Feasibility of polyclonal avian immunoglobulins (IgY) as prophylaxis against human norovirus infection

Background: Human norovirus (HuNoV) is the leading viral cause of diarrhea, with GII.4 as the predominant genotype of HuNoV outbreaks globally. However, new genogroup variants emerge periodically, complicating the development of anti-HuNoV vaccines; other prophylactic or therapeutic medications specifically for HuNoV disease are lacking. Passive immunization using oral anti-HuNoV antibodies may be a rational alternative. Here, we explore the feasibility of using avian immunoglobulins (IgY) for preventing HuNoV infection in vitro in a human intestinal enteroid (HIE) model. Methods: Hens were immunized with virus-like particles (VLP) of a GII.4 HuNoV strain (GII.4/CHDC2094/1974/US) by intramuscular injection. The resulting IgY was evaluated for inhibition of binding to histo-blood group antigens (HBGA) and viral neutralization against representative GII.4 and GII.6 clinical isolates, using an HIE model. Results: IgY titers were detected by three weeks following initial immunization, persisting at levels of 1:221 (1:2,097,152) from 9 weeks to 23 weeks. Anti-HuNoV IgY significantly (p < 0.05) blocked VLP adhesion to HBGA up to 1:12,048 dilution (0.005 mg/mL), and significantly (p < 0.05) inhibited replication of HuNoV GII.4[P16] Sydney 2012 in HIEs up to 1:128 dilution (0.08 mg/mL). Neutralization was not detected against genotype GII.6. Conclusions: We demonstrate the feasibility of IgY for preventing infection of HIE by HuNoV GII.4. Clinical preparations should cover multiple circulating HuNoV genotypes for comprehensive effects. Plans for animal studies are underway.Instituto de VirologíaFil: Artman, Chad. Scaled Microbiomics; Estados UnidosFil: Idegwu, Nnebuefe. Scaled Microbiomics; Estados UnidosFil: Brumfield, Kyle D. College Park Campus. University of Maryland. Maryland Pathogen Research Institute; Estados UnidosFil: Brumfield, Kyle D. College Park Campus. University of Maryland. University of Maryland Institute for Advanced Computer Studies; Estados UnidosFil: Lai, Ken. University of Saskatchewan. Vaccine and Infectious Disease Organization; CanadáFil: Hauta, Shirley. University of Saskatchewan. Vaccine and Infectious Disease Organization; CanadáFil: Falzarano, Darryl. University of Saskatchewan. Vaccine and Infectious Disease Organization; CanadáFil: Falzarano, Darryl. University of Saskatchewan. Western College of Veterinary Medicine. Department of Veterinary Microbiology; CanadáFil: Parreño, Gladys Viviana. Virginia-Maryland College of Veterinary Medicine. Department of Biomedical Sciences and Pathobiology; Estados UnidosFil: Parreño, Gladys Viviana. Instituto Nacional de Tecnología Agropecuaria (INTA). INCUINTA. Instituto de Virologia e Innovaciones Tecnologicas (IVIT); ArgentinaFil: Yuan, Lijuan. Virginia-Maryland College of Veterinary Medicine. Department of Biomedical Sciences and Pathobiology; Estados UnidosFil: Geyer, James D. University of Alabama. College of Community Health Science. Institute for Rural Health Research; Estados UnidosFil: Goepp, Julius G. Scaled Microbiomics; Estados Unido

Avian antibodies (IgY) targeting spike glycoprotein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) inhibit receptor binding and viral replication.

BackgroundThe global pandemic of Coronavirus infectious disease 2019 (COVID-19), caused by SARS-CoV-2, has plunged the world into both social and economic disarray, with vaccines still emerging and a continued paucity of personal protective equipment; the pandemic has also highlighted the potential for rapid emergence of aggressive respiratory pathogens and the need for preparedness. Avian immunoglobulins (IgY) have been previously shown in animal models to protect against new infection and mitigate established infection when applied intranasally. We carried out a proof-of-concept study to address the feasibility of using such antibodies as mucosally-applied prophylaxis against SARS-CoV-2.MethodsHens were immunized with recombinant S1 spike glycoprotein of the virus, and the resulting IgY was evaluated for binding specificity, inhibition of glycoprotein binding to angiotensin converting enzyme-2 (ACE2) protein (the requisite binding site for the virus), and inhibition of viral replication in Vero cell culture.ResultsTiters of anti-S1 glycoprotein IgY were evident in yolks at 14 days post-immunization, peaking at 21 days, and at peak concentrations of 16.8 mg/ml. IgY showed strong and significant inhibition of S1/ACE2 binding interactions, and significantly inhibited viral replication at a concentration of 16.8 mg/ml. Four weeks' collection from eggs of two hens produced a total of 1.55 grams of IgY.ConclusionsIn this proof-of-concept study we showed that avian immunoglobulins (IgY) raised against a key virulence factor of the SARS-CoV-2 virus successfully inhibited the critical initial adhesion of viral spike glycoproteins to human ACE2 protein receptors and inhibited viral replication in vitro, in a short period using only two laying hens. We conclude that production of large amounts of IgY inhibiting viral binding and replication of SARS-CoV-2 is feasible, and that incorporation of this or similar material into an intranasal spray and/or other mucosal protecting products may be effective at reducing infection and spread of COVID-19

