Biology subjects

Simmons, M.

Publications and source records attributed to Simmons, M..

6 recordsLinked to original sources

bioRxiv2026

Dissecting the interactions of the ISG15-USP18-STAT2 inhibitory complex

The suppression of type I interferon (IFN) signalling by the ISG15-USP18-STAT2 inhibitory complex (ISG15 IC) is an established regulatory mechanism of the antiviral response. However, a molecular understanding of how the ISG15 IC forms to suppress IFN signalling is still emerging. Here, we use AlphaFold modelling in conjunction with biochemical and biophysical approaches to elucidate the interactions of this multiprotein assembly. Our analysis identified a unique STAT2 binding loop (SBL) in USP18, which is critical for the USP18-STAT2 association. Further biochemical characterisation through site-directed mutagenesis confirmed the importance of residues within and surrounding the SBL, enabling the design of mutants with both increased and decreased binding affinities. Moreover, several USP18 and STAT2 patient mutations severely disrupted this interaction. Lastly, using influenza B virus (IBV) and Zika virus (ZIKV) proteins, we investigated the influence of these viral effector proteins on these interactions. Taken together, these results provide much-needed insights into a key aspect of IFN signalling control.

biochemistry↗

bioRxiv2020

Transcriptional profile of pyramidal neurons in chronic schizophrenia reveals lamina-specific dysfunction of neuronal immunity

While the pathophysiology of schizophrenia has been extensively investigated using homogenized postmortem brain samples, few studies have examined changes in brain samples with techniques that may attribute perturbations to specific cell types. To fill this gap, we performed microarray assays on mRNA isolated from anterior cingulate cortex (ACC) superficial and deep pyramidal neurons from 12 schizophrenia and 12 control subjects using laser capture microdissection. Among all the annotated genes, we identified 134 significantly increased and 130 decreased genes in superficial pyramidal neurons, while 93 significantly increased and 101 decreased genes were found in deep pyramidal neurons, in schizophrenia compared to control subjects. In these differentially expressed genes, we detected lamina-specific changes of 55 and 31 genes in superficial and deep neurons in schizophrenia, respectively. Gene set enrichment analysis (GSEA) was applied to the entire pre-ranked differential expression gene lists to gain a complete pathway analysis throughout all annotated genes. Our analysis revealed over-represented groups of gene sets in schizophrenia, particularly in immunity and synapse related pathways in pyramidal neurons, suggesting the disruption of these pathways plays an important role in schizophrenia. We also detected pathways previously demonstrated in schizophrenia pathophysiology, including cytokine and chemotaxis, post-synaptic signaling, and glutamatergic synapses. In addition, we observed several novel pathways, including ubiquitin-independent protein catabolic process. By comparing our differential expression gene profiles with 51 antipsychotic treatment datasets, we demonstrated that our results were not influenced by antipsychotic treatment of our subjects. Taken together, we found pyramidal neuron-specific changes in neuronal immunity, synaptic dysfunction, and olfactory dysregulation in schizophrenia, providing new insights for the cell-subtype specific pathophysiology of chronic schizophrenia.

neuroscience↗

bioRxiv2019

Rapid, multiplexed, whole genome and plasmid sequencing of foodborne pathogens using long-read nanopore technology

United States public health agencies are focusing on next-generation sequencing (NGS) to quickly identify and characterize foodborne pathogens. Here, the MinION nanopore, long-read sequencer was used to simultaneously sequence the entire chromosome and plasmids of Salmonella enterica subsp. enterica serovar Bareilly and Escherichia coli O157:H7. A rapid, random sequencing approach, coupled with de novo genome assembly within a customized data analysis workflow, that can resolve highly-repetitive genomic regions, was developed. In sequencing runs, as short as four hours, using nanopore data alone, full-length genomes were obtained with an average identity of 99.87% for Salmonella Bareilly and 99.89% for E. coli in comparison to the respective MiSeq references. These long-read assemblies provided information on serotype, virulence factors, and antimicrobial resistance genes. Using a custom-developed, SNP-selection workflow, the potential of the nanopore-only assemblies (after only 30 minutes of sequencing) for rapid phylogenetic inference, with identical topology compared to the published dataset, was demonstrated. To achieve maximum quality assemblies, the developed bioinformatics workflow employed additional polishing steps to correct the systematic errors produced by the nanopore-only assemblies. Nanopore sequencing provided a shorter (10 hours library preparation and sequencing) turnaround time compared to other NGS technologies.

