Biology subjects

Shim, H.

Publications and source records attributed to Shim, H..

28 records · Page 2Linked to original sources

bioRxiv2021

NanoSplicer: Accurate identification of splice junctions using Oxford Nanopore sequencing

MotivationLong read sequencing methods have considerable advantages for characterising RNA isoforms. Oxford nanopore sequencing records changes in electrical current when nucleic acid traverses through a pore. However, basecalling of this raw signal (known as a squiggle) is error prone, making it challenging to accurately identify splice junctions. Existing strategies include utilising matched short-read data and/or annotated splice junctions to correct nanopore reads but add expense or limit junctions to known (incomplete) annotations. Therefore, a method that could accurately identify splice junctions solely from nanopore data would have numerous advantages. ResultsWe developed "NanoSplicer" to identify splice junctions using raw nanopore signal (squiggles). For each splice junction the observed squiggle is compared to candidate squiggles representing potential junctions to identify the correct candidate. Measuring squiggle similarity enables us to compute the probability of each candidate junction and find the most likely one. We tested our method using 1. synthetic mRNAs with known splice junctions 2. biological mRNAs from a lung-cancer cell-line. The results from both datasets demonstrate NanoSplicer improves splice junction identification, especially when the basecalling error rate near the splice junction is elevated. Our method is implemented in the software package NanoSplicer, available at https://github.com/shimlab/NanoSplicer.

bioinformatics↗

bioRxiv2021

Diverse ATPase proteins in mobilomes constitute a large potential sink for prokaryotic host ATP

Prokaryote mobilome genomes rely on host machineries for survival and replication. Given that mobile genetic elements (MGEs) derive their energy from host cells, we investigated the diversity of ATP-utilizing proteins in MGE genomes to determine whether they might be associated with proteins that could suppress related host proteins that consume host energy. A comprehensive search of 353 huge phage genomes revealed that up to 9% of the proteins have ATPase domains. For example, ATPase proteins constitute ~3% of the genomes of Lak phages with ~550 kbp genomes that occur in the microbiomes of humans and other animals. Statistical analysis shows the number of ATPase proteins increases linearly with genome length, consistent with a large sink for host ATP during replication of megaphages. Using metagenomic data from diverse environments, we found 505 mobilome proteins with ATPase domains fused to diverse functional domains. Among these composite ATPase proteins, 61.6% have known functional domains that could contribute to host energy diversion during the mobilome life cycle. As many have domains that are known to interact with nucleic acids and proteins, we infer that numerous ATPase proteins are used during replication and for protection from host immune systems. We found a set of uncharacterized ATPase proteins with nuclease and protease activities, displaying unique domain architectures that are energy intensive based on the presence of multiple ATPase domains. In many cases, these composite ATPase proteins genomically co-localize with small proteins in genomic contexts that are reminiscent of toxin-antitoxin systems and phage helicase-antibacterial helicase systems. Small proteins that function as inhibitors may be a common strategy for control of cellular processes, thus could inspire the development of new nucleic acid and protein manipulation tools, with diverse biotechnological applications.

microbiology↗

bioRxiv2021

Genomics-informed insights into microbial degradation of N,N-dimethylformamide

Effective degradation of N,N-Dimethylformamide (DMF), an important industrial waste product, is challenging as only few bacterial isolates are known to be capable of degrading DMF. Aerobic remediation of DMF has typically been used, whereas anoxic remediation attempts are recently made, using nitrate as one electron acceptor, and ideally include methane as a byproduct. Here, we analyzed 20,762 complete genomes and 28 constructed draft genomes for the genes associated with DMF degradation. We identified 952 genomes that harbor genes involved in DMF degradation, expanding the known diversity of prokaryotes with these metabolic capabilities. Our findings suggest acquisition of DMF-degrading gene via plasmids are important in the order Rhizobiales and genus Paracoccus, but not in most other lineages. Degradation pathway analysis reveals that most putative DMF degraders using aerobic Pathway I will accumulate methylamine intermediate, while members of Paracoccus, Rhodococcus, Achromobacter, and Pseudomonas could potentially mineralize DMF completely under aerobic conditions. The aerobic DMF degradation via Pathway II is more common than thought and is primarily present in -and {beta}-Proteobacteria and Actinobacteria. Most putative DMF degraders could grow with nitrate anaerobically (Pathway III), however, genes for the use of methyl-CoM to produce methane were not found. These analyses suggest that microbial consortia could be more advantageous in DMF degradation than pure culture, particularly for methane production under the anaerobic condition. The identified genomes and plasmids form an important foundation for optimizing bioremediation of DMF-containing wastewaters. ImportanceDMF is extensively used as a solvent in industries, and is classified as a probable carcinogen. DMF is a refractory compound resistant to degradation, and until now, only few bacterial isolates have been reported to degrade DMF. To achieve effective microbial degradation of DMF from wastewater, it is necessary to identify genomic diversity with the potential to degrade DMF and characterize the genes involved in two aerobic degradation pathways and potential anaerobic degradation for methane production. A wide diversity of organisms has the potential to degrade DMF. Plasmid-mediated degradation of DMF is important for Rhizobiales and Paracoccus. Most DMF degraders could grow anaerobically with nitrate as electron acceptor, while co-cultures are required to complete intermediate methanogenesis for methane production. This is the first genomics-based global investigation into DMF degradation pathways. The genomic database generated by this study provides an important foundation for the bioremediation of DMF in industrial waste waters. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=123 SRC="FIGDIR/small/435917v2_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@e46e1dorg.highwire.dtl.DTLVardef@f93887org.highwire.dtl.DTLVardef@1ee5ce8org.highwire.dtl.DTLVardef@15a529_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2021