A Taxonomy-Agnostic Approach to Targeted Microbiome Therapeutics&mdash;Leveraging Principles of Systems Biology

The study of human microbiomes has yielded insights into basic science, and applied therapeutics are emerging. However, conflicting definitions of what microbiomes are and how they affect the health of the &ldquo;host&rdquo; are less understood. A major impediment towards systematic design, discovery, and implementation of targeted microbiome therapeutics is the continued reliance on taxonomic indicators to define microbiomes in health and disease. Such reliance often confounds analyses, potentially suggesting associations where there are none, and conversely failing to identify significant, causal relationships. This review article discusses recent discoveries pointing towards a molecular understanding of microbiome &ldquo;dysbiosis&rdquo; and away from a purely taxonomic approach. We highlight the growing role of systems biological principles in the complex interrelationships between the gut microbiome and host cells, and review current approaches commonly used in targeted microbiome therapeutics, including fecal microbial transplant, bacteriophage therapies, and the use of metabolic toxins to selectively eliminate specific taxa from dysbiotic microbiomes. These approaches, however, remain wholly or partially dependent on the bacterial taxa involved in dysbiosis, and therefore may not capitalize fully on many therapeutic opportunities presented at the bioactive molecular level. New technologies capable of addressing microbiome-associated diseases as molecular problems, if solved, will open possibilities of new classes and categories of targeted microbiome therapeutics aimed, in principle, at all dysbiosis-driven disorders

Microbial resolution of whole genome shotgun and 16S amplicon metagenomic sequencing using publicly available NEON data.

Microorganisms are ubiquitous in the biosphere, playing a crucial role in both biogeochemistry of the planet and human health. However, identifying these microorganisms and defining their function are challenging. Widely used approaches in comparative metagenomics, 16S amplicon sequencing and whole genome shotgun sequencing (WGS), have provided access to DNA sequencing analysis to identify microorganisms and evaluate diversity and abundance in various environments. However, advances in parallel high-throughput DNA sequencing in the past decade have introduced major hurdles, namely standardization of methods, data storage, reproducible interoperability of results, and data sharing. The National Ecological Observatory Network (NEON), established by the National Science Foundation, enables all researchers to address queries on a regional to continental scale around a variety of environmental challenges and provide high-quality, integrated, and standardized data from field sites across the U.S. As the amount of metagenomic data continues to grow, standardized procedures that allow results across projects to be assessed and compared is becoming increasingly important in the field of metagenomics. We demonstrate the feasibility of using publicly available NEON soil metagenomic sequencing datasets in combination with open access Metagenomics Rapid Annotation using the Subsystem Technology (MG-RAST) server to illustrate advantages of WGS compared to 16S amplicon sequencing. Four WGS and four 16S amplicon sequence datasets, from surface soil samples prepared by NEON investigators, were selected for comparison, using standardized protocols collected at the same locations in Colorado between April-July 2014. The dominant bacterial phyla detected across samples agreed between sequencing methodologies. However, WGS yielded greater microbial resolution, increased accuracy, and allowed identification of more genera of bacteria, archaea, viruses, and eukaryota, and putative functional genes that would have gone undetected using 16S amplicon sequencing. NEON open data will be useful for future studies characterizing and quantifying complex ecological processes associated with changing aquatic and terrestrial ecosystems

A Review of the Environmental Trigger and Transmission Components for Prediction of Cholera

Climate variables influence the occurrence, growth, and distribution of Vibrio cholerae in the aquatic environment. Together with socio-economic factors, these variables affect the incidence and intensity of cholera outbreaks. The current pandemic of cholera began in the 1960s, and millions of cholera cases are reported each year globally. Hence, cholera remains a significant health challenge, notably where human vulnerability intersects with changes in hydrological and environmental processes. Cholera outbreaks may be epidemic or endemic, the mode of which is governed by trigger and transmission components that control the outbreak and spread of the disease, respectively. Traditional cholera risk assessment models, namely compartmental susceptible-exposed-infected-recovered (SEIR) type models, have been used to determine the predictive spread of cholera through the fecal–oral route in human populations. However, these models often fail to capture modes of infection via indirect routes, such as pathogen movement in the environment and heterogeneities relevant to disease transmission. Conversely, other models that rely solely on variability of selected environmental factors (i.e., examine only triggers) have accomplished real-time outbreak prediction but fail to capture the transmission of cholera within impacted populations. Since the mode of cholera outbreaks can transition from epidemic to endemic, a comprehensive transmission model is needed to achieve timely and reliable prediction with respect to quantitative environmental risk. Here, we discuss progression of the trigger module associated with both epidemic and endemic cholera, in the context of the autochthonous aquatic nature of the causative agent of cholera, V. cholerae, as well as disease prediction.https://doi.org/10.3390/tropicalmed603014

A comparative analysis of drinking water employing metagenomics.

