Influenza virus neuraminidase cleaves sialic acid groups from cell glycoproteins, enabling release of the virus from host cells. Neuraminidase also contributes to virus binding to the sialic acid groups of cell glycoproteins, which could complement the receptor-binding function of hemagglutinin, enhancing enzymatic activities of neuraminidase, and facilitate virus infection.
A new method for intrinsic terminator prediction based on Rnall, an RNA local secondary structure prediction algorithm developed recently, and two U-tail score schemas are developed. By optimizing three parameters (thermodynamic energy of RNA hairpin structure, U-tail T weight, and U-tail hybridization energy), the method can recognize 92.25% of known terminators while rejecting 98.48% of predicted RNA local secondary structures in coding regions (negative control) as false intrinsic terminators in E. coli. This method was applied to scan the genome of Synechococcus sp. WH8102, and we predicted 266 intrinsic terminators, which included 232 protein-coding genes, 12 tRNA genes, and 3 rRNA genes. About 17% of these terminators are located at the end of operons. It is also identified 8 pairs of bio-directional terminators. The method for intrinsic terminator prediction has been incorporated into Rnall, which is available at http://digbio.missouri.edu/∼wanx/Rnall/.
Rapid identification of influenza antigenic variants will be critical in selecting optimal vaccine candidates and thus a key to developing an effective vaccination program. Recent studies suggest that multiple simultaneous mutations at antigenic sites accumulatively enhance antigenic drift of influenza A viruses. However, pre-existing methods on antigenic variant identification are based on analyses from individual sites. Because the impacts of these co-evolved sites on influenza antigenicity may not be additive, it will be critical to quantify the impact of not only those single mutations but also multiple simultaneous mutations or co-evolved sites. Here, we developed and applied a computational method, AntigenCO, to identify and quantify both single and co-evolutionary sites driving the historical antigenic drifts. AntigenCO achieved an accuracy of up to 90.05% for antigenic variant prediction, significantly outperforming methods based on single sites. AntigenCO can be useful in antigenic variant identification in influenza surveillance.
Influenza viruses have been responsible for large losses of lives around the world and continue to present a great public health challenge. Antigenic characterization based on hemagglutination inhibition (HI) assay is one of the routine procedures for influenza vaccine strain selection. However, HI assay is only a crude experiment reflecting the antigenic correlations among testing antigens (viruses) and reference antisera (antibodies). Moreover, antigenic characterization is usually based on more than one HI dataset. The combination of multiple datasets results in an incomplete HI matrix with many unobserved entries. This paper proposes a new computational framework for constructing an influenza antigenic cartography from this incomplete matrix, which we refer to as Matrix Completion-Multidimensional Scaling (MC-MDS). In this approach, we first reconstruct the HI matrices with viruses and antibodies using low-rank matrix completion, and then generate the two-dimensional antigenic cartography using multidimensional scaling. Moreover, for influenza HI tables with herd immunity effect (such as those from Human influenza viruses), we propose a temporal model to reduce the inherent temporal bias of HI tables caused by herd immunity. By applying our method in HI datasets containing H3N2 influenza A viruses isolated from 1968 to 2003, we identified eleven clusters of antigenic variants, representing all major antigenic drift events in these 36 years. Our results showed that both the completed HI matrix and the antigenic cartography obtained via MC-MDS are useful in identifying influenza antigenic variants and thus can be used to facilitate influenza vaccine strain selection. The webserver is available at http://sysbio.cvm.msstate.edu/AntigenMap.
