This service exclusively searches for literature that cites resources. Please be aware that the total number of searchable documents is limited to those containing RRIDs and does not include all open-access literature.
Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of protein products can only be annotated computationally. If computational predictions are to be relied upon, it is crucial that the accuracy of these methods be high. Here we report the results from the first large-scale community-based critical assessment of protein function annotation (CAFA) experiment. Fifty-four methods representing the state of the art for protein function prediction were evaluated on a target set of 866 proteins from 11 organisms. Two findings stand out: (i) today's best protein function prediction algorithms substantially outperform widely used first-generation methods, with large gains on all types of targets; and (ii) although the top methods perform well enough to guide experiments, there is considerable need for improvement of currently available tools.
Epileptic encephalopathies are severe seizure disorders accompanied by intellectual disability. Whole-exome sequencing technology has enabled the discovery of genetic mutations responsible for a wide range of diseases, and severe epilepsy and neurodevelopmental diseases are often associated with rare de novo mutations. We identified a novel de novo frameshift mutation in the PPP3CA gene encoding calcium-dependent protein phosphatase (calcineurin) catalytic subunit A (c.1255_1256del, p.Ser419Cysfs*31) in an 11.5-mo-old female with early-onset refractory epilepsy and developmental delay. This finding expands the list of PPP3CA mutations associated with early-onset severe neurodevelopmental disease with seizures and provides further details on clinical features.
Mycobacterium tuberculosis (MTB) Beijing strains have caused a great concern because of their rapid emergence and increasing prevalence in worldwide regions. Great efforts have been made to investigate the pathogenic characteristics of Beijing strains such as hypervirulence, drug resistance and favoring transmission. Phylogenetically, MTB Beijing family was divided into modern and ancient sublineages. Modern Beijing strains displayed enhanced virulence and higher prevalence when compared with ancient Beijing strains, but the genetic basis for this difference remains unclear. In this study, by analyzing previously published sequencing data of 1082 MTB Beijing isolates, we determined the genetic changes that were commonly present in modern Beijing strains but absent in ancient Beijing strains. These changes include 44 single-nucleotide polymorphisms (SNPs) and two short genomic deletions. Through bioinformatics analysis, we demonstrated that these genetic changes had high probability of functional effects. For example, 4 genes were frameshifted due to premature stop mutation or genomic deletions, 19 nonsynonymous SNPs located in conservative codons, and there is a significant enrichment in regulatory network for all nonsynonymous mutations. Besides, three SNPs located in promoter regions were verified to alter downstream gene expressions. Our study precisely defined the genetic features of modern Beijing strains and provided interesting clues for future researches to elucidate the mechanisms that underlie this sublineage's successful expansion. These findings from the analysis of the modern Beijing sublineage could provide us a model to understand the dynamics of pathogenicity of MTB.
The identity and degree of heterogeneity of glial progenitors and their contributions to brain tumor malignancy remain elusive. By applying lineage-targeted single-cell transcriptomics, we uncover an unanticipated diversity of glial progenitor pools with unique molecular identities in developing brain. Our analysis identifies distinct transitional intermediate states and their divergent developmental trajectories in astroglial and oligodendroglial lineages. Moreover, intersectional analysis uncovers analogous intermediate progenitors during brain tumorigenesis, wherein oligodendrocyte-progenitor intermediates are abundant, hyper-proliferative, and progressively reprogrammed toward a stem-like state susceptible to further malignant transformation. Similar actively cycling intermediate progenitors are prominent components in human gliomas with distinct driver mutations. We further unveil lineage-driving networks underlying glial fate specification and identify Zfp36l1 as necessary for oligodendrocyte-astrocyte lineage transition and glioma growth. Together, our results resolve the dynamic repertoire of common and divergent glial progenitors during development and tumorigenesis and highlight Zfp36l1 as a molecular nexus for balancing glial cell-fate decision and controlling gliomagenesis.
