Biology subjects

Ghedin, E.

Publications and source records attributed to Ghedin, E..

At least 19 recordsLinked to original sources

bioRxiv2026

Disulphide and sequence-encoded conformational priors guide nanobody structure prediction

Nanobody binding is largely governed by the HCDR3 loop, which adopts distinct placement regimes relative to the framework: compact, framework-contacting (kinked blueprint) and solvent-exposed (extended blueprint). Many nanobodies also contain additional cysteines that form non-canonical disulphide bonds, imposing covalent constraints on binding-loop conformations. Current structure predictors are typically trained and benchmarked with smooth coordinate-based objectives, so models may appear reasonable under root-mean-square deviation (RMSD), while adopting an incorrect HCDR3 blueprint or failing to recover the native disulphide connectivity, impacting paratope geometry and functional interpretation. Here, we show that the HCDR3 blueprint is predictable from sequence alone, allowing for explicit constraints during modelling. We implement these principles into NbForge, a lightweight nanobody folding model that incorporates blueprint- and disulphide-aware inductive biases and is trained with filtered self-distillation. NbForge improves recovery of HCDR3 blueprint and non-canonical disulphide formation over previous lightweight models and achieves coordinate accuracy at par to state-of-the-art, large, resource-intensive predictors, while running at sub-second inference speed. We show that using NbForge monomer models as templates further improves the success rate of predicting nanobody-antigen complexes. Together, these results motivate blueprint- and disulphide-aware benchmarks for nanobody modelling beyond RMSD, and show that appropriate inductive biases can close the performance gap to heavyweight predictors. We make the sequence classifier (NbFrame) and NbForge available for download and via a user-friendly web server.

bioinformatics↗

bioRxiv2025

SurfDiff: protein surface profiling for selective or broadly reactive epitope prioritisation in binder and immunogen design

Rational selection of protein-surface patches - epitopes or ligandable sites - is essential for developing targeted antibodies, binding proteins, peptides, or small-molecule ligands, and for informing immunogen design in vaccines. We present SurfDiff, a structure- and sequence-informed framework for protein surface comparison. SurfDiff supports one-to-one and one-to-many comparisons by combining local structural alignments with physicochemically and spatially aware neighbourhood analysis. It assigns residue-level uniqueness and similarity scores, which can be aggregated into surface-level similarity, selectivity, and a discriminability score that captures conservation across desired targets while penalising similarity to undesired off-targets. These scores, calculated solely from the targets without any knowledge of the binders, reliably predict known experimental selectivity for diverse binders including small molecules, peptides, antibodies and antibody mimetics across viral antigens, cytokine isoforms, GPCR subtypes and serum albumin. They also correlate strongly with binding and neutralisation data across pathogen variants, and substantially outperform commonly used bioinformatic metrics such as sequence substitution matrices and structural similarity measures. SurfDiff provides a generalisable and interpretable approach to protein surface profiling, enabling selective or cross-reactive binder design, cross-species prioritisation, and facilitating immunogen selection. We make SurfDiff available as downloadable open-source software: (https://gitlab.developers.cam.ac.uk/ch/sormanni/surfdiff) and as a webserver: www-cohsoftware.ch.cam.ac.uk/index.php/surfdiff.

bioinformatics↗

bioRxiv2024

The microbiome of Total Suspended Particles (TSP) and its influence on the respiratory microbiome of healthy office workers

Air particulate matter (PM) is widely recognized for its potential to negatively affect human health, including changes in the upper respiratory microbiome. However, research on PM-associated microbiota remains limited and mostly focused on PM (e.g., PM2.5 and PM10). This study aims to characterize for the first time the microbiome of Total Suspended Particles (TSP) and investigate the correlations of indoor TSP with the human upper respiratory microbiome. Biological and environmental samples were collected over three collection periods lasting three weeks each, between May and July 2022 at the University of Milan and the University of Insubria Como. TSP were sampled using a filter-based technique, while respiratory samples from both anterior nares (AN) and the nasopharynx (NP) were collected using swabs. Microbiome analysis of both human (N = 145) and TSP (N = 51) samples was conducted on metagenomic sequencing data. A comparison of indoor and outdoor TSP microbiomes revealed differences in microbial diversity and taxonomic composition. The indoor samples had higher relative abundance of environmental bacteria often associated with opportunistic infections like Paracoccus sp., as well as respiratory bacteria such as Staphylococcus aureus and Klebsiella pneumoniae. Additionally, both indoor and outdoor TSP samples contained broad spectrum antibiotic resistance genes. Indoor TSP exposure was negatively associated with commensal bacteria and positively associated with Staphylococcus aureus relative abundance. Finally, a correlation between the relative abundance of respiratory bacteria identified in the indoor TSP and the upper respiratory microbiome was found, suggesting a potential interaction between TSP and the upper airways.