Antigenic characterization based on serological data, such as Hemagglutination Inhibition (HI) assay, is one of the routine procedures for influenza vaccine strain selection. In many cases, it would be impossible to measure all pairwise antigenic correlations between testing antigens and reference antisera in each individual experiment. Thus, we have to combine and integrate the HI tables from a number of individual experiments. Measurements from different experiments may be inconsistent due to different experimental conditions. Consequently we will observe a matrix with missing data and possibly inconsistent measurements. In this paper, we develop a new mathematical model, which we refer to as Joint Matrix Completion and Filtering, for HI data integration. In this approach, we simultaneously handle the incompleteness and uncertainty of observations by assuming that the underlying merged HI data matrix has low rank, as well as carefully modeling different levels of noises in each individual table. An efficient blockwise coordinate descent procedure is developed for optimization. The performance of our approach is validated on synthetic and real influenza datasets. The proposed joint matrix completion and filtering model can be adapted as a general model for biological data integration, targeting data noises and missing values within and across experiments.
The H1N1 subtype of influenza A virus has caused two of the four documented pandemics and is responsible for seasonal epidemic outbreaks, presenting a continuous threat to public health. Co-circulating antigenically divergent influenza strains significantly complicates vaccine development and use. Here, by combining evolutionary, structural, functional, and population information about the H1N1 proteome, we seek to answer two questions: (1) do residues on the protein surfaces evolve faster than the protein core residues consistently across all proteins that constitute the influenza proteome? and (2) in spite of the rapid evolution of surface residues in influenza proteins, are there any protein regions on the protein surface that do not evolve? To answer these questions, we first built phylogenetically-aware models of the patterns of surface and interior substitutions. Employing these models, we found a single coherent pattern of faster evolution on the protein surfaces that characterizes all influenza proteins. The pattern is consistent with the events of inter-species reassortment, the worldwide introduction of the flu vaccine in the early 80’s, as well as the differences caused by the geographic origins of the virus. Next, we developed an automated computational pipeline to comprehensively detect regions of the protein surface residues that were 100% conserved over multiple years and in multiple host species. We identified conserved regions on the surface of 10 influenza proteins spread across all avian, swine, and human strains; with the exception of a small group of isolated strains that affected the conservation of three proteins. Surprisingly, these regions were also unaffected by genetic variation in the pandemic 2009 H1N1 viral population data obtained from deep sequencing experiments. Finally, the conserved regions were intrinsically related to the intra-viral macromolecular interaction interfaces. Our study may provide further insights towards the identification of novel protein targets for influenza antivirals.
Influenza vaccination is one of the major options to counteract the effects of influenza diseases. Selection of an effective vaccine strain is the key to the success of an effective vaccination program since vaccine protection can only be achieved when the selected influenza vaccine strain matches the antigenic variants causing future outbreaks. Identification of an antigenic variant is the first step to determine whether vaccine strain needs to be updated. Antigenic distance derived from immunological assays, such as hemagglutination inhibition, is commonly used to measure the antigenic closeness between circulating strains and the current influenza vaccine strain. Thus, consensus on an explicit and robust antigenic distance measurement is critical in influenza surveillance. Based on the current seasonal influenza surveillance procedure, we propose and compare three antigenic distance measurements, including Average antigenic distance (A-distance), Mutual antigenic distance (M-distance), and Largest antigenic distance (L-distance). With the assistance of influenza antigenic cartography, our simulation results demonstrated that M-distance is a robust influenza antigenic distance measurement. Experimental results on both simulation and seasonal influenza surveillance data demonstrate that M-distance can be effectively utilized in influenza vaccine strain selection.