microbiology↗

bioRxiv2019

Evolutionary dynamics of phage resistance in bacterial biofilms

Interactions among bacteria and their viral predators, the bacteriophages, are likely among the most common ecological phenomena on Earth. The constant threat of phage infection to bacterial hosts, and the imperative of achieving infection on the part of phages, drives an evolutionary contest in which phage-resistant bacteria emerge, often followed by phages with new routes of infection. This process has received abundant theoretical and experimental attention for decades and forms an important basis for molecular genetics and theoretical ecology and evolution. However, at present, we know very little about the nature of phage-bacteria interaction - and the evolution of phage resistance - inside the surface-bound communities that microbes usually occupy in natural environments. These communities, termed biofilms, are encased in a matrix of secreted polymers produced by their microbial residents. Biofilms are spatially constrained such that interactions become limited to neighbors or near-neighbors; diffusion of solutes and particulates is reduced; and there is pronounced heterogeneity in nutrient access and therefore physiological state. These factors can dramatically impact the way phage infections proceed even in simple, single-strain biofilms, but we still know little of their effect on phage resistance evolutionary dynamics. Here we explore this problem using a computational simulation framework customized for implementing phage infection inside multi-strain biofilms. Our simulations predict that it is far easier for phage-susceptible and phage-resistant bacteria to coexist inside biofilms relative to planktonic culture, where phages and hosts are well-mixed. We characterize the negative frequency dependent selection that underlies this coexistence, and we then test and confirm this prediction using an experimental model of biofilm growth measured with confocal microscopy at single-cell and single-phage resolution.

microbiology↗

bioRxiv2019

Using the NCBI AMRFinder Tool to Determine Antimicrobial Resistance Genotype-Phenotype Correlations Within a Collection of NARMS Isolates

Antimicrobial resistance (AMR) is a major public health problem that requires publicly available tools for rapid analysis. To identify acquired AMR genes in whole genome sequences, the National Center for Biotechnology Information (NCBI) has produced a high-quality, curated, AMR gene reference database consisting of up-to-date protein and gene nomenclature, a set of hidden Markov models (HMMs), and a curated protein family hierarchy. Currently, the Bacterial Antimicrobial Resistance Reference Gene Database contains 4,579 antimicrobial resistance gene proteins and more than 560 HMMs. Here, we describe AMRFinder, a tool that uses this reference dataset to identify AMR genes. To assess the predictive ability of AMRFinder, we measured the consistency between predicted AMR genotypes from AMRFinder against resistance phenotypes of 6,242 isolates from the National Antimicrobial Resistance Monitoring System (NARMS). This included 5,425 Salmonella enterica, 770 Campylobacter spp., and 47 Escherichia coli phenotypically tested against various antimicrobial agents. Of 87,679 susceptibility tests performed, 98.4% were consistent with predictions. To assess the accuracy of AMRFinder, we compared its gene symbol output with that of a 2017 version of ResFinder, another publicly available resistance gene database. Most gene calls were identical, but there were 1,229 gene symbol differences between them, with differences due to both algorithmic differences and database composition. AMRFinder missed 16 loci that Resfinder found, while Resfinder missed 1,147 loci AMRFinder identified. Two missing drug classes from the 2017 version of ResFinder contributed 81% of missed loci. Based on these results, AMRFinder appears to be a highly accurate AMR gene detection system. ImportanceAntimicrobial resistance is a major public health problem. Traditionally, antimicrobial resistance has been identified using phenotypic assays. With the advent of genome sequencing, we now can identify resistance genes and deduce if an isolate could be resistant to antibiotics. We describe a database of 4,579 acquired antimicrobial resistance genes, the largest publicly available, and a software tool to identify genes in bacterial genomes, AMRFinder. Unlike other tools, AMRFinder uses a gene hierarchy to prevent overpredicting what the correct gene call should be, enabling more accurate assessment. To assess these resources, we determined the resistance gene content of over 6,200 bacterial isolates from the National Antimicrobial Resistance Monitoring System that have been assayed using traditional methods and that also have had their genomes sequenced. We also compared our gene assessments to those of a popularly used tool. We found that AMRFinder has a high overall consistency between genotypes and phenotypes.

microbiology↗

bioRxiv2016

Theory for the spatiotemporal interaction between lytic phages and biofilm-dwelling bacteria

Many bacteria are adapted for attaching to surfaces and for building complex communities, termed biofilms. The biofilm mode of life is predominant in bacterial ecology. So, too, is exposure of bacteria to ubiquitous viral pathogens, termed bacteriophages. Although biofilmphage encounters are likely to be very common in nature, little is known about how phages might interact with biofilm-dwelling bacteria. It is also unclear how the ecological dynamics of phages and their hosts depend on the biological and physical properties of the biofilm environment. To make headway in this area, here we develop the first biofilm simulation framework that captures key mechanistic features of biofilm growth and phage infection. Using these simulations, we find that the equilibrium state of interaction between biofilms and phages is governed largely by nutrient availability to biofilms, infection likelihood per host encounter, and the ability of phages to diffuse through biofilm populations. Interactions between the biofilm matrix and phage particles are thus likely to be of fundamental importance, controlling the extent to which bacteria and phages can coexist in natural contexts. Our results open avenues to new questions of host-parasite coevolution and horizontal gene transfer in spatially structured biofilm contexts.

microbiology↗