microbiology↗

bioRxiv2024

CD4+ and CD8+ T cells are required to prevent SARS-CoV-2 persistence in the nasal compartment

SARS-CoV-2 is the causative agent of COVID-19 and continues to pose a significant public health threat throughout the world. Following SARS-CoV-2 infection, virus-specific CD4+ and CD8+ T cells are rapidly generated to form effector and memory cells and persist in the blood for several months. However, the contribution of T cells in controlling SARS-CoV-2 infection within the respiratory tract are not well understood. Using C57BL/6 mice infected with a naturally occurring SARS-CoV-2 variant (B.1.351), we evaluated the role of T cells in the upper and lower respiratory tract. Following infection, SARS-CoV-2-specific CD4+ and CD8+ T cells are recruited to the respiratory tract and a vast proportion secrete the cytotoxic molecule Granzyme B. Using antibodies to deplete T cells prior to infection, we found that CD4+ and CD8+ T cells play distinct roles in the upper and lower respiratory tract. In the lungs, T cells play a minimal role in viral control with viral clearance occurring in the absence of both CD4+ and CD8+ T cells through 28 days post-infection. In the nasal compartment, depletion of both CD4+ and CD8+ T cells, but not individually, results in persistent and culturable virus replicating in the nasal compartment through 28 days post-infection. Using in situ hybridization, we found that SARS-CoV-2 infection persisted in the nasal epithelial layer of tandem CD4+ and CD8+ T cell-depleted mice. Sequence analysis of virus isolates from persistently infected mice revealed mutations spanning across the genome, including a deletion in ORF6. Overall, our findings highlight the importance of T cells in controlling virus replication within the respiratory tract during SARS-CoV-2 infection.

immunology↗

bioRxiv2024

Patient Subtyping Analysis of Baseline Multi-omic Data Reveals Distinct Pre-immune States Predictive of Vaccination Responses

Understanding the molecular mechanisms that underpin diverse vaccination responses is a critical step toward developing efficient vaccines. Molecular subtyping approaches can offer valuable insights into the heterogeneous nature of responses and aid in the design of more effective vaccines. In order to explore the molecular signatures associated with the vaccine response, we analyzed baseline transcriptomics data from paired samples of whole blood, proteomics and glycomics data from serum, and metabolomics data from urine, obtained from influenza vaccine recipients (2019-2020 season) prior to vaccination. After integrating the data using a network-based model, we performed a subtyping analysis. The integration of multiple data modalities from 62 samples resulted in five baseline molecular subtypes with distinct molecular signatures. These baseline subtypes differed in the expression of pre-existing adaptive or innate immunity signatures, which were linked to significant variation across subtypes in baseline immunoglobulin A (IgA) and hemagglutination inhibition (HAI) titer levels. It is worth noting that these significant differences persisted through day 28 post-vaccination, indicating the effect of initial immune state on vaccination response. These findings highlight the significance of interpersonal variation in baseline immune status as a crucial factor in determining vaccine response and efficacy. Ultimately, incorporating molecular profiling could enable personalized vaccine optimization.