The microbiological content of drinking water traditionally is determined by employing culture-dependent methods that are unable to detect all microorganisms, especially those that are not culturable. High-throughput sequencing now makes it possible to determine the microbiome of drinking water. Thus, the natural microbiota of water and water distribution systems can now be determined more accurately and analyzed in significantly greater detail, providing comprehensive understanding of the microbial community of drinking water applicable to public health. In this study, shotgun metagenomic analysis was performed to determine the microbiological content of drinking water and to provide a preliminary assessment of tap, drinking fountain, sparkling natural mineral, and non-mineral bottled water. Predominant bacterial species detected were members of the phyla Actinobacteria and Proteobacteria, notably the genera Alishewanella, Salmonella, and Propionibacterium in non-carbonated non-mineral bottled water, Methyloversatilis and Methylibium in sparkling natural mineral water, and Mycobacterium and Afipia in tap and drinking fountain water. Fecal indicator bacteria, i.e., Escherichia coli or enterococci, were not detected in any samples examined in this study. Bacteriophages and DNA encoding a few virulence-associated factors were detected but determined to be present only at low abundance. Antibiotic resistance markers were detected only at abundance values below our threshold of confidence. DNA of opportunistic plant and animal pathogens was identified in some samples and these included bacteria (Mycobacterium spp.), protozoa (Acanthamoeba mauritaniensis and Acanthamoeba palestinensis), and fungi (Melampsora pinitorqua and Chryosporium queenslandicum). Archaeal DNA (Candidatus Nitrosoarchaeum) was detected only in sparkling natural mineral water. This preliminary study reports the complete microbiome (bacteria, viruses, fungi, and protists) of selected types of drinking water employing whole-genome high-throughput sequencing and bioinformatics. Investigation into activity and function of the organisms detected is in progress

Gut microbiome insights from 16S rRNA analysis of 17-year periodical cicadas (Hemiptera: Magicicada spp.) Broods II, VI, and X

Periodical cicadas (Hemiptera: Magicicada) have coevolved with obligate bacteriome-inhabiting microbial symbionts, yet little is known about gut microbial symbiont composition or differences in composition among allochronic Magicicada broods (year classes) which emerge parapatrically or allopatrically in the eastern United States. Here, 16S rRNA amplicon sequencing was performed to determine gut bacterial community profiles of three periodical broods, including II (Connecticut and Virginia, 2013), VI (North Carolina, 2017), and X (Maryland, 2021, and an early emerging nymph collected in Ohio, 2017). Results showed similarities among all nymphal gut microbiomes and between morphologically distinct 17-year Magicicada, namely Magicicada septendecim (Broods II and VI) and 17-year Magicicada cassini (Brood X) providing evidence of a core microbiome, distinct from the microbiome of burrow soil inhabited by the nymphs. Generally, phyla Bacteroidetes [Bacteroidota] (&gt; 50% relative abundance), Actinobacteria [Actinomycetota], or Proteobacteria [Pseudomonadota] represented the core. Acidobacteria and genera Cupriavidus, Mesorhizobium, and Delftia were prevalent in nymphs but less frequent in adults. The primary obligate endosymbiont, Sulcia (Bacteroidetes), was dominant amongst core genera detected. Chryseobacterium were common in Broods VI and X. Chitinophaga, Arthrobacter, and Renibacterium were common in Brood X, and Pedobacter were common to nymphs of Broods II and VI. Further taxonomic assignment of unclassified Alphaproteobacteria sequencing reads allowed for detection of multiple copies of the Hodgkinia 16S rRNA gene, distinguishable as separate operational taxonomic units present simultaneously. As major emergences of the broods examined here occur at 17-year intervals, this study will provide a valuable comparative baseline in this era of a changing climate

Building Environmental and Sociological Predictive Intelligence to Understand the Seasonal Threat of SARS-CoV-2 in Human Populations

Current modeling practices for environmental and sociological modulated infectious diseases remain inadequate to forecast the risk of outbreak(s) in human populations, partly due to a lack of integration of disciplinary knowledge, limited availability of disease surveillance datasets, and overreliance on compartmental epidemiological modeling methods. Harvesting data knowledge from virus transmission (aerosols) and detection (wastewater) of SARS-CoV-2, a heuristic score-based environmental predictive intelligence system was developed that calculates the risk of COVID-19 in the human population. Seasonal validation of the algorithm was uniquely associated with wastewater surveillance of the virus, providing a lead time of 7–14 days before a county-level outbreak. Using county-scale disease prevalence data from the United States, the algorithm could predict COVID-19 risk with an overall accuracy ranging between 81% and 98%. Similarly, using wastewater surveillance data from Illinois and Maryland, the SARS-CoV-2 detection rate was greater than 80% for 75% of the locations during the same time the risk was predicted to be high. Results suggest the importance of a holistic approach across disciplinary boundaries that can potentially allow anticipatory decision-making policies of saving lives and maximizing the use of available capacity and resources