Antigenic characterization of emerging and re-emerging viruses is necessary for the prevention of and response to outbreaks, evaluation of infection mechanisms, understanding of virus evolution, and selection of strains for vaccine development. Primary analytic methods, including enzyme-linked immunosorbent/lectin assays, hemagglutination inhibition, neuraminidase inhibition, micro-neutralization assays, and antigenic cartography, have been widely used in the field of influenza research. These techniques have been improved upon over time for increased analytical capacity, and some have been mobilized for the rapid characterization of the SARS-CoV-2 virus as well as its variants, facilitating the development of highly effective vaccines within 1 year of the initially reported outbreak. While great strides have been made for evaluating the antigenic properties of these viruses, multiple challenges prevent efficient vaccine strain selection and accurate assessment. For influenza, these barriers include the requirement for a large virus quantity to perform the assays, more than what can typically be provided by the clinical samples alone, cell- or egg-adapted mutations that can cause antigenic mismatch between the vaccine strain and circulating viruses, and up to a 6-month duration of vaccine development after vaccine strain selection, which allows viruses to continue evolving with potential for antigenic drift and, thus, antigenic mismatch between the vaccine strain and the emerging epidemic strain. SARS-CoV-2 characterization has faced similar challenges with the additional barrier of the need for facilities with high biosafety levels due to its infectious nature. In this study, we review the primary analytic methods used for antigenic characterization of influenza and SARS-CoV-2 and discuss the barriers of these methods and current developments for addressing these challenges.
Locating areas where genetic change is inhibited can illuminate underlying processes that drive evolution of pathogens. The persistence of highly pathogenic H5N1 avian influenza in Vietnam since 2003, and the continuous molecular evolution of Vietnamese avian influenza viruses, indicates that local environmental factors are supportive not only of incidence but also of viral adaptation. This article explores whether gene flow is constant across Vietnam, or whether there exist boundary areas where gene flow exhibits discontinuity. Using a dataset of 125 highly pathogenic H5N1 avian influenza viruses, principal components analysis and wombling analysis are used to indicate the location, magnitude, and statistical significance of genetic boundaries. Results show that a small number of geographically minor boundaries to gene flow in highly pathogenic H5N1 avian influenza viruses exist in Vietnam, but that overall there is little division in genetic exchange. This suggests that differences in genetic characteristics of viruses from one region to another are not the result of barriers to H5N1 viral exchange in Vietnam, and that H5N1 avian influenza is able to spread relatively unimpeded across the country.
An interactive clustering model based on positional weight matrices is described and results obtained using the model to analyze gene regulation patterns in archaea are presented. The 5′ flanking sequences of ORFs identified in four archaea, Sulfolobus solfataricus, Pyrobaculum aerophilum, Halobacterium sp. NRC-1, and Pyrococcus abyssi, were clustered using the model. Three regular patterns of clusters were identified for most ORFs. One showed genes with only a ribosome-binding site; another showed genes with a transcriptional regulatory region located at a constant location with respect to the start codon. A third pattern combined the previous two. Both P. aerophilum and Halobacterium sp. NRC-1 exhibited clusters of genes that lacked any regular pattern. Halobacterium sp. NRC-1 also presented regular features not seen in the other organisms. This group of archaea seems to use a combination of eubacterial and eukaryotic regulatory features as well as some unique to individual species. Our results suggest that interactive clustering may be used to examine the divergence of the gene regulatory machinery in archaea and to identify the presence of archaea-specific gene regulation patterns.
Reduced seasonal influenza vaccine effectiveness (VE) was observed in individuals who received repeated annual vaccinations. Preexisting influenza antibody levels were also found inversely correlated with postvaccination titers. These reports suggest that preexisting immunity may affect contemporary seasonal vaccine performance.
