10,903 research outputs found
The mRNA-bound proteome of the human malaria parasite Plasmodium falciparum.
BackgroundGene expression is controlled at multiple levels, including transcription, stability, translation, and degradation. Over the years, it has become apparent that Plasmodium falciparum exerts limited transcriptional control of gene expression, while at least part of Plasmodium's genome is controlled by post-transcriptional mechanisms. To generate insights into the mechanisms that regulate gene expression at the post-transcriptional level, we undertook complementary computational, comparative genomics, and experimental approaches to identify and characterize mRNA-binding proteins (mRBPs) in P. falciparum.ResultsClose to 1000 RNA-binding proteins are identified by hidden Markov model searches, of which mRBPs encompass a relatively large proportion of the parasite proteome as compared to other eukaryotes. Several abundant mRNA-binding domains are enriched in apicomplexan parasites, while strong depletion of mRNA-binding domains involved in RNA degradation is observed. Next, we experimentally capture 199 proteins that interact with mRNA during the blood stages, 64 of which with high confidence. These captured mRBPs show a significant overlap with the in silico identified candidate RBPs (p < 0.0001). Among the experimentally validated mRBPs are many known translational regulators active in other stages of the parasite's life cycle, such as DOZI, CITH, PfCELF2, Musashi, and PfAlba1-4. Finally, we also detect several proteins with an RNA-binding domain abundant in Apicomplexans (RAP domain) that is almost exclusively found in apicomplexan parasites.ConclusionsCollectively, our results provide the most complete comparative genomics and experimental analysis of mRBPs in P. falciparum. A better understanding of these regulatory proteins will not only give insight into the intricate parasite life cycle but may also provide targets for novel therapeutic strategies
The lncRNA landscape of breast cancer reveals a role for DSCAM-AS1 in breast cancer progression.
Molecular classification of cancers into subtypes has resulted in an advance in our understanding of tumour biology and treatment response across multiple tumour types. However, to date, cancer profiling has largely focused on protein-coding genes, which comprise <1% of the genome. Here we leverage a compendium of 58,648 long noncoding RNAs (lncRNAs) to subtype 947 breast cancer samples. We show that lncRNA-based profiling categorizes breast tumours by their known molecular subtypes in breast cancer. We identify a cohort of breast cancer-associated and oestrogen-regulated lncRNAs, and investigate the role of the top prioritized oestrogen receptor (ER)-regulated lncRNA, DSCAM-AS1. We demonstrate that DSCAM-AS1 mediates tumour progression and tamoxifen resistance and identify hnRNPL as an interacting protein involved in the mechanism of DSCAM-AS1 action. By highlighting the role of DSCAM-AS1 in breast cancer biology and treatment resistance, this study provides insight into the potential clinical implications of lncRNAs in breast cancer
RANK: Large-Scale Inference with Graphical Nonlinear Knockoffs
Power and reproducibility are key to enabling refined scientific discoveries
in contemporary big data applications with general high-dimensional nonlinear
models. In this paper, we provide theoretical foundations on the power and
robustness for the model-free knockoffs procedure introduced recently in
Cand\`{e}s, Fan, Janson and Lv (2016) in high-dimensional setting when the
covariate distribution is characterized by Gaussian graphical model. We
establish that under mild regularity conditions, the power of the oracle
knockoffs procedure with known covariate distribution in high-dimensional
linear models is asymptotically one as sample size goes to infinity. When
moving away from the ideal case, we suggest the modified model-free knockoffs
method called graphical nonlinear knockoffs (RANK) to accommodate the unknown
covariate distribution. We provide theoretical justifications on the robustness
of our modified procedure by showing that the false discovery rate (FDR) is
asymptotically controlled at the target level and the power is asymptotically
one with the estimated covariate distribution. To the best of our knowledge,
this is the first formal theoretical result on the power for the knockoffs
procedure. Simulation results demonstrate that compared to existing approaches,
our method performs competitively in both FDR control and power. A real data
set is analyzed to further assess the performance of the suggested knockoffs
procedure.Comment: 37 pages, 6 tables, 9 pages supplementary materia
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Integrated single cell analysis of blood and cerebrospinal fluid leukocytes in multiple sclerosis
Common dysregulation network in the human prefrontal cortex underlies two neurodegenerative diseases.