immunology↗

bioRxiv2023

Host obesity impacts genetic variation in influenza A viral populations

Obesity is a chronic health condition characterized by excess adiposity leading to a systemic increase in inflammation and dysregulation of metabolic hormones and immune cell populations. Obesity is well established as a risk factor for many noncommunicable diseases; however, its consequences for infectious disease are poorly understood. Influenza A virus (IAV) is a highly infectious pathogen responsible for seasonal and pandemic influenza. Host risk factors, including compromised immunity and pre-existing health conditions, can contribute to increased infection susceptibility and disease severity. During viral replication in a host, the negative sense single stranded RNA genome of IAV accumulates genetic diversity that may have important consequences for viral evolution and transmission. Here, we investigated the impact of host obesity on IAV genetic variation using a diet induced obesity ferret model. We infected obese and lean male ferrets with the A/Hong Kong/1073/1999 (H9N2) IAV strain. Using a co-caging study design, we investigated the maintenance, generation, and transmission of intrahost IAV genetic variation by sequencing viral genomic RNA obtained from nasal wash samples over multiple days of infection. We found evidence for an enhanced role of positive selection acting on de novo mutations in obese hosts that led to nonsynonymous changes that rose to high frequency. In addition, we identified numerous cases of recurrent low-frequency mutations throughout the genome that were specific to obese hosts. Despite these obese-specific variants, overall viral genetic diversity did not differ significantly between obese and lean hosts. This is likely due to the high supply rate of de novo variation and common evolutionary adaptations to the ferret host regardless of obesity status, which we show are mediated by variation in the hemagglutinin (HA) and polymerase genes (PB2 and PB1). As with single nucleotide variants, we identified a class of defective viral genomes (DVGs) that were found uniquely in either obese or lean hosts, but overall DVG diversity and dynamics did not differ between the two groups. Our study provides the first insight into the consequences of host obesity on viral genetic diversity and adaptation, suggesting that host factors associated with obesity alter the selective environment experienced by a viral population, thereby impacting the spectrum of genetic variation.

microbiology↗

bioRxiv2022

Miniature spatial transcriptomics for studying parasite-endosymbiont relationships at the micro scale

Several important human infectious diseases are caused by microscale-sized parasitic nematodes like filarial worms. Filarial worms have their own spatial tissue organization and to uncover this tissue structure, we need methods that can spatially resolve these miniature specimens. Most filarial worms evolved a mutualistic association with endosymbiotic bacteria Wolbachia, however, the mechanisms underlying the dependency of filarial worms on the fitness of these bacteria remain unknown. As Wolbachia is essential for the development, reproduction, and survival of filarial worms, we focused on studying a posterior region containing reproductive tissue and developing embryos of adult female Brugia malayi worms. To spatially explore how Wolbachia interacts with the worms reproductive system, we performed a spatial characterization using Spatial Transcriptomics (ST) across our region of interest. We provide a proof-of-concept for miniature-ST to explore spatial gene expression patterns in small sample types, demonstrating the methods ability to uncover nuanced tissue region expression patterns, observe the spatial localization of key B. malayi - Wolbachia pathway genes, and co-localize the B. malayi spatial transcriptome in Wolbachia tissue regions. We envision our approach to open up new scenarios for the study of infectious diseases caused by micro-scale parasitic worms.

genomics↗

bioRxiv2022

Influenza A virus reassortment is strain dependent

RNA viruses can exchange genetic material during coinfection, an interaction that creates novel strains with implications for viral evolution and public health. Influenza A viral genetic exchange occurs when genome segments from distinct strains reassort in coinfection. Predicting potential reassortment between influenza strains is a longstanding goal. Experimental coinfection studies have shed light on factors that limit or promote reassortment. However, determining the reassortment potential between diverse Influenza A strains has remained an elusive goal. To fill this gap, we developed a high throughput genotyping approach to quantify reassortment among a diverse panel of human influenza virus strains, encompassing 41 years of epidemics, multiple geographic locations, and both circulating human subtypes A/H1N1 and A/H3N2. We found that the reassortment rate (proportion of reassortants) is an emergent property of a pair of strains where strain identity is a predictor of the reassortment rate. We show little evidence that antigenic subtype drives reassortment as intersubtype (H1N1xH3N2) and intrasubtype reassortment rates were, on average, similar. Instead, our data suggest that certain strains bias the reassortment rate up or down, independently of the coinfecting partner. We also observe that viral productivity is an emergent property of coinfections and that it is not correlated to reassortment rate, thus affecting the total number of reassortant progeny produced. Assortment of individual segments among progeny, and pairwise segment combinations within progeny, were not random and generally favored homologous combinations. This outcome was not related to strain similarity or shared subtype. Reassortment rate was closely correlated to both the proportion of unique genotypes and the proportion of progeny with heterologous pairwise segment combinations. We provide experimental evidence that viral genetic exchange is potentially an individual social trait subject to natural selection, which implies the propensity for reassortment is not evenly shared among strains. This study highlights the need for research incorporating diverse strains to discover the traits that shift the reassortment potential as we work towards the goal of predicting influenza virus evolution resulting from segment exchange.

microbiology↗

bioRxiv2022

Reduced inflammatory response and promoted multiciliated cell differentiation in mice protected by defective interfering influenza virus