Mechanistic insights into the success of xenobiotic degraders resolved from metagenomes of microbial enrichment cultures

Even though microbial communities can be more effective at degrading xenobiotics than cultured micro-organisms, yet little is known about the microbial strategies that underpin xenobiotic biodegradation by microbial communities. Here, we employ metagenomic community sequencing to explore the mechanisms that drive the development of 49 xenobiotic-degrading microbial communities, which were enriched from 7 contaminated soils or sediments with a range of xenobiotic compounds. We show that multiple microbial strategies likely drive the development of xenobiotic degrading communities, notably (i) presence of genes encoding catabolic enzymes to degrade xenobiotics; (ii) presence of genes encoding efflux pumps; (iii) auxiliary catabolic genes on plasmids; and (iv) positive interactions dominate microbial communities with efficient degradation. Overall, the integrated analyses of microbial ecological strategies advance our understanding of microbial processes driving the biodegradation of xenobiotics and promote the design of bioremediation systems.

microbiology↗

bioRxiv2020

A scalable method for identifying recombinants from unaligned sequences

Recombination is a fundamental process in molecular evolution, and the identification of recombinant sequences is of major interest for biologists. However, current methods for detecting recombinants only work for aligned sequences, often require a reference panel, and do not scale well to large datasets. Thus they are not suitable for the analyses of highly diverse genes, such as the var genes of the malaria parasite Plasmodium falciparum, which are known to diversify primarily through recombination. We introduce an algorithm to detect recombinant sequences from an unaligned dataset. Our approach can effectively handle thousands of sequences without the need of an alignment or a reference panel, offering a general tool suitable for the analysis of many different types of sequences. We demonstrate the effectiveness of our algorithm through extensive numerical simulations; in particular, it maintains its accuracy in the presence of insertions and deletions. We apply our algorithm to a dataset of 17,335 DBL types in var genes from Ghana, enabling the comparison between recombinant and non-recombinant types for the first time. We observe that sequences belonging to the same ups type or DBL subclass recombine amongst themselves more frequently, and that non-recombinant DBL types are more conserved than recombinant ones. Author summaryRecombination is a fundamental process in molecular evolution where two genes exchange genetic material, diversifying the genes. It is important to properly model this process when reconstructing evolutionary history, and to do so we need to be able to identify recombinant genes. In this manuscript, we develop a method for this which can be applied to scenarios where current methods often fail, such as where genes are very diverse. We specifically focus on detecting recombinants in the var genes of the malaria parasite Plasmodium falciparum. These genes influence the length and severity of malaria infection, and therefore their study is critical to the treatment and prevention of malaria. They are also highly diverse, primarily because of recombination. Our analysis of genes from a cross-sectional study in Ghana study show fundamental relations between the patterns and prevalence of recombination in these genes and other important biological categorisations.

evolutionary biology↗

bioRxiv2020

Characteristics of fMRI responses to visual stimulation in anesthetized vs. awake mice

AbstractThe functional characteristics of the mouse visual system have not previously been well explored using fMRI. In this research, we examined 9.4 T BOLD fMRI responses to visual stimuli of varying pulse durations (1 - 50 ms) and temporal frequencies (1 - 10 Hz) under ketamine and xylazine anesthesia, and compared fMRI responses of anesthetized and awake mice. Under anesthesia, significant positive BOLD responses were detected bilaterally in the major structures of the visual pathways, including the dorsal lateral geniculate nuclei, superior colliculus, lateral posterior nucleus of thalamus, primary visual area, and higher-order visual area. BOLD responses increased slightly with pulse duration, were maximal at 3 - 5 Hz stimulation, and significantly decreased at 10 Hz, which were all consistent with previous neurophysiological findings. When the mice were awake, the BOLD fMRI response was faster in all active regions and stronger in the subcortical areas compared with the anesthesia condition. In the V1, the BOLD response was biphasic for 5 Hz stimulation and negative for 10 Hz stimulation under wakefulness, whereas prolonged positive BOLD responses were observed at both frequencies under anesthesia. Unexpected activation was detected in the extrastriate postrhinal area and non-visual subiculum complex under anesthesia, but not under wakefulness. Widespread positive BOLD activity under anesthesia likely results from the disinhibition and sensitization of excitatory neurons induced by ketamine. Overall, fMRI can be a viable tool for mapping brain-wide functional networks.