Using expression profiles from postmortem prefrontal cortex samples of 624 dementia patients and non-demented controls, we investigated global disruptions in the co-regulation of genes in two neurodegenerative diseases, late-onset Alzheimer's disease (AD) and Huntington's disease (HD). We identified networks of differentially co-expressed (DC) gene pairs that either gained or lost correlation in disease cases relative to the control group, with the former dominant for both AD and HD and both patterns replicating in independent human cohorts of AD and aging. When aligning networks of DC patterns and physical interactions, we identified a 242-gene subnetwork enriched for independent AD/HD signatures. This subnetwork revealed a surprising dichotomy of gained/lost correlations among two inter-connected processes, chromatin organization and neural differentiation, and included DNA methyltransferases, DNMT1 and DNMT3A, of which we predicted the former but not latter as a key regulator. To validate the inter-connection of these two processes and our key regulator prediction, we generated two brain-specific knockout (KO) mice and show that Dnmt1 KO signature significantly overlaps with the subnetwork (P = 3.1 Ă— 10(-12)), while Dnmt3a KO signature does not (P = 0.017)
Phenotypic responses to interspecies competition and commensalism in a naturally derived microbial co-culture
The fundamental question of whether different microbial species will co-exist or compete in a given environment depends on context, composition and environmental constraints. Model microbial systems can yield some general principles related to this question. In this study we employed a naturally occurring co-culture composed of heterotrophic bacteria, Halomonas sp. HL-48 and Marinobacter sp. HL- 58, to ask two fundamental scientific questions: 1) how do the phenotypes of two naturally co-existing species respond to partnership as compared to axenic growth? and 2) how do growth and molecular phenotypes of these species change with respect to competitive and commensal interactions? We hypothesized – and confirmed – that co-cultivation under glucose as the sole carbon source would result in competitive interactions. Similarly, when glucose was swapped with xylose, the interactions became commensal because Marinobacter HL-58 was supported by metabolites derived from Halomonas HL- 48. Each species responded to partnership by changing both its growth and molecular phenotype as assayed via batch growth kinetics and global transcriptomics. These phenotypic responses depended on nutrient availability and so the environment ultimately controlled how they responded to each other. This simplified model community revealed that microbial interactions are context-specific and different environmental conditions dictate how interspecies partnerships will unfold
Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data
<p>Abstract</p> <p>Background</p> <p>In practice many biological time series measurements, including gene microarrays, are conducted at time points that seem to be interesting in the biologist's opinion and not necessarily at fixed time intervals. In many circumstances we are interested in finding targets that are expressed periodically. To tackle the problems of uneven sampling and unknown type of noise in periodicity detection, we propose to use robust regression.</p> <p>Methods</p> <p>The aim of this paper is to develop a general framework for robust periodicity detection and review and rank different approaches by means of simulations. We also show the results for some real measurement data.</p> <p>Results</p> <p>The simulation results clearly show that when the sampling of time series gets more and more uneven, the methods that assume even sampling become unusable. We find that M-estimation provides a good compromise between robustness and computational efficiency.</p> <p>Conclusion</p> <p>Since uneven sampling occurs often in biological measurements, the robust methods developed in this paper are expected to have many uses. The regression based formulation of the periodicity detection problem easily adapts to non-uniform sampling. Using robust regression helps to reject inconsistently behaving data points.</p> <p>Availability</p> <p>The implementations are currently available for Matlab and will be made available for the users of R as well. More information can be found in the web-supplement <abbrgrp><abbr bid="B1">1</abbr></abbrgrp>.</p
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