Influenza defective interfering (DI) viruses have long been considered promising antiviral candidates because of their ability to interfere with replication-competent viruses and to induce antiviral immunity. However, the mechanisms underlying DI-mediated antiviral immunity have not been extensively explored. Here, we demonstrated interferon (IFN) independent protection conferred by influenza DI virus against homologous virus infection in mice deficient in type I and III IFN signaling. By integrating transcriptional and post-transcriptional regulatory data we identified unique host signatures in response to DI co-infection. DI-treated mice exhibited reduced viral transcription, less intense inflammatory and innate immune responses, and primed multiciliated cell differentiation in their lungs at an early stage of infection, even in the absence of type I or III IFNs. Overall, our study provides mechanistic insight into the protection mediated by DIs, implying a unifying theme involving inflammation and multiciliogenesis in maintaining respiratory homeostasis, and reveals their IFN-independent antiviral activity.

microbiology↗

bioRxiv2021

Structurally conserved domains between flavivirus and alphavirus fusion glycoproteins contribute to replication in mammals and infectious virion production.

Alphaviruses and flaviviruses have class II fusion glycoproteins that are essential for virion assembly and infectivity. Importantly, the tip of domain II is structurally conserved between the alphavirus and flavivirus fusion proteins, yet whether these structural similarities between virus families translate to functional similarities is unclear. Using in vivo evolution of Zika virus (ZIKV), we identified several novel emerging variants including an envelope glycoprotein variant in {beta}-strand c (V114M) of domain II. We have previously shown that the analogous {beta}-strand c and the ij loop, located in the tip of domain II of the alphavirus E1 glycoprotein, are important for infectivity. This led us to hypothesize that flavivirus E {beta}-strand c also contributes to flavivirus infection. We generated this ZIKV glycoprotein variant and found that while it had little impact on infection in mosquitoes, it reduced replication in human cells and mice, and increased virus sensitivity to ammonium chloride, as seen for alphaviruses. In light of these results and given our alphavirus ij loop studies, we mutated a conserved alanine at the tip of the flavivirus ij loop to valine to test its effect on ZIKV infectivity. Interestingly, this mutation inhibited infectious virion production of ZIKV and yellow fever virus, but not West Nile virus. Together, these studies show that structurally analogous residues in the alphavirus and flavivirus class II fusion proteins contribute to virus infection in vivo and highlight these shared domains as targets for broad-spectrum arbovirus therapies. Author SummaryArboviruses are a significant global public health threat, yet there are no antivirals targeting these viruses. This problem is in part due to our lack of knowledge on the molecular mechanisms involved in the arbovirus life cycle. In particular, virus entry and assembly are essential processes in the virus life cycle and steps that can be targeted for the development of antiviral therapies. Therefore, understanding common, fundamental mechanisms used by different arboviruses for entry and assembly is essential. In this study, we show that flavivirus and alphavirus residues located in structurally conserved and analogous regions of the class II fusion proteins contribute to common mechanisms of entry, dissemination, and infectious virion production. These studies highlight how class II fusion proteins function and provide novel targets for development of antivirals.

microbiology↗

bioRxiv2021

Accurate Virus Identification with Interpretable Raman Signatures by Machine Learning

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device coupled with label-free Raman Spectroscopy holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning approach applied to recognize the virus based on its Raman spectrum. In this paper, we present a machine learning analysis on Raman spectra of human and avian viruses. A Convolutional Neural Network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A vs. type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and non-enveloped viruses, and 99% for differentiating avian coronavirus (infectious bronchitis virus, IBV) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups (e.g. amide, amino acid, carboxylic acid) we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses. The accurate and interpretable machine learning model developed for Raman virus identification presents promising potential in a real-time virus detection system. Significance StatementA portable micro-fluidic platform for virus capture promises rapid enrichment and label-free optical identification of viruses by Raman spectroscopy. A large Raman dataset collected on a variety of viruses enables the training of machine learning (ML) models capable of highly accurate and sensitive virus identification. The trained ML models can then be integrated with the portable device to provide real-time virus detection and identification capability. We validate this conceptual framework by presenting highly accurate virus type and subtype identification results using a convolutional neural network to classify Raman spectra of viruses.

biophysics↗

bioRxiv2021

Diversity and selection of SARS-CoV-2 minority variants in the early New York City outbreak