Transcriptome analysis has played an essential role for revealing gene expression and the complexity of regulations at transcriptional level. RNA-seq is a powerful tool for transcriptome profiling, which uses deep-sequencing technologies to directly determine the cDNA sequence. Here, we utilized RNA-seq to explore the transcriptome of Mycobacteriummarinum (M. marinum), which is a useful model to study the pathogenesis of Mycobacterium tuberculosis (Mtb). Two profiles of exponential and early stationary phase cultures were generated after a physical ribosome RNA removal step. We systematically described the transcriptome and analyzed the functions for the differentiated expressed genes between the two phases. Furthermore, we predicted 360 operons throughout the whole genome, and 13 out of 17 randomly selected operons were validated by qRT-PCR. In general, our study has primarily uncovered M. marinum transcriptome, which could help to gain a better understanding of the regulation system in Mtb that underlines disease pathogenesis.
Persistent pulmonary hypertension of the newborn (PPHN) is a severe clinical problem among neonatal intensive care unit (NICU) patients. The genetic pathogenesis of PPHN is unclear. Only a few genetic polymorphisms have been identified in infants with PPHN. Our study aimed to investigate the potential genetic etiology of PPHN.
An early and accurate evaluation of the risk of bronchopulmonary dysplasia (BPD) in premature infants is pivotal in implementing preventive strategies. The risk prediction models nowadays for BPD risk that included only clinical factors but without genetic factors are either too complex without practicability or provide poor-to-moderate discrimination. We aim to identify the role of genetic factors in BPD risk prediction early and accurately.
Human brain development is a complex process involving neural proliferation, differentiation, and migration that are directed by many essential cellular factors and drivers. Here, using the NetBID2 algorithm and developing human brain RNA sequencing dataset, we identify synaptotagmin-like 3 (SYTL3) as one of the top drivers of early human brain development. Interestingly, SYTL3 exhibits high activity but low expression in both early developmental human cortex and human embryonic stem cell (hESC)-derived neurons. Knockout of SYTL3 (SYTL3-KO) in human neurons or knockdown of Sytl3 in embryonic mouse cortex markedly promotes neuronal migration. SYTL3-KO causes an abnormal distribution of deep-layer neurons in brain organoids and reduces presynaptic neurotransmitter release in hESC-derived neurons. We further demonstrate that SYTL3-KO-accelerated neuronal migration is modulated by high expression of matrix metalloproteinases. Together, based on bioinformatics and biological experiments, we identify SYTL3 as a regulator of cortical neuronal migration in human and mouse developing brains.
Many signaling and other genes known as "hidden" drivers may not be genetically or epigenetically altered or differentially expressed at the mRNA or protein levels, but, rather, drive a phenotype such as tumorigenesis via post-translational modification or other mechanisms. However, conventional approaches based on genomics or differential expression are limited in exposing such hidden drivers. Here, we present a comprehensive algorithm and toolkit NetBID2 (data-driven network-based Bayesian inference of drivers, version 2), which reverse-engineers context-specific interactomes and integrates network activity inferred from large-scale multi-omics data, empowering the identification of hidden drivers that could not be detected by traditional analyses. NetBID2 has substantially re-engineered the previous prototype version by providing versatile data visualization and sophisticated statistical analyses, which strongly facilitate researchers for result interpretation through end-to-end multi-omics data analysis. We demonstrate the power of NetBID2 using three hidden driver examples. We deploy NetBID2 Viewer, Runner, and Cloud apps with 145 context-specific gene regulatory and signaling networks across normal tissues and paediatric and adult cancers to facilitate end-to-end analysis, real-time interactive visualization and cloud-based data sharing. NetBID2 is freely available at https://jyyulab.github.io/NetBID .
