Naveen Duhan

Avian metapneumovirus (aMPV) subtypes A and B emerged in United States poultry in late 2023 and early 2024, prompting genome-scale surveillance from clinical samples. Here, we developed and optimized targeted amplicon sequencing (TAS) assays for both subtypes and applied them to 104 subtype-positive clinical samples collected from chicken and turkey farms across nine US states between early 2024 and early 2026. TAS recovered 91 genomes suitable for comparative analysis, including 44 aMPV-A and 47 aMPV-B sequences, with successful recovery extending to Ct values of 34.6 for aMPV-A and 31.2 for aMPV-B. Recovered genomes showed near-complete breadth and high mapping efficiency. Phylogenetic analyses of both G-gene and whole-genome datasets showed that US aMPV-A field strains formed a distinct monophyletic lineage within group IV and resolved into three closely related clusters. Cluster 2, first recognized in North Carolina and later detected in Ohio, spread across 30 turkey and chicken farms. Cluster 2 genomes were defined by a concentrated G-protein hotspot within residues 209–275, and most North Carolina Cluster 2 genomes carried 10–11 nonsynonymous substitutions in this region, including multiple proline substitutions suggestive of local structural change. Missouri Cluster 3 remained cohesive but distinct from both Cluster 1 and Cluster 2 in the G-gene and whole-genome trees. In contrast, US aMPV-B field strains remained highly homogeneous across hosts and states, with more than 99% nucleotide identity by both G-gene and WGS analyses. We also identified 12 vaccine-derived genomes on both vaccinated and nonvaccinated farms. These included six genomes related to aMPV-A vaccine and six related to aMPV-B vaccines (VCO3/50 and 1062), all of which retained vaccine-defining markers together with additional substitutions consistent with continued circulation after vaccine use in the field. Selection analyses showed that the G gene had the highest gene-wise dN/dS ratio in both subtypes. Additional elevated signal was observed in SH and M2, and candidate positively or episodically selected codons were concentrated in the subtype A Cluster 2 G-gene hotspot. These findings show that TAS supports direct-from-sample aMPV genomic surveillance and provides genomic context for field clusters, vaccine-derived lineages, and continued adaptive change in aMPV in US poultry.

Porcine reproductive and respiratory syndrome (PRRS) is a serious disease that causes reproductive failure and late abortion in breeder farms, as well as severe respiratory manifestations in pigs of all ages. In the United States, the swine industry has been affected by PRRSV-2, particularly due to control challenges and the virus’s ongoing evolution. In this study, we conducted a genetic characterization of the 642 PRRSV-2 ORF5 sequences generated from diagnostic submissions at the Animal Disease Research and Diagnostic Laboratory (ADRDL) at South Dakota State University (SDSU). These sequences were generated over a 3-year period from 2023 through 2025, with 208 (32.4%), 130 (20.2%), and 304 (47.4%) sequences in 2023, 2024, and 2025, respectively. These sequences were from swine diagnostic submissions from ten states in the Midwest, including Minnesota (MN; 350/642, 54.5%), South Dakota (SD; 182/642, 28.3%), and Iowa (IA; 73/642, 11.4%), collectively accounting for 94.2% of submitted sequences. The genetic typing revealed that the L1C sub-lineage was the most frequently detected among the submitted sequences, accounting for around 60%, with L1C.5 as the major contributor (371/642). Other sub-lineages, such as L5A, L1A, L1D, and L1H, were also detected, but in smaller numbers. More than 80% of the detected sequences were classified as wild-type or wild-like based on ORF5 identity to commercial vaccine reference strains. Interestingly, this study documented increased detection of the recently reported L1C.5.32 variant, which was the most frequently detected variant in 2025 (123 detections), particularly in Minnesota. Moreover, L1C.5.34, L1C.5.36, L1C.3.25, and L1C.2 variants were also detected, and the L1H.18 variant showed increased detection, especially in 2025. In conclusion, this study provides genetic characterization of PRRSV-2 ORF5 sequences from Midwestern U.S. diagnostic submissions (a key U.S. swine production region), submitted to the SDSU ADRDL.

