Biology subjects

Weng, C.

Publications and source records attributed to Weng, C..

24 records · Page 2Linked to original sources

bioRxiv2019

CDE-1 suppresses the production of risiRNA by coupling polyuridylation and degradation of 26S rRNA

Antisense ribosomal siRNAs (risiRNAs) downregulate pre-rRNAs through the nuclear RNAi pathway in Caenorhabditis elegans. However, the biogenesis and regulation of risiRNAs remain obscure. Previously, we showed that 26S rRNAs are uridylated at the 3-ends by an unknown terminal polyuridylation polymerase before the rRNAs are degraded by a 3 to 5 exoribonuclease SUSI-1(ceDIS3L2). There are three polyuridylation polymerases, CDE-1, PUP-2, and PUP-3, in C. elegans. Here, we found that CDE-1 is specifically involved in suppressing risiRNA production. CDE-1 localizes to perinuclear granules in the germline and uridylates both Argonaute-associated 22G-RNAs and 26S rRNAs at the 3-ends. Immunoprecipitation followed by mass spectrometry (IP-MS) revealed that CDE-1 interacts with SUSI-1(ceDIS3L2). Consistent with those results, both CDE-1 and SUSI-1(ceDIS3L2) are required for the inheritance of RNAi. Therefore, this work identified a rRNA surveillance machinery of rRNAs that couples terminal polyuridylation and degradation.

molecular biology↗

bioRxiv2019

Phen2Gene: Rapid Phenotype-Driven Gene Prioritization for Rare Diseases

Human Phenotype Ontology (HPO) terms are increasingly used in diagnostic settings to aid in the characterization of patient phenotypes. The HPO annotation database is updated frequently and can provide detailed phenotype knowledge on various human diseases, and many HPO terms are now mapped to candidate causal genes with binary relationships. To further improve the genetic diagnosis of rare diseases, we incorporated these HPO annotations, gene-disease databases, and gene-gene databases in a probabilistic model to build a novel HPO-driven gene prioritization tool, Phen2Gene. Phen2Gene accesses a database built upon this information called the HPO2Gene Knowledgebase (H2GKB), which provides weighted and ranked gene lists for every HPO term. Phen2Gene is then able to access the H2GKB for patient-specific lists of HPO terms or PhenoPackets descriptions supported by GA4GH (http://phenopackets.org/), calculate a prioritized gene list based on a probabilistic model, and output gene-disease relationships with great accuracy. Phen2Gene outperforms existing gene prioritization tools in speed, and acts as a real-time phenotype driven gene prioritization tool to aid the clinical diagnosis of rare undiagnosed diseases. In addition to a command line tool released under the MIT license (https://github.com/WGLab/Phen2Gene), we also developed a web server and web service (https://phen2gene.wglab.org/) for running the tool via web interface or RESTful API queries. Finally, we have curated a large amount of benchmarking data for phenotype-to-gene tools involving 197 patients across 76 scientific articles and 85 patients de-identified HPO term data from CHOP.

bioinformatics↗

bioRxiv2019

TWO-SIGMA: a novel TWO-component SInGle cell Model-based Association method for single-cell RNA-seq data

In this paper, we develop TWO-SIGMA, a TWO-component SInGle cell Model-based Association method for differential expression (DE) analyses in single-cell RNA-seq (scRNA-seq) data. The first component models the probability of "drop-out" with a mixed-effects logistic regression model and the second component models the (conditional) mean expression with a mixed-effects negative binomial regression model. TWO-SIGMA is extremely flexible in that it: (i) does not require a log-transformation of the outcome, (ii) allows for overdispersed and zero-inflated counts, (iii) accommodates a correlation structure between cells from the same biological sample via random effect terms, (iv) can analyze unbalanced designs (in which the number of cells does not need to be identical for all samples), (v) can control for additional sample-level and cell-level covariates including batch effects, (vi) provides interpretable effect size estimates, and (vii) enables general tests of DE beyond two-group comparisons. To our knowledge, TWO-SIGMA is the only method for analyzing scRNA-seq data that can simultaneously accomplish each of these features. Simulations studies show that TWO-SIGMA outperforms alternative regression-based approaches in both type-I error control and power enhancement when the data contains even moderate within-sample correlation. A real data analysis using pancreas islet single-cells exhibits the flexibility of TWO-SIGMA and demonstrates that incorrectly failing to include random effect terms can have dramatic impacts on scientific conclusions. TWO-SIGMA is implemented in the R package twosigma available at https://github.com/edvanburen/twosigma.

