Biology subjects

Gunsalus, K.

Publications and source records attributed to Gunsalus, K..

11 recordsLinked to original sources

bioRxiv2025

LGL-1 and the RhoGAP protein PAC-1 redundantly control polarization of the C. elegans embryonic epidermal epithelium

Apical-basal polarity is essential for epithelial organization and function and is established by conserved cortical polarity proteins. However, the requirements for canonical polarity factors vary between tissues and organisms. For example, the basolateral protein lethal giant larvae (Lgl) is essential for epithelial polarity in Drosophila but dispensable in C. elegans. To better define the epithelial polarity program in C. elegans, we performed a genome-wide RNAi screen for synthetic lethality with lgl-1. Combined loss of LGL-1 and the RhoGAP PAC-1 caused embryonic lethality due to elongation defects and epidermal rupture. Epidermal cells showed expansion of the apical domain and aPKC, accompanied by mislocalization of junctional proteins and LET-413Scribble. These defects indicate overactivity of apical polarity determinants. Consistently, partial inactivation of aPKC or CDC-42 suppressed lethality in pac-1; lgl-1 animals. Together, our results identify PAC-1 and LGL-1 as redundant inhibitors of apical polarity in the C. elegans embryonic epidermis and provide new insights into how conserved mechanisms are adapted across species.

developmental biology↗

bioRxiv2022

Omicron Spike protein has a positive electrostatic surface that promotes ACE2 recognition and antibody escape

High transmissibility is a hallmark of the Omicron variant of SARS-CoV-2. Understanding the molecular determinants of Omicrons transmissibility will impact development of intervention strategies. Here we map the electrostatic potential surface of the Spike protein to show that major SARS-CoV-2 variants have accumulated positive charges in solvent-exposed regions of the Spike protein, especially its ACE2-binding interface. Significantly, the Omicron Spike-ACE2 complex has complementary electrostatic surfaces. In contrast, interfaces between Omicron and neutralizing antibodies tend to have similar positively charged surfaces. Structural modeling demonstrates that the electrostatic property of Omicrons Spike receptor binding domain (S RBD) plays a role in enhancing ACE2 recognition and destabilizing Spike-antibody complexes. Collectively, our structural analysis implies that Omicron S RBD interaction interfaces have been optimized to simultaneously promote access to human ACE2 receptors and evade antibodies. These findings suggest that electrostatic interactions are a major contributing factor for increased Omicron transmissibility relative to other variants.

molecular biology↗

bioRxiv2021

The Caenorhabditis elegans TDRD5/7-like protein, LOTR-1, interacts with the helicase ZNFX-1 to balance epigenetic signals in the germline

LOTUS and Tudor domain containing proteins have critical roles in the germline. Proteins that contain these domains, such as Tejas/Tapas in Drosophila, help localize Vasa to the germ granules and facilitate piRNA-mediated transposon silencing. The homologous proteins in mammals, TDRD5 and TDRD7, are required during spermiogenesis. Until now, proteins containing both LOTUS and Tudor domains in Caenorhabditis elegans have remained elusive. Here we describe LOTR-1 (D1081.7), which derives its name from its LOTUS and Tudor domains. Interestingly, LOTR-1 docks next to P granules to colocalize with the broadly conserved Z-granule helicase, ZNFX-1. LOTR-1s Z-granule association requires its Tudor domain, but both LOTUS and Tudor deletions affect brood size when coupled with a knockdown of the Vasa homolog glh-1. In addition to interacting with the germ-granule components WAGO-1, PRG-1 and DEPS-1, we identified a Tudor-dependent association with ZNFX-1. Like znfx-1 mutants, lotr-1 mutants lose small RNAs from the 3 ends of WAGO and Mutator targets, reminiscent of the loss of piRNAs from the 3 ends of piRNA precursor transcripts in mouse Tdrd5 mutants. Our work suggests that LOTR-1 acts in a conserved mechanism that brings small RNA generating mechanisms towards the 3 ends of small RNA templates or precursors.

developmental biology↗

bioRxiv2021

Novel LOTUS-domain proteins are organizational hubs that recruit C. elegans Vasa to germ granules