Abstract Background In addition to causing the pandemic influenza outbreaks of 1918 and 2009, subtype H1N1 influenza A viruses (IAVs) have caused seasonal epidemics since 1977. Antigenic property of influenza viruses are determined by both protein sequence and N-linked glycosylation of influenza glycoproteins, especially hemagglutinin (HA). The currently available computational methods are only considered features in protein sequence but not N-linked glycosylation. Results A multi-task learning sparse group least absolute shrinkage and selection operator (LASSO) (MTL-SGL) regression method was developed and applied to derive two types of predominant features including protein sequence and N-linked glycosylation in hemagglutinin (HA) affecting variations in serologic data for human and swine H1N1 IAVs. Results suggested that mutations and changes in N-linked glycosylation sites are associated with the rise of antigenic variants of H1N1 IAVs. Furthermore, the implicated mutations are predominantly located at five reported antibody-binding sites, and within or close to the HA receptor binding site. All of the three N-linked glycosylation sites (i.e. sequons NCSV at HA 54, NHTV at HA 125, and NLSK at HA 160) identified by MTL-SGL to determine antigenic changes were experimentally validated in the H1N1 antigenic variants using mass spectrometry analyses. Compared with conventional sparse learning methods, MTL-SGL achieved a lower prediction error and higher accuracy, indicating that grouped features and MTL in the MTL-SGL method are not only able to handle serologic data generated from multiple reagents, supplies, and protocols, but also perform better in genetic sequence-based antigenic quantification. Conclusions In summary, the results of this study suggest that mutations and variations in N-glycosylation in HA caused antigenic variations in H1N1 IAVs and that the sequence-based antigenicity predictive model will be useful in understanding antigenic evolution of IAVs.
A striking characteristic of the past four influenza pandemic outbreaks in the United States has been the multiple waves of infections. However, the mechanisms responsible for the multiple waves of influenza or other acute infectious diseases are uncertain. Understanding these mechanisms could provide knowledge for health authorities to develop and implement prevention and control strategies.We exhibit five distinct mechanisms, each of which can generate two waves of infections for an acute infectious disease. The first two mechanisms capture changes in virus transmissibility and behavioral changes. The third mechanism involves population heterogeneity (e.g., demography, geography), where each wave spreads through one sub-population. The fourth mechanism is virus mutation which causes delayed susceptibility of individuals. The fifth mechanism is waning immunity. Each mechanism is incorporated into separate mathematical models, and outbreaks are then simulated. We use the models to examine the effects of the initial number of infected individuals (e.g., border control at the beginning of the outbreak) and the timing of and amount of available vaccinations.Four models, individually or in any combination, reproduce the two waves of the 2009 H1N1 pandemic in the United States, both qualitatively and quantitatively. One model reproduces the two waves only qualitatively. All models indicate that significantly reducing or delaying the initial numbers of infected individuals would have little impact on the attack rate. Instead, this reduction or delay results in a single wave as opposed to two waves. Furthermore, four of these models also indicate that a vaccination program started earlier than October 2009 (when the H1N1 vaccine was initially distributed) could have eliminated the second wave of infection, while more vaccine available starting in October would not have eliminated the second wave.
Influenza virus antigenic variants continue to emerge and cause disease outbreaks. Time-consuming, costly, and middle-throughput serologic methods using virus isolates are routinely used to identify influenza antigenic variants for vaccine strain selection. However, the resulting data are notoriously noisy and difficult to interpret and integrate because of variations in reagents, supplies, and protocol implementation. A novel method without such limitations is needed for antigenic variant identification.
Vietnam is one of the countries most affected by outbreaks of H5N1 highly pathogenic avian influenza viruses. First identified in Vietnam in poultry in 2001 and in humans in 2004, the virus has since caused 111 cases and 56 deaths in humans. In 2003/2004 H5N1 outbreaks, nearly the entire poultry population of Vietnam was culled. Our earlier study (Wan et al., 2008, PLoS ONE, 3(10): e3462) demonstrated that there have been at least six independent H5N1 introductions into Vietnam and there were nine newly emerged reassortants from 2001 to 2007 in Vietnam. H5N1 viruses in Vietnam cluster distinctly around Hanoi and Ho Chi Minh City. However, the nature of the relationship between genetic divergence and geographic patterns is still unclear.In this study, we hypothesized that genetic distances between H5N1 viruses in Vietnam are correlated with geographic distances, as the result of distinct population and environment patterns along Vietnam's long north to south longitudinal extent. Based on this hypothesis, we combined spatial statistical methods with genetic analytic techniques and explicitly used geographic space to explore genetic evolution of H5N1 highly pathogenic avian influenza viruses at the sub-national scale in Vietnam. Our dataset consisted of 125 influenza viruses (with whole genome sets) isolated in Vietnam from 2003 to 2007. Our results document the significant effect of space and time on genetic evolution and the rise of two regional centers of genetic mixing by 2007. These findings give insight into processes underlying viral evolution and suggest that genetic differentiation is associated with the distance between concentrations of human and poultry populations around Hanoi and Ho Chi Minh City.The results show that genetic evolution of H5N1 viruses in Vietnamese domestic poultry is highly correlated with the location and spread of those viruses in geographic space. This correlation varies by scale, time, and gene, though a classic isolation by distance pattern is observed. This study is the first to characterize the geographic structure of influenza viral evolution at the sub-national scale in Vietnam and can shed light on how H5N1 HPAIVs evolve in certain geographic settings.