genomics↗

bioRxiv2018

Differential phase partition of a PICS complex is required for piRNA processing and chromosome segregation in C. elegans

Piwi-interacting RNAs (piRNAs) play significant roles in suppressing transposons and non-self nucleic acids, maintaining genome integrity, defending against viral infections, and are essential for fertility in a variety of organisms. In C. elegans, most piRNA precursors are transcribed by RNA polymerase II in the nucleus and are subjected to a number of processing and maturation steps. However, the biogenesis of piRNAs are still not fully understood. We used functional proteomics to study piRNA biogenesis in C. elegans and identified a PICS complex that is required for piRNA processing and chromosome segregation. The PICS complex contains two known piRNA biogenesis factors TOFU-6 and PID-1, and three new proteins PICS-1, TOST-1, and ERH-2, which exhibit dynamic localization among different subcellular compartments. In the germline of gravid animals, the PICS complex contains TOFU-6/PICS-1/ERH-2/PID-1 and largely concentrate at the perinuclear granule zone and engages in piRNA processing. During early embryogenesis, the TOFU-6/PICS-1/ERH-2/TOST-1 complex accumulates in the nucleus and play essential roles in chromosome segregation and cell division. Interestingly, the function of these factors in mediating chromosome segregation is independent of piRNA production. Therefore, we speculate that a differential phase partition of PICS factors may help cells to coordinate distinct cellular processes.

genetics↗

bioRxiv2018

The USTC complex co-opts an ancient machinery to drive piRNA transcription in C. elegans

Piwi-interacting RNAs (piRNAs) engage Piwi proteins to suppress transposons and non-self nucleic acids, maintain genome integrity, and are essential for fertility in a variety of organisms. In C. elegans most piRNA precursors are transcribed from two genomic clusters that contain thousands of individual piRNA transcription units. While a few genes have been shown to be required for piRNA biogenesis the mechanism of piRNA transcription remains elusive. Here we used functional proteomics approaches to identify an upstream sequence transcription complex (USTC) that is essential for piRNA biogenesis. The USTC complex contains PRDE-1, TOFU-4, TOFU-5 and SNPC-4. The USTC complex form a unique piRNA foci in germline nuclei and coat the piRNA cluster genomic loci. USTC factors associate with the Ruby motif just upstream of type I piRNA genes. USTC factors are also mutually dependent for binding to the piRNA clusters and to form the piRNA foci. Interestingly, USTC components bind differentially to piRNAs in the clusters and other non-coding RNA genes. These results reveal USTC as a striking example of the repurposing of a general transcription factor complex to aid in genome defence against transposons.

developmental biology↗

bioRxiv2017

A cytoplasmic Argonaute protein promotes the inheritance of RNAi

RNAi-elicited gene silencing is heritable and can persist for multiple generations after its initial induction in C. elegans. However, the mechanism by which parental-acquired trait-specific information from RNAi is inherited by the progenies is not fully understood. Here, we identified a cytoplasmic Argonaute protein, WAGO-4, necessary for the inheritance of RNAi. WAGO-4 exhibits asymmetrical translocation to the germline during early embryogenesis, accumulates at the perinuclear foci in the germline, and is required for the inheritance of exogenous RNAi targeting both germline- and soma-expressed genes. WAGO-4 binds to 22G-RNAs and their mRNA targets. Interestingly, WAGO-4-associated endogenous 22G-RNAs target the same cohort of germline genes as CSR-1 and contain untemplated addition of uracil at the 3 ends. The poly(U) polymerase CDE-1 is required for the untemplated uridylation of 22G-RNAs and inheritance of RNAi. Therefore, we conclude that, in addition to the nuclear RNAi pathway, the cytoplasmic RNAi machinery also promotes RNAi inheritance.

genetics↗