Mutations in the insulin receptor (INSR) gene are responsible for Donohue syndrome (DS) and Rabson-Mendenhall syndrome (RMS). Insulin resistance is a feature of both diseases. Our patient was a Chinese neonate suffering from abnormal glucose homeostasis, hyperinsulinemia, dry skin, heavy hair, growth retardation and an elevated testosterone level. To search for candidate point mutations, small insertions or deletions and copy number variants, 2742 inherited disease-gene panel sequencing was performed. One pathogenic mutation (c.3355C>T, p.Arg1119Trp) and a novel 2.43Kb deletion (chr19:7150507-7152938) in INSR were found. The patient was diagnosed as RMS. Sanger sequencing and real-time quantitative polymerase chain reaction (PCR) confirmed the missense variant and microdeletion, respectively. We therefore supposed that these variants were candidate mutations in this case. We report a novel 2.43Kb deletion in INSR gene and provide further proof of the power of next generation sequencing in rare disease diagnosis.
The microenvironment of postpartum mammary gland involution (PMI) has been linked to the increased risk of breast cancer and poor outcome of patients. Nevertheless the mechanism underlying regulates the microenvironment remains largely unknown. MUC1, which is abnormally overexpressed in most breast cancer, is physiologically expressed in PMI. Using MUC1 cytoplasm domain (MUC1-CD) transgenic mice, we reveal that the overexpression of MUC1-CD in mammary epithelial cells increases M2 type macrophage infiltration in PMI. By sustain activating p50, MUC1 upregulates M2 macrophage chemo-attractants and the anti-apoptotic protein Bcl-xL. Because of the tumor promotional microenvironments and reduced apoptosis, MUC1-CD delays PMI process and results in atypical phenotype in multiparous mice mammary. This finding is further supported by the positive association between the expression of MUC1 and p50 in Luminal A and Luminal B subtypes through analyzing breast cancer databases. Taken together, our study demonstrates that MUC1-CD plays an important role in regulating microenvironment of PMI and promoting postpartum mammary tumorigenicity, providing novel prevention and treatment strategies against postpartum breast cancer.
Disruptive mutations in chromatin remodeler CHD8 cause autism spectrum disorders, exhibiting widespread white matter abnormalities; however, the underlying mechanisms remain elusive. We show that cell-type specific Chd8 deletion in oligodendrocyte progenitors, but not in neurons, results in myelination defects, revealing a cell-intrinsic dependence on CHD8 for oligodendrocyte lineage development, myelination and post-injury remyelination. CHD8 activates expression of BRG1-associated SWI/SNF complexes that in turn activate CHD7, thus initiating a successive chromatin remodeling cascade that orchestrates oligodendrocyte lineage progression. Genomic occupancy analyses reveal that CHD8 establishes an accessible chromatin landscape, and recruits MLL/KMT2 histone methyltransferase complexes distinctively around proximal promoters to promote oligodendrocyte differentiation. Inhibition of histone demethylase activity partially rescues myelination defects of CHD8-deficient mutants. Our data indicate that CHD8 exhibits a dual function through inducing a cascade of chromatin reprogramming and recruiting H3K4 histone methyltransferases to establish oligodendrocyte identity, suggesting potential strategies of therapeutic intervention for CHD8-associated white matter defects.
Pathway or gene set over-representation analysis (ORA) has become a routine task in functional genomics studies. However, currently widely used ORA tools employ statistical methods such as Fisher's exact test that reduce a pathway into a list of genes, ignoring the constitutive functional non-equivalent roles of genes and the complex gene-gene interactions. Here, we develop a novel method named LEGO (functional Link Enrichment of Gene Ontology or gene sets) that takes into consideration these two types of information by incorporating network-based gene weights in ORA analysis. In three benchmarks, LEGO achieves better performance than Fisher and three other network-based methods. To further evaluate LEGO's usefulness, we compare LEGO with five gene expression-based and three pathway topology-based methods using a benchmark of 34 disease gene expression datasets compiled by a recent publication, and show that LEGO is among the top-ranked methods in terms of both sensitivity and prioritization for detecting target KEGG pathways. In addition, we develop a cluster-and-filter approach to reduce the redundancy among the enriched gene sets, making the results more interpretable to biologists. Finally, we apply LEGO to two lists of autism genes, and identify relevant gene sets to autism that could not be found by Fisher.