We describe MIP-1 and MIP-2, novel paralogous C. elegans germ granule components that interact with the intrinsically disordered MEG-3 protein. These proteins promote P granule condensation, form granules independently of MEG-3 in the postembryonic germ line, and balance each other in regulating P granule growth and localization. MIP-1 and MIP-2 each contain two LOTUS domains and intrinsically disordered regions and form homo- and heterodimers. They bind and anchor the Vasa homolog GLH-1 within P granules and are jointly required for coalescence of MEG-3, GLH-1, and PGL proteins. Animals lacking MIP-1 and MIP-2 show temperature-sensitive embryonic lethality, sterility, and mortal germ lines. Germline phenotypes include defects in stem cell self-renewal, meiotic progression, and gamete differentiation. We propose that these proteins serve as scaffolds and organizing centers for ribonucleoprotein networks within P granules that help recruit and balance essential RNA processing machinery to regulate key developmental transitions in the germ line.

cell biology↗

bioRxiv2021

Structural modeling of the SARS-CoV-2 Spike/human ACE2 complex interface can identify high-affinity variants associated with increased transmissibility

The COVID-19 pandemic has triggered concerns about the emergence of more infectious and pathogenic viral strains. As a public health measure, efficient screening methods are needed to determine the functional effects of new sequence variants. Here we show that structural modeling of SARS-CoV-2 Spike protein binding to the human ACE2 receptor, the first step in host-cell entry, predicts many novel variant combinations with enhanced binding affinities. By focusing on natural variants at the Spike-hACE2 interface and assessing over 700 mutant complexes, our analysis reveals that high-affinity Spike mutations (including N440K, S443A, G476S, E484R, G502P) tend to cluster near known human ACE2 recognition sites (K31 and K353). These Spike regions are conformationally flexible, allowing certain mutations to optimize interface interaction energies. Although most human ACE2 variants tend to weaken binding affinity, they can interact with Spike mutations to generate high-affinity double mutant complexes, suggesting variation in individual susceptibility to infection. Applying structural analysis to highly transmissible variants, we find that circulating point mutations S447N, E484K and N501Y form high-affinity complexes (~40% more than wild-type). By combining predicted affinities and available antibody escape data, we show that fast-spreading viral variants exploit combinatorial mutations possessing both enhanced affinity and antibody resistance, including S447N/E484K, E484K/N501Y and K417T/E484K/N501Y. Thus, three-dimensional modeling of the Spike/hACE2 complex predicts changes in structure and binding affinity that correlate with transmissibility and therefore can help inform future intervention strategies.

molecular biology↗

bioRxiv2021

Pheniqs 2.0: accurate, high performance Bayesian decoding and confidence estimation for combinatorial barcode indexing

BackgroundSystems biology increasingly relies on deep sequencing with combinatorial index tags to associate biological sequences with their sample, cell, or molecule of origin. Accurate data interpretation depends on the ability to classify sequences based on correct decoding of these combinatorial barcodes. The probability of correct decoding is influenced by both sequence quality and the number and arrangement of barcodes. The rising complexity of experimental designs calls for a probability model that accounts for both sequencing errors and random noise, generalizes to multiple combinatorial tags, and can handle any barcoding scheme. The needs for reproducibility and community benchmark standards demand a peer-reviewed tool that preserves decoding quality scores and provides tunable control over classification confidence that balances precision and recall. Moreover, continuous improvements in sequencing throughput require a fast, parallelized and scalable implementation. ResultsWe developed a flexible, robustly engineered software that performs probabilistic decoding and supports arbitrarily complex barcoding designs. Pheniqs computes the full posterior decoding error probability of observed barcodes by consulting basecalling quality scores and prior distributions, and reports sequences and confidence scores in Sequence Alignment/Map (SAM) fields. The product of posteriors for multiple independent barcodes provides an overall confidence score for each read. Pheniqs achieves greater accuracy than minimum edit distance or simple maximum likelihood estimation, and it scales linearly with core count to enable the classification of >11 billion reads in 1h15m using <50 megabytes of memory. Pheniqs has been in production use for seven years in our genomics core facility. ConclusionsWe introduce a computationally efficient software that implements both probabilistic and minimum distance decoders and show that decoding barcodes using posterior probabilities is more accurate than available methods. Pheniqs allows fine-tuning of decoding sensitivity using intuitive confidence thresholds and is extensible with alternative decoders and new error models. Any arbitrary arrangement of barcodes is easily configured, enabling computation of combinatorial confidence scores for any barcoding strategy. An optimized multithreaded implementation assures that Pheniqs is faster and scales better with complex barcode sets than existing tools. Support for POSIX streams and multiple sequencing formats enables easy integration with automated analysis pipelines.