The organization of subcellular components in a cell is critical for its function and studying cellular processes, protein–protein interactions, identifying potential drug targets, network analysis, and other systems biology mechanisms. Determining protein localization experimentally is time‐consuming and expensive. Due to the need for meticulous experimentation, validation, and data analysis, computational methods provide a quick and accurate alternative. Arabidopsis thaliana , a beneficial model organism in plant biology, facilitates experimentation and applies to other plants. Predicting its proteins' subcellular localization can improve our understanding of cellular processes and have applications in crop improvement and biotechnology. We propose AtSubP‐2.0, an extension of our previously developed and widely used AtSubP v1.0 tool for annotating the Arabidopsis proteome. For precise protein subcellular localization prediction, AtSubP‐2.0 employs a four‐phase strategy. The first phase differentiates between single and dual localization with accuracy (97.66% in fivefold training/testing, 98.10% on independent data) and high Matthews correlation coefficient (0.88 training, 0.90 independent). Single localized proteins are classified into 12 locations at the second phase, with accuracy (98.37% in fivefold training/testing, 97.43% on independent data) and Matthews correlation coefficient (0.94 training, 0.91 independent). The third phase categorizes dual location proteins into nine classes with accuracy (99.65% in fivefold training/testing, 98.16% on independent data) and Matthews correlation coefficient (0.92 training, 0.87 independent). We also employed a fourth phase that classifies the membrane type proteins predicted in phase I into single‐pass and multi‐pass membrane with accuracy (98% in fivefold training/testing, 98.55% on independent data) and a high Matthews correlation coefficient (0.95 training, 0.97 independent). A web‐based prediction server has been implemented for community use and is freely available at https://kaabil.net/AtSubP2/ , including a standalone version. AtSubP2 will help researchers to better understand organelle‐specific functions, cellular processes, and regulatory mechanisms important for plant growth, development, and response to environmental stimuli.

Avian metapneumovirus (aMPV), classified within the Pneumoviridae family, wreaks havoc on poultry health. It typically causes upper respiratory tract and reproductive tract infections, mainly in turkeys, chickens, and ducks. Four subtypes of AMPV (A, B, C, D) and two unclassified subtypes have been identified, of which subtypes A and B are widely distributed across the world. In January 2024, an outbreak of severe respiratory disease occurred on turkey and chicken farms across different states in the US. Metagenomics sequencing of selected tissue and swab samples confirmed the presence of aMPV subtype B. Subsequently, all samples were screened using an aMPV subtype A and B multiplex real-time RT-PCR kit. Of the 221 farms, 124 (56%) were found to be positive for aMPV-B. All samples were negative for subtype A. Six whole genomes were assembled, five from turkeys and one from chickens; all six assembled genomes showed 99.29 to 99.98% nucleotide identity, indicating a clonal expansion event for aMPV-B within the country. In addition, all six sequences showed 97.74 to 98.58% nucleotide identity with previously reported subtype B sequences, e.g., VCO3/60616, Hungary/657/4, and BR/1890/E1/19. In comparison to these two reference strains, the study sequences showed unique 49–62 amino acid changes across the genome, with maximum changes in glycoprotein (G). One unique AA change from T (Threonine) to I (Isoleucine) at position 153 in G protein was reported only in the chicken aMPV sequence, which differentiated it from turkey sequences. The twelve unique AA changes along with change in polarity of the G protein may indicate that these unique changes played a role in the adaptation of this virus in the US poultry. This is the first documented report of aMPV subtype B in US poultry, highlighting the need for further investigations into its genotypic characterization, pathogenesis, and evolutionary dynamics.