Developmental disorders (DDs) are early onset disorders affecting 5%-10% of children worldwide. Chromosomal microarray analysis detecting CNVs is currently recommended as the first-tier test for DD diagnosis. However, this analysis omits a high percentage of disease-causing single nucleotide variations (SNVs) that warrant further sequencing. Currently, next-generation sequencing can be used in clinical scenarios detecting CNVs, and the use of exome sequencing in the DD cohort ahead of the microarray test has not been evaluated.
Malformation of cortical development (MCD) includes a variety of developmental disorders that are common causes of neurodevelopmental delay and epilepsy. Most recently, clinical studies found that patients carrying KIF5C mutations present early-onset MCD; however, the underlying mechanisms remain elusive.
Rubinstein-Taybi syndrome (RSTS) is a rare genetic disease characterized by broad thumbs and halluces, facial dysmorphisms, short stature, and intellectual disability. RSTS is mainly caused by de novo variants in epigenetics-associated gene, CREBBP. To date, there is no cohort study of CREBBP variants in Chinese RSTS patients.
Accelerated evolution of regulatory sequence can alter the expression pattern of target genes, and cause phenotypic changes. In this study, we used DNase I hypersensitive sites (DHSs) to annotate putative regulatory sequences in the human genome, and conducted a genome-wide analysis of the effects of accelerated evolution on regulatory sequences. Working under the assumption that local ancient repeat elements of DHSs are under neutral evolution, we discovered that ∼0.44% of DHSs are under accelerated evolution (ace-DHSs). We found that ace-DHSs tend to be more active than background DHSs, and are strongly associated with epigenetic marks of active transcription. The target genes of ace-DHSs are significantly enriched in neuron-related functions, and their expression levels are positively selected in the human brain. Thus, these lines of evidences strongly suggest that accelerated evolution on regulatory sequences plays important role in the evolution of human-specific phenotypes.
A key step in network analysis is to partition a complex network into dense modules. Currently, modularity is one of the most popular benefit functions used to partition network modules. However, recent studies suggested that it has an inherent limitation in detecting dense network modules. In this study, we observed that despite the limitation, modularity has the advantage of preserving the primary network structure of the undetected modules. Thus, we have developed a simple iterative Network Partition (iNP) algorithm to partition a network. The iNP algorithm provides a general framework in which any modularity-based algorithm can be implemented in the network partition step. Here, we tested iNP with three modularity-based algorithms: multi-step greedy (MSG), spectral clustering and Qcut. Compared with the original three methods, iNP achieved a significant improvement in the quality of network partition in a benchmark study with simulated networks, identified more modules with significantly better enrichment of functionally related genes in both yeast protein complex network and breast cancer gene co-expression network, and discovered more cancer-specific modules in the cancer gene co-expression network. As such, iNP should have a broad application as a general method to assist in the analysis of biological networks.
Welcome to the FDI Lab - SciCrunch.org Resources search. From here you can search through a compilation of resources used by FDI Lab - SciCrunch.org and see how data is organized within our community.
You are currently on the Community Resources tab looking through categories and sources that FDI Lab - SciCrunch.org has compiled. You can navigate through those categories from here or change to a different tab to execute your search through. Each tab gives a different perspective on data.
If you have an account on FDI Lab - SciCrunch.org then you can log in from here to get additional features in FDI Lab - SciCrunch.org such as Collections, Saved Searches, and managing Resources.
Here is the search term that is being executed, you can type in anything you want to search for. Some tips to help searching:
You can save any searches you perform for quick access to later from here.
We recognized your search term and included synonyms and inferred terms along side your term to help get the data you are looking for.
If you are logged into FDI Lab - SciCrunch.org you can add data records to your collections to create custom spreadsheets across multiple sources of data.
Here are the facets that you can filter your papers by.
From here we'll present any options for the literature, such as exporting your current results.
If you have any further questions please check out our FAQs Page to ask questions and see our tutorials. Click this button to view this tutorial again.
Year:
Count: