambling and drug addiction, where impulsive behavior becomes maladaptive.

genetics↗