High error rates of viral RNA-dependent RNA polymerases lead to diverse intra-host viral populations during infection. Errors made during replication that are not strongly deleterious to the virus can lead to the generation of minority variants. However, accurate detection of minority variants in viral sequence data is complicated by errors introduced during sample preparation and data analysis. We used synthetic RNA controls and simulated data to test seven variant calling tools across a range of allele frequencies and simulated coverages. We show that choice of variant caller, and use of replicate sequencing have the most significant impact on single nucleotide variant (SNV) discovery and demonstrate how both allele frequency and coverage thresholds impact both false discovery and false negative rates. We use these parameters to find minority variants in sequencing data from SARS-CoV-2 clinical specimens and provide guidance for studies of intrahost viral diversity using either single replicate data or data from technical replicates. Our study provides a framework for rigorous assessment of technical factors that impact SNV identification in viral samples and establishes heuristics that will inform and improve future studies of intrahost variation, viral diversity, and viral evolution. IMPORTANCEWhen viruses replicate inside a host, the virus replication machinery makes mistakes. Over time, these mistakes create mutations that result in a diverse population of viruses inside the host. Mutations that are neither lethal to the virus, nor strongly beneficial, can lead to minority variants that are minor members of the virus population. However, preparing samples for sequencing can also introduce errors that resemble minority variants, resulting in inclusion of false positive data if not filtered correctly. In this study, we aimed to determine the best methods for identification and quantification of these minority variants by testing the performance of seven commonly used variant calling tools. We used simulated and synthetic data to test their performance against a true set of variants, and then used these studies to inform variant identification in data from clinical SARS-CoV-2 clinical specimens. Together, analyses of our data provide extensive guidance for future studies of viral diversity and evolution.

microbiology↗

bioRxiv2021

Sex disparities in influenza: a multiscale network analysis

Sex differences in the pathogenesis of infectious diseases due to differential immune responses between females and males have been well documented for multiple pathogens. However, the molecular mechanism underlying the observed sex differences in influenza virus infection remains poorly understood. In this study, we used a network-based approach to characterize the blood transcriptome collected over the course of infection with influenza A virus from female and male ferrets to dissect sex-biased gene expression. We identified significant differences in the temporal dynamics and regulation of immune responses between females and males. Our results elucidate sex-differentiated pathways involved in the unfolded protein response (UPR), lipid metabolism, and inflammatory responses, including a female-biased IRE1/XBP1 activation and male-biased crosstalk between metabolic reprogramming and IL-1 and AP-1 pathways. Overall, our study provides molecular insights into sex differences in transcriptional regulation of immune responses and contributes to a better understanding of sex bias in influenza pathogenesis.

systems biology↗

bioRxiv2021

Sex-specific glycosylation of secreted immunomodulatory proteins in the filarial nematode Brugia malayi

The extended persistence of filarial nematodes within a host suggests immunomodulatory mechanisms that allow the parasites to resist or evade the host immune response. There is increasing evidence for immunomodulatory glycans expressed by a diversity of parasitic worms. In this study, we integrate multiple layers of the host-parasite interface to investigate the glycome of a model filarial parasite, Brugia malayi. We report a significant overrepresentation of terminal GalNAc moieties in adult female worms coupled with an overall upregulation in O-glycosylation, T-antigen expression, and a bias for galactose containing glycans. Adult males preferentially displayed a bias for terminal GlcNAc containing glycans, and fucosylated epitopes. Subsequent proteomic analysis confirmed sex-biases in protein glycosylation and highlighted the sex-specific glycosylation of well characterized immunomodulators expressed and secreted by B. malayi. We identify sex-specific effectors at that interface and suggest approaches to selectively interfere with the parasitic life cycle and potentially control transmission.

microbiology↗

bioRxiv2020

Age-dependent glycomic response to the 2009 pandemic H1N1 influenza virus and its association with disease severity

Influenza A viruses cause a spectrum of responses, from mild cold-like symptoms to severe respiratory illness and death. Viral strains and intrinsic host factors, such as age, can influence the severity of the disease. Glycosylation plays a critical role in influenza pathogenesis, however the molecular drivers of influenza outcomes remain unknown. In this work, we characterized the glycomic response to the H1N1 2009 pandemic influenza A virus in age-dependent severity. Using a ferret model and a lectin microarray technology we have developed, we compared responses in newly weaned and aged animals, a model for young children and the elderly, respectively. Glycomic analysis revealed changes in glycosylation over the course of the infection, that were associated with severity in an age-dependent manner. These responses may help explain the differential susceptibility to influenza A virus infection of young children and the elderly. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=98 SRC="FIGDIR/small/165613v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1c5aebdorg.highwire.dtl.DTLVardef@15316e6org.highwire.dtl.DTLVardef@45ff3org.highwire.dtl.DTLVardef@1376533_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2020