In March 2017, a novel highly pathogenic avian influenza A(H7N9) virus was detected at two commercial broiler breeder facilities in Tennessee, United States. In this study, a wild bird low pathogenic avian influenza A virus, A/blue-winged teal/Wyoming/AH0099021/2016(H7N9), was shown to be the probable precursor of the novel H7N9 virus; this low pathogenic virus has eight possible progenitor genes sharing > 99% sequence identity with the novel H7N9 virus. Phylogeographic analyses showed that viral gene constellations that formed and circulated among dabbling ducks contributed to the emergence of the novel H7N9 virus. This is in contrast to the virus that caused the 2016 H7N8 outbreak, which had more genetic contributions from viruses circulating among diving ducks. Study findings support the need for ongoing wild bird surveillance to monitor circulating viruses and to understand possible evolutionary pathways of virus emergence in poultry.
The coronavirus disease 2019 pandemic has engulfed the world and is the highlight of medical community at this time. As humanity fights the battle against this virus, questions are arising regarding the appropriate management of at risk patient populations. The immunocompromised cohort is particularly susceptible to this infection, and we have tried to explore the medical management of one such group, which is composed of individuals with inflammatory bowel disease (IBD). There is limited data on the management of IBD during the ongoing pandemic. Several medical societies have put forth suggestions on how to manage immunocompromised patients in order to minimize risk of developing coronavirus disease 2019. This review aims to present available recommendations from experts and provides an insight on preventive and therapeutic strategies that can be implemented for the medical management of patients with IBD. We anticipate that as more information arises, new guidelines will emerge.
DNA-binding proteins play important roles in various cellular processes, and the identification of DNA-binding proteins is important for understanding and interpreting protein function. This manuscript presents algorithms for feature representation based on primary protein sequences and selective ensemble classification. We first propose a multi-source interaction fusion feature representation model that simultaneously considers interactions among physicochemical properties, evolutionary information, and gap distances between residues. We also provide a selective ensemble algorithm based on gap distances that yields differential base classifiers by selecting the feature subspaces. The selective ensemble algorithm improves the generalization ability of the integrated classifiers. We then compare the proposed algorithms with some state-of-the-art methods using multiple datasets. The experimental results show that the proposed algorithms are competitive and effectively identify DNA-binding proteins. The major contributions of the present study are the establishment of a model and algorithm for feature representation that involves interaction efforts and the development of a selective ensemble classification algorithm based on parameter perturbation. The proposed algorithms can also be applied to other biological questions related to amino acid sequences.