bioinformatics↗

bioRxiv2019

NASQAR: A web-based platform for High-throughput sequencing data analysis and visualization

BackgroundAs high-throughput sequencing applications continue to evolve, the rapid growth in quantity and variety of sequence-based data calls for the development of new software libraries and tools for data analysis and visualization. Often, effective use of these tools requires computational skills beyond those of many researchers. To ease this computational barrier, we have created a dynamic web-based platform, NASQAR (Nucleic Acid SeQuence Analysis Resource). ResultsNASQAR offers a collection of custom and publicly available open-source web applications that make extensive use of a variety of R packages to provide interactive data analysis and visualization. The platform is publicly accessible at http://nasqar.abudhabi.nyu.edu/. Open-source code is on GitHub at https://github.com/nasqar/NASQAR, and the system is also available as a Docker image at https://hub.docker.com/r/aymanm/nasqarall. NASQAR is a collaboration between the core bioinformatics teams of the NYU Abu Dhabi and NYU New York Centers for Genomics and Systems Biology. ConclusionsNASQAR empowers non-programming experts with a versatile and intuitive toolbox to easily and efficiently explore, analyze, and visualize their Transcriptomics data interactively. Popular tools for a variety of applications are currently available, including Transcriptome Data Preprocessing, RNA-seq Analysis (including Single-cell RNA-seq), Metagenomics, and Gene Enrichment.

bioinformatics↗

bioRxiv2019

BioSAILs: versatile workflow management for high-throughput data analysis

MotivationHigh-throughput analysis in the current era of systems biology encompasses a range of analysis workflows for different applications, such as transcriptomics, epigenetics, variant discovery, de novo genome assembly, etc. Many research institutes house a genomics core facility that is responsible for generating high-throughput sequencing data and often includes a core bioinformatics team that carries out data analysis. Core teams must both keep track of data and maintain numerous software packages and databases - which must be scalable according to ever-changing research needs - and they must allocate computational resources to them. Typical data analysis pipelines involve multiple software packages and require the ability to install, execute, and maintain software stacks on a variety of hardware/operating system configurations, including high-performance computing (HPC) facilities, stand-alone servers, and Cloud services. At the same time, individual researchers need to analyze their data and share their analysis steps with collaborators. Having to rely on ad hoc scripts and rigid internally developed pipelines is inefficient, difficult to track and maintain, and ultimately, limits the ability to adapt and evolve as research methods progress.\n\nResultsHere, we present BioSAILs (Bioinformatics Standardized Analysis Information Layers), a scientific workflow management system (WMS) developed by the Core Bioinformatics team at NYU Abu Dhabi. BioSAILs comprises two central components, BioX command and HPCRunner command, supported by BioStacks software stacks. BioX structures executable workflows that may be either run directly on a desktop or lab server, or submitted by HPCRunner to HPC infrastructure or the Cloud. BioSAILs is supported by a range of pre-configured, customizable BioStacks software stacks for various applications (e.g. RNA-seq, de novo genome/transcriptome assembly, variant discovery, etc.) Software stacks are built using Conda and BioConda (6), have been pre-packaged into modules using EasyBuild, and can be deployed with or without BioSAILs. Within the BioSAILs WMS, users can also invoke commands that take advantage of containerized software in Docker (10) and Singularity (8). Finally, the BioSAILs web resource provides documentation, blog posts related to various BioSAILs analysis tasks, forums, knowledge base & FAQs, as well as an interactive web-based workflow editor/creator. BioSAILs is production-level software that has been in use as the main WMS at NYU Abu Dhabi for the past 2 years and is continuously developed and maintained by the core bioinformatics team.\n\nAvailabilityBioSAILs is open source software, available under the GNU license. Online documentation, support and guides can be found at biosails.abudhabi.nyu.edu. The public Github project is available at Github - BioSAILs. HPC-Runner and BioX-Workflow can be installed through Conda using the BioConda channel.