Glycomic analysis of host-response reveals high mannose as a key mediator of influenza severity

Influenza virus infections cause a wide variety of outcomes, from mild disease to 3-5 million cases of severe illness and ~290,000-645,000 deaths annually worldwide. The molecular mechanisms underlying these disparate outcomes are currently unknown. Glycosylation within the human host plays a critical role in influenza virus biology. However, the impact these modifications have on the severity of influenza disease has not been examined. Herein, we profile the glycomic host responses to influenza virus infection as a function of disease severity using a ferret model and our lectin microarray technology. We identify the glycan epitope high mannose as a marker of influenza virus-induced pathogenesis and severity of disease outcome. Induction of high mannose is dependent upon the unfolded protein response (UPR) pathway, a pathway previously shown to associate with lung damage and severity of influenza virus infection. Also, the mannan-binding lectin (MBL2), an innate immune lectin that negatively impacts influenza outcomes, recognizes influenza virus-infected cells in a high mannose dependent manner. Together, our data argue that the high mannose motif is an infection-associated molecular pattern on host cells that may guide immune responses leading to the concomitant damage associated with severity. SIGNIFICANCEInfluenza virus infection causes a range of outcomes from mild illness to death. The molecular mechanisms leading to these differential host responses are currently unknown. Herein, we identify the induction of high mannose, a glycan epitope, as a key mediator of severe disease outcome. We propose a mechanism in which activation of the unfolded protein response (UPR) upon influenza virus infection turns on expression of high mannose, which is then recognized by the innate immune lectin MBL2, activating the complement cascade and leading to subsequent inflammation. This work is the first to systematically study host glycomic changes in response to influenza virus infection, identifying high mannose as a key feature of differential host response.

microbiology↗

bioRxiv2020

iGenomics: Comprehensive DNA Sequence Analysis on your Smartphone

Following the miniaturization of integrated circuitry and other computer hardware over the past several decades, DNA sequencing is following a similar path. Leading this trend is the Oxford Nanopore sequencing platform, which currently offers the hand-held MinION instrument and even smaller instruments on the near horizon. This technology has been used in several important applications, including the analysis of genomes of major pathogens in remote stations around the world. However, despite the simplicity of the sequencer, an equally simple and portable analysis platform is not yet available. iGenomics is the first comprehensive mobile genome analysis application, with capabilities to align reads, call variants, and visualize the results entirely on an iOS device. Implemented in Objective-C using the FM-index, banded dynamic programming, and other high-performance bioinformatics techniques, iGenomics is optimized to run in a mobile environment. We benchmark iGenomics using a variety of real and simulated Nanopore sequencing datasets and show that iGenomics has performance comparable to the popular BWA-MEM/Samtools/IGV suite, without needing a laptop or server cluster. iGenomics is available open-source (https://github.com/stuckinaboot/iGenomics) and for free on Apples App Store (https://apps.apple.com/us/app/igenomics-mobile-dna-analysis/id1495719841).

bioinformatics↗

bioRxiv2019

Mapping the evolutionary landscape of Zika virus infection in immunocompromised mice.

The fundamental basis of how arboviruses evolve in nature and what regulates the adaptive process remain unclear. To address this problem, we established a Zika virus (ZIKV) vector-borne transmission system in immunocompromised mice that mimics evolutionary characteristics of ZIKV infection in humans. Using this system, we defined factors that influence the evolutionary landscape of ZIKV infection and show that transmission route and specific organ microenvironments impact viral diversity and defective viral genome (DVG) production. In addition, we identified in mice the emergence of ZIKV mutants previously seen in natural infections, including variants present in currently circulating strains, as well as mutations unique to the mouse infections. With these studies, we have established an insect-to-mouse transmission model to study ZIKV evolution. We also defined how organ microenvironments and infection route impact the ZIKV evolutionary landscape, providing a deeper understanding of the factors that regulate arbovirus evolution and emergence.

microbiology↗