Microsatellites, also known as simple sequence repeats (SSRs), are polymorphic loci that play an important role in genome research, animal breeding, and disease control. Ranch animals are important components of agricultural landscape. The ranch animal SSR database, ranchSATdb, is a web resource which contains 15,520,263 putative SSR markers. This database provides a comprehensive tool for performing end-to-end marker selection, from SSRs prediction to generating marker primers and their cross-species feasibility, visualization of the resulting markers, and finding similarities between the genomic repeat sequences all in one place without the need to switch between other resources. The user-friendly online interface allows users to browse SSRs by genomic coordinates, repeat motif sequence, chromosome, motif type, motif frequency, and functional annotation. Users may enter their preferred flanking area around the repeat to retrieve the nucleotide sequence, they can investigate SSRs present in the genic or the genes between SSRs, they can generate custom primers, and they can also execute in silico validation of primers using electronic PCR. For customized sequences, an SSR prediction pipeline called miSATminer is also built. New species will be added to this website’s database on a regular basis throughout time. To improve animal health via genomic selection, we hope that ranchSATdb will be a useful tool for mapping quantitative trait loci (QTLs) and marker-assisted selection. The web-resource is freely accessible at https://bioinfo.usu.edu/ranchSATdb/.

SARS-CoV-2, a novel betacoronavirus strain, has caused a pandemic that has claimed the lives of nearly 6.7M people worldwide. Vaccines and medicines are being developed around the world to reduce the disease spread, fatality rates, and control the new variants. Understanding the protein-protein interaction mechanism of SARS-CoV-2 in humans, and their comparison with the previous SARS-CoV and MERS strains, is crucial for these efforts. These interactions might be used to assess vaccination effectiveness, diagnose exposure, and produce effective biotherapeutics. Here, we present the HuCoPIA database, which contains approximately 100,000 protein-protein interactions between humans and three strains (SARS-CoV-2, SARS-CoV, and MERS) of betacoronavirus. The interactions in the database are divided into common interactions between all three strains and those unique to each strain. It also contains relevant functional annotation information of human proteins. The HuCoPIA database contains SARS-CoV-2 (41,173), SARS-CoV (31,997), and MERS (26,862) interactions, with functional annotation of human proteins like subcellular localization, tissue-expression, KEGG pathways, and Gene ontology information. We believe HuCoPIA will serve as an invaluable resource to diverse experimental biologists, and will help to advance the research in better understanding the mechanism of betacoronaviruses.

Drought stress severely affects the growth and development of rice, especially at the reproductive stage, which results in disturbed metabolic processes, reduced seed-set/grain filling, deteriorated grain quality, declined productivity, and lower yield. Despite the recent advances in understanding the responses of rice to drought stress, there is a need to comprehensively integrate the morpho-physio-biochemical studies with the molecular responses/differential expression of genes and decipher the underlying pathways that regulate the adaptability of rice at various drought-sensitive growth stages. Our comparative analysis of immature panicle from a drought-tolerant (Nagina 22) and a drought-sensitive (IR 64) rice cultivar grown under control (well-watered) and water-deficit/drought stress (treatment, imposed at the reproductive stage) conditions unraveled some novel stress-responsive genes/pathways responsible for reproductive-stage drought stress tolerance. The results revealed a more important role of upregulated (6706) genes in the panicle of N 22 at reproductive-stage drought stress compared to that (5590) in IR 64. Functional enrichment and MapMan analyses revealed that majority of the DEGs were associated with the phytohormone, redox signalling/homeostasis, secondary metabolite, and transcription factor-mediated mitigation of the adverse effects of drought stress in N 22. The upregulated expression of the genes associated with starch/sucrose metabolism, secondary metabolites synthesis, transcription factors, glutathione, linoleic acid, and phenylalanine metabolism in N 22 was significantly more than that in the panicle of IR 64. Compared to IR 64, 2743 genes were upregulated in N 22 under control conditions, which further increased (4666) under drought stress in panicle of the tolerant cultivar. Interestingly, we observed 6706 genes to be upregulated in the panicle of N 22 over IR 64 under drought and 5814 genes get downregulated in the panicle of N 22 over IR 64 under the stress. In addition, RT-qPCR analysis confirmed differential expression patterns of the DEGs. These genes/pathways associated with the reproductive-stage drought tolerance might provide an important source of molecular markers for genetic manipulation of rice for enhanced drought tolerance.