Domestic poultry act as a reservoir for persistent H5N1 endemicity in Vietnam, and the circulation of poultry flocks across farms and to market is thought to drive the spatial movement and evolution of avian influenza viruses. Using a dataset of complete or nearly full genomic sequences from highly pathogenic H5N1 avian influenza viruses collected in domestic poultry in Vietnam from 2003 to 2007, we explore potential differences in genetic characteristics according to species of isolation and the spatiotemporal characteristics of the viruses. Clustering algorithms and AN OVA indicate that H5N1 viruses in Vietnam show differences in the amount of genetic change that chicken viruses experience as compared to duck viruses, with duck viruses showing higher rates of molecular evolution on all eight of influenza's gene segments. There also exist distinct patterns of genetic differentiation according to the year in which they were isolated. These findings suggest that genetic evolution of avian influenza viruses is continuous through time but could also be mediated by the species in which the viruses occur, information that has implications for prevention efforts. Las aves comerciales actúan como un reservorio para el carácter endémico persistente de los virus H5N1 en Vietnam, y se cree que la circulación de aves comerciales de las granjas a los mercados establece el movimiento espacial y la evolución del virus de la influenza aviar. Mediante el uso de un conjunto de datos de secuencias genómicas completas o casi completas de los virus H5N1 altamente patógenos de la influenza aviar procedentes de aves domésticas en Vietnam entre los años 2003 al 2007, se exploraron las posibles diferencias en las características genéticas según la especie de aislamiento y las características espacio-temporales de los virus. Los algoritmos de agrupamiento y el análisis de varianza indican que los virus H5N1 en Vietnam muestran diferencias en la cantidad de cambio genético que los virus de pollo experimentan en comparación con los virus de pato, con los virus de patos que muestran tasas más altas de evolución molecular de los ocho segmentos genéticos del virus de la influenza. También existen distintos patrones de diferenciación genética según el año en el que fueron aislados. Estos hallazgos sugieren que la evolución genética de los virus de la influenza aviar es continua a través del tiempo, pero también podría estar mediada por la especie en la que los virus se presentan, esta información tiene implicaciones para los esfuerzos de prevención.
Ascoviruses, iridoviruses, asfarviruses and poxviruses are all cytoplasmic DNA viruses. The evolutionary origins of cytoplasmic DNA viruses have never been fully addressed. Morphological, genetic and molecular data were used to test if all four cytoplasmic virus families (Ascoviridae, Iridoviridae, Asfarviridae, and Poxvirirdae) evolved from nuclear replicating baculoviruses and how the four virus groups are related. Molecular phylogenetic analyses using DNA polymerase predicted that cytoplasmic DNA viruses might have evolved from nuclear replicating baculoviruses, and that poxviruses and asfarviruses share a common ancestor with iridoviruses. These three cytoplasmic viruses again shared a common ancestor with ascoviruses. Morphological and genetic data predicted the same evolutionary trend as molecular data predicted. A genome sequence comparison showed that ascoviruses have more baculovirus protein homologues than do iridoviruses, which suggested that ascoviruses have evolved from baculoviruses and iridoviruses evolved from ascoviruses. Poxviruses showed genetic and morphological similarity to other cytoplamic viruses, such as ascoviruses, suggesting it has undergone reticulate evolution via hybridization, recombination and lateral gene transfer with other viruses. Within the ascovirus family, we tested if molecular phylogenetic analyses agree with biological inference; that is, ascovirus had an evolutionary trend of increasing genome size, expanding host range and widening tissue tropism for these viruses. Both molecular and biological data predicted this evolutionary trend. The phylogenetic relationship among the four species of ascovirus was predicted to be that TnAV-2 and HvAV-3 shared a common ancestor with SfAV-1 and the three virus species again shared a common ancestor with DpAV-4.
In this study, we present a systematic exploration of the core/periphery structures in protein interaction networks (PINs). First, the concepts of cores and peripheries in PINs are defined. Then, computational methods are proposed to identify cores from PINs. Application of these methods to a combined yeast PIN has identified 110 k-plex cores and 138 star cores. Based on more precise structural characteristics, our studies reveal new prospects of principles and roles of proteins. Our results show that, aside from connectivity, the structural variations between different types of proteins are also related to the variation in biological properties. Two classes of 1-peripheral proteins have been identified: party peripheries, which are more likely to be part of protein complex, and connector peripheries, which are more likely connected to different complexes or individual proteins. This study may facilitate the understanding of the topological characteristics of proteins in interaction networks and thus help elucidate the organization of cellular systems.