neuroscience↗

bioRxiv2020

McSplicer: a probabilistic model for estimating splice site usage from RNA-seq data

Alternative splicing removes intronic sequences from transcripts in alternative ways to produce different forms (isoforms) of mature mRNA. The composition of expressed transcripts and their alternative forms give specific functionalities to cells in a particular condition or developmental stage. In addition, a large fraction of human disease mutations affect splicing and lead to aberrant mRNA and protein products. Current methods that interrogate the transcriptome based on RNA-seq either suffer from short read length when trying to infer full-length transcripts, or are restricted to predefined units of alternative splicing that they quantify from local read evidence. Instead of attempting to quantify individual outcomes of the splicing process such as local splicing events or full-length transcripts, we propose to quantify alternative splicing using a simplified probabilistic model of the underlying splicing process. Our model is based on the usage of individual splice sites and can generate arbitrarily complex types of splicing patterns. In our method, McSplicer, we estimate the parameters of our model using all read data at once and we demonstrate in our experiments that this yields more accurate estimates compared to competing methods. Our model is able to describe multiple effects of splicing mutations using few, easy to interpret parameters, as we illustrate in an experiment on RNA-seq data from autism spectrum disorder patients. McSplicer is implemented in Python and available as open-source at https://github.com/canzarlab/McSplicer.

bioinformatics↗

bioRxiv2019

Targeting the PI5P4K lipid kinase family in cancer using novel covalent inhibitors

The PI5P4Ks have been demonstrated to be important for cancer cell proliferation and other diseases. However, the therapeutic potential of targeting these kinases is understudied due to a lack of potent, specific small molecules available. Here we present the discovery and characterization of a novel pan-PI5P4K inhibitor, THZ-P1-2, that covalently targets cysteines on a disordered loop in PI5P4K/{beta}/{gamma}. THZ-P1-2 demonstrates cellular on-target engagement with limited off-targets across the kinome. AML/ALL cell lines were sensitive to THZ-P1-2, consistent with PI5P4Ks reported role in leukemogenesis. THZ-P1-2 causes autophagosome clearance defects and upregulation in TFEB nuclear localization and target genes, disrupting autophagy in a covalent-dependent manner and phenocopying the effects of PI5P4K genetic deletion. Our studies demonstrate that PI5P4Ks are tractable targets, with THZ-P1-2 as a useful tool to further interrogate the therapeutic potential of PI5P4K inhibition and inform drug discovery campaigns for these lipid kinases in cancer metabolism and other autophagy-dependent disorders.

cancer biology↗

bioRxiv2018

circuitSNPs: Predicting genetic effects using a Neural Network to model regulatory modules of DNase-seq footprints

MotivationIdentifying and characterizing the function of non coding regions in the genome, and the genetic variants disrupting gene regulation, is a challenging question in genetics. Through the use of high throughput experimental assays that provide information about the chromatin state within a cell, coupled with modern computational approaches, much progress has been made towards this goal, yet we still lack a comprehensive characterization of the regulatory grammar. We propose a new method that combines sequence and chromatin accessibility information through a neural network framework with the goal of determining and annotating the effect of genetic variants on regulation of chromatin accessibility and gene transcription. Importantly, our new approach can consider multiple combinations of transcription factors binding at the same location when assessing the functional impact of non-coding genetic variation.\n\nResultsOur method, circuitSNPs, generates predictions describing the functional effect of genetic variants on local chromatin accessibility. Further, we demonstrate that circuitSNPs not only performs better than other variant annotation tools, but also retains the causal motifs / transcription factors that drive the predicted regulatory effect.\n\nContactfluca@wayne.edu, rpique@wayne.edu\n\nAvailabilityhttp://github.com/piquelab/circuitSNPs

bioinformatics↗

bioRxiv2017

WheatNet: A genome-scale functional network for hexaploid bread wheat, Triticum aestivum

Gene networks provide a system-level overview of genetic organizations and enable the dissection of functional modules underlying complex traits. Here we report the generation of WheatNet, the first genome-scale functional network for T. aestivum and a companion web server (www.inetbio.org/wheatnet). WheatNet was constructed by integrating 20 distinct genomics datasets, including 156,000 wheat-specific co-expression links mined from 1,929 microarray data. A unique feature of WheatNet is that each network node represents either a single gene or a group of genes. We computationally partitioned gene groups mimicking homeologous genes by clustering 99,386 wheat genes, resulting in 20,248 gene groups comprising 63,401 genes and 35,985 individual genes. Thus, WheatNet was constructed using 56,233 nodes, and the final integrated network has 20,230 nodes and 567,000 edges. The edge information of the integrated WheatNet and all 20 component networks are available for download.

genetics↗