The study of molecular interactions, especially the inter-species protein-protein interactions, is crucial for understanding the disease infection mechanism in plants. These interactions play an important role in disease infection and host immune responses against pathogen attack. Among various critical fungal diseases, the incidences of Karnal bunt (Tilletia indica) around the world have hindered the export of the crops such as wheat from infected regions, thus causing substantial economic losses. Due to sparse information on T. indica, limited insight is available with regard to gaining in-depth knowledge of the interaction mechanisms between the host and pathogen proteins during the disease infection process. Here, we report the development of a comprehensive database and webserver, TritiKBdb, that implements various tools to study the protein-protein interactions in the Triticum species-Tilletia indica pathosystem. The novel ‘interactomics’ tool allows the user to visualize/compare the networks of the predicted interactions in an enriched manner. TritiKBdb is a user-friendly database that provides functional annotations such as subcellular localization, available domains, KEGG pathways, and GO terms of the host and pathogen proteins. Additionally, the information about the host and pathogen proteins that serve as transcription factors and effectors, respectively, is also made available. We believe that TritiKBdb will serve as a beneficial resource for the research community, and aid the community in better understanding the infection mechanisms of Karnal bunt and its interactions with wheat. The database is freely available for public use at http://bioinfo.usu.edu/tritikbdb/.

Microsatellites, or simple sequence repeats (SSRs), are polymorphic loci that play a major role as molecular markers for genome analysis and plant breeding. The legume SSR database is a webserver which contains simple sequence repeats (SSRs) from genomes of 13 legume species. A total of 3,706,276 SSRs are present in the database, 698,509 of which are genic SSRs, and 3,007,772 are non-genic. This webserver is an integrated tool to perform end-to-end marker selection right from generating SSRs to designing and validating primers, visualizing the results and blasting the genomic sequences at one place without juggling between several resources. The user-friendly web interface allows users to browse SSRs based on the genomic region, chromosome, motif type, repeat motif sequence, frequency of motif, and advanced searches allow users to search based on chromosome location range and length of SSR. Users can give their desired flanking region around repeat and obtain the sequence, they can explore the genes in which the SSRs are present or the genes between which the SSRs are bound design custom primers, and perform in silico validation using PCR. An SSR prediction pipeline is implemented where the user can submit their genomic sequence to generate SSRs. This webserver will be frequently updated with more species, in time. We believe that legumeSSRdb would be a useful resource for marker-assisted selection and mapping quantitative trait loci (QTLs) to practice genomic selection and improve crop health. The database can be freely accessed at http://bioinfo.usu.edu/legumeSSRdb/.

The Citrus genus comprises some of the most important and commonly cultivated fruit plants. Within the last decade, citrus greening disease (also known as huanglongbing or HLB) has emerged as the biggest threat for the citrus industry. This disease does not have a cure yet and, thus, many efforts have been made to find a solution to this devastating condition. There are challenges in the generation of high-yield resistant cultivars, in part due to the limited and sparse knowledge about the mechanisms that are used by the Liberibacter bacteria to proliferate the infection in Citrus plants. Here, we present GreeningDB, a database implemented to provide the annotation of Liberibacter proteomes, as well as the host–pathogen comparactomics tool, a novel platform to compare the predicted interactomes of two HLB host–pathogen systems. GreeningDB is built to deliver a user-friendly interface, including network visualization and links to other resources. We hope that by providing these characteristics, GreeningDB can become a central resource to retrieve HLB-related protein annotations, and thus, aid the community that is pursuing the development of molecular-based strategies to mitigate this disease’s impact. The database is freely available at http://bioinfo.usu.edu/GreeningDB/ (accessed on 11 August 2021).