DOI : 10.1109/bibm.2008.9 Anahtar Kelimeler :
Proteins, Pins, Biology computing, Biomedical computing, Electronic mail, Fungi, Cellular networks, Biological system modeling, Biological systems, Assembly, bioinformatics, cellular biophysics, microorganisms, molecular configurations, proteins, proteomics, PIN core structures, PIN periphery structures, protein interaction networks, protein structure-property relationship, computational methods, PIN core identification, yeast PIN, k-plex cores, star cores, connectivity, peripheral proteins, party peripheries, connector peripheries, protein topological characteristics, cellular system organization, core, periphery, protein interaction network
In China, approximately 20% of the animal original influenza A viruses have molecular markers of amantadine resistance. Through phylogenetic data analyses and geospatial statistical analyses, this study suggests emergence of amantadine resistance in animal influenza could be due to selection pressures in China, for example, amantadine usage in some areas.
DOI : 10.1016/j.meegid.2013.09.004 Anahtar Kelimeler :
Influenza A virus, Animal origin influenza A virus, Amantadine resistance, Drug resistance, Phylogenetic analysis, Geospatial analysis
ISSN: 1567-1348 Cilt: 20 Sayfa: 298-303
This study identifies population and environment drivers of genetic change in H5N1 avian influenza viruses (AIV) in Vietnam using a landscape genetics approach. While prior work has examined how combinations of local-level environmental variables influence H5N1 occurrence, this research expands the analysis to the complex genetic characteristics of H5N1 viruses. A dataset of 125 highly pathogenic H5N1 AIV isolated in Vietnam from 2003 to 2007 is used to explore which population and environment variables are correlated with increased genetic change among viruses. Results from non-parametric multidimensional scaling and regression analyses indicate that variables relating to both the environmental and social ecology of humans and birds in Vietnam interact to affect the genetic character of viruses. These findings suggest that it is a combination of suitable environments for species mixing, the presence of high numbers of potential hosts, and in particular the temporal characteristics of viral occurrence, that drive genetic change among H5N1 AIV in Vietnam.
Bovine respiratory disease (BRD) is economically significant, and influenza D virus (IDV) is commonly identified in cattle with BRD. Mannheimia haemolytica (MHA) is an opportunistic bacterial contributor to BRD; surveillance data suggest that MHA and IDV co-infection occurs in cattle. The objective of this study was to evaluate the synergistic pathogenesis in cattle co-infected with IDV and MHA. Sixteen dairy calves were randomly assigned to four groups of four calves. The IDV + MHA + group received D/bovine/C00046 N/Mississippi/2014 (D/46 N) intranasally at 0 days post-inoculation (DPI) and Mannheimia haemolytica D153 (MHA D153) intratracheally at 5 DPI. The IDV + MHA- group received only D/46 N at 0 DPI; the IDV-MHA + group received only MHA D153 at 5 DPI; and the IDV-MHA- group received neither agent. Clinical scores were calculated twice daily. At 10 DPI, IDV + MHA+, IDV-MHA+, and IDV-MHA- calves were euthanized and evaluated for pathologic lesions. The IDV + groups seroconverted to IDV by 10 DPI. Clinical scores were higher in IDV + groups than IDV- groups on 2–5 DPI (p = 0.001). After MHA challenge on 5 DPI, clinical scores (6–10 DPI) were slightly lower in IDV+MHA+ group than IDV-MHA+ group (p < 0.05) but not significantly different between MHA+ groups and MHA- groups. The average gross pathology score was higher for IDV-MHA+ group than groups IDV-MHA- and IDV+MHA+; however, no significant differences were identified among groups. Under the conditions of this study, infection with IDV before MHA enhance neither clinical disease nor lung pathology, relative to calves infected with MHA alone.