In trypanosomes, trans-splicing is a major essential RNA-processing mechanism that involves the addition of a spliced leader sequence to all mRNAs from a small RNA species, known as the spliced ...leader RNA (SL RNA). SL RNA maturation is poorly understood and it is not clear where assembly with Sm proteins takes place. In this study, we followed the localization of the SL RNA during knockdown of Sm proteins and XPO1, which in metazoa functions in transport of mRNA and U snRNAs from the nucleus to the cytoplasm. We found that XPO1 has no role in SL RNA biogenesis in wild-type cells, or when the cells are depleted of Sm proteins. During Sm depletion, ‘defective’ SL RNA lacking cap modification at position +4 first accumulates in the nucleus, suggesting that Sm assembly on SL RNA most probably takes place in this compartment. Only after massive nuclear accumulation is the ‘defective’ SL RNA exported to the cytoplasm to form SL RNP-C, which may be a route to dispose of SL RNA when its normal biogenesis is blocked.
In eukaryotes the seven Sm core proteins bind to U1, U2, U4, and U5 snRNAs. In Trypanosoma brucei, Sm proteins have been implicated in binding both spliced leader (SL) and U snRNAs. In this study, we ...examined the function of these Sm proteins using RNAi silencing and protein purification. RNAi silencing of each of the seven Sm genes resulted in accumulation of SL RNA as well as reduction of several U snRNAs. Interestingly, U2 was unaffected by the loss of SmB, and both U2 and U4 snRNAs were unaffected by the loss of SmD3, suggesting that these snRNAs are not bound by the heptameric Sm complex that binds to U1, U5, and SL RNA. RNAi silencing and protein purification showed that U2 and U4 snRNAs were bound by a unique set of Sm proteins that we termed SSm (specific spliceosomal Sm proteins). This is the first study that identifies specific core Sm proteins that bind only to a subset of spliceosomal snRNAs.
The Human Cell Atlas White Paper Regev, Aviv; Teichmann, Sarah; Rozenblatt-Rosen, Orit ...
arXiv (Cornell University),
10/2018
Journal Article
Odprti dostop
The Human Cell Atlas (HCA) will be made up of comprehensive reference maps of
all human cells - the fundamental units of life - as a basis for understanding
fundamental human biological processes and ...diagnosing, monitoring, and treating
disease. It will help scientists understand how genetic variants impact disease
risk, define drug toxicities, discover better therapies, and advance
regenerative medicine. A resource of such ambition and scale should be built in
stages, increasing in size, breadth, and resolution as technologies develop and
understanding deepens. We will therefore pursue Phase 1 as a suite of flagship
projects in key tissues, systems, and organs. We will bring together experts in
biology, medicine, genomics, technology development and computation (including
data analysis, software engineering, and visualization). We will also need
standardized experimental and computational methods that will allow us to
compare diverse cell and tissue types - and samples across human communities -
in consistent ways, ensuring that the resulting resource is truly global.
This document, the first version of the HCA White Paper, was written by
experts in the field with feedback and suggestions from the HCA community,
gathered during recent international meetings. The White Paper, released at the
close of this yearlong planning process, will be a living document that evolves
as the HCA community provides additional feedback, as technological and
computational advances are made, and as lessons are learned during the
construction of the atlas.
The Hippo signaling pathway is a major regulator of organ size. In the liver, Hippo pathway deregulation promotes hyperplasia and hepatocellular carcinoma primarily through hyperactivation of its ...downstream effector, YAP. The LATS2 tumor suppressor is a core member of the Hippo pathway. A screen for LATS2-interacting proteins in liver-derived cells identified the transcription factor SREBP2, master regulator of cholesterol homeostasis. LATS2 down-regulation caused SREBP activation and accumulation of excessive cholesterol. Likewise, mice harboring liver-specific Lats2 conditional knockout (Lats2-CKO) displayed constitutive SREBP activation and overexpressed SREBP target genes and developed spontaneous fatty liver disease. Interestingly, the impact of LATS2 depletion on SREBP-mediated transcription was clearly distinct from that of YAP overexpression. When challenged with excess dietary cholesterol, Lats2-CKO mice manifested more severe liver damage than wild-type mice. Surprisingly, apoptosis, inflammation, and fibrosis were actually attenuated relative to wild-type mice, in association with impaired p53 activation. Subsequently, Lats2-CKO mice failed to recover effectively from cholesterol-induced damage upon return to a normal diet. Additionally, decreased LATS2 mRNA in association with increased SREBP target gene expression was observed in a subset of human nonalcoholic fatty liver disease cases. Together, these findings further highlight the tight links between tumor suppressors and metabolic homeostasis.
Introduction : Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term ambulatory ECG recordings have shown high performance. However, the ...rhythm-based approach does not take advantage of the morphological information conveyed by the different ECG waveforms, particularly the f-waves. As a result, the performance of such models may be inherently limited. Methods : To address this limitation, we have developed a deep learning model, named RawECGNet, to detect episodes of AF and atrial flutter (AFl) using the raw, single-lead ECG. We compare the generalization performance of RawECGNet on two external data sets that account for distribution shifts in geography, ethnicity, and lead position. RawECGNet is further benchmarked against a state-of-the-art deep learning model, named ArNet2, which utilizes rhythm information as input. Results : Using RawECGNet, the results for the different leads in the external test sets in terms of the F1 score were 0.91-0.94 in RBDB and 0.93 in SHDB, compared to 0.89-0.91 in RBDB and 0.91 in SHDB for ArNet2. The results highlight RawECGNet as a high-performance, generalizable algorithm for detection of AF and AFl episodes, exploiting information on both rhythm and morphology.
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