bioinformatics↗

bioRxiv2018

Tissue- and sex-specific small RNAomes reveal sex differences in response to the environment

BackgroundRNA interference (RNAi) related pathways are essential for germline development and fertility in metazoa and can contribute to inter-and trans-generational inheritance. In the nematode Caenorhabditis elegans environmental double-stranded RNA provided by feeding can lead to heritable changes in phenotype and gene expression. Notably, transmission efficiency differs between the male and female germline, yet the underlying mechanisms remain elusive.\n\nResultsHere we use high-throughput sequencing of dissected gonads to quantify sex-specific endogenous piRNAs, miRNAs and siRNAs in the C. elegans germline and the somatic gonad. We identify genes with exceptionally high levels of 22G RNAs that are associated with low mRNA expression, a signature compatible with silencing. We further demonstrate that contrary to the hermaphrodite germline, the male germline, but not male soma, is resistant to environmental RNAi triggers provided by feeding. This sex-difference in silencing efficacy is associated with lower levels of gonadal RNAi amplification products. Moreover, this tissue-and sex-specific RNAi resistance is regulated by the germline, since mutant males with a feminized germline are RNAi sensitive.\n\nConclusionThis study provides important sex-and tissue-specific expression data of miRNA, piRNA and siRNA as well as mechanistic insights into sex-differences of gene regulation in response to environmental cues.

genomics↗

bioRxiv2018

Evolutionary analysis implicates RNA polymerase II pausing and chromatin structure in nematode piRNA biogenesis

Piwi-interacting RNAs (piRNAs) control transposable elements widely across metazoans but have rapidly evolving biogenesis pathways. In Caenorhabditis elegans, almost all piRNA loci are found within two 3Mb clusters on Chromosome IV. Each piRNA locus possesses an upstream motif that recruits RNA polymerase II to produce a [~]28 nt precursor transcript. Here, we use comparative epigenomics across nematodes to gain insight into piRNA biogenesis. We show that the piRNA upstream motif is derived from core promoter elements controlling snRNA biogenesis. We describe two alternative modes of piRNA organisation in nematodes: in C. elegans and closely related nematodes, piRNAs are clustered within repressive H3K27me3 chromatin, whilst in other species, typified by Pristionchus pacificus, piRNAs are distributed genome-wide within introns of actively transcribed genes. In both groups, piRNA production depends on downstream sequence signals associated with RNA polymerase II pausing, which synergise with the chromatin environment to control piRNA precursor transcription.

genomics↗

bioRxiv2017

Pheniqs: Fast and flexible quality-aware sequence demultiplexing

1MotivationOutput from high throughput sequencing instruments often exceeds what is necessary to assay a single sample. To better utilize this capacity, multiple samples are independently tagged with a unique \"barcode\" sequence and are then pooled, or \"multiplexed\", and sequenced together. Classifying, or \"demultiplexing\", the reads involves decoding the barcode sequence. Although instruments estimate the probability of incorrectly calling each nucleobase, available demultiplexers do not consult those estimates or report classification error probabilities.\n\nResultsWe present Pheniqs, a fast and flexible sequence demultiplexer and quality analyzer. In addition to providing an efficient implementation of the widespread minimum distance decoder, Pheniqs introduces a novel Phred-adjusted maximum likelihood decoder that consults base calling quality scores and estimates the probability of a barcode decoding error. Setting an upper bound on the permissible error provides an intuitive way to control demultiplexing confidence and directly influence precision and recall. Pheniqs supports FASTQ and multiple Sequence Alignment/Map formats and uses auxiliary SAM tags to report both library classification and demultiplexing error probability. Evaluation on both real and semi-synthetic data indicates that Pheniqs is faster than existing demultiplexers, substantially when demultiplexing longer reads, and achieves greater accuracy by correctly reflecting quality measurements.\n\nAvailability and ImplementationImplemented in multithreaded C++ and available under the terms of the AGPL-3.0 license agreement at http://github.com/biosails/pheniqs. Manual and examples are available at http://biosails.github.io/pheniqs.

bioinformatics↗