We address the hypothesis that bioinformatics analysis can effectively identify miRNAs and target genes associated with the innate immune response to Staphylococcus aureus-induced mastitis in cows. Gram-positive bacteria, such as S. aureus, are some of the mastitis pathogens that cause subclinical infection and pose one of the most severe threats to dairy cattle due to their contagiousness and incidence. Therefore, newer molecular markers must be identified to predict bovine mastitis non-invasively with low error and high specificity. To address this, we conducted in-silico analysis to identify hub genes related to the innate immune system specific to the S. aureus pathogen in subclinical mastitis infections in cows, and experimental validation of the miRNAs of these determined hub genes was completed. As a result of bioinformatic analysis, we identified five hub genes (TLR2, MYD88, CASP4, NOD2 and TBK1) related to the innate immune system that are specific to the S. aureus pathogen. We investigated the expression levels of miRNAs associated with these genes (bta-miR-15a, bta-miR-16b, bta-miR-23a, bta-miR-27a-3p, bta-miR-103, bta-miR-146b, and bta-mir-374b) using real-time quantitative PCR. Except for bta-miR-15a and bta-miR-23a, all miRNAs studied varied in expression between healthy cows and cows with subclinical mastitis infected with S. aureus. This is the first study to bioinformatically determine hub genes specific to the innate immune system and the S. aureus pathogen in subclinical mastitis infections and then validate the determined miRNAs in milk between healthy and subclinical mastitis infected cows. The findings of our study expand our understanding of the roles of these miRNAs in cows with S. aureus-infected subclinical mastitis.

This review article describes the co-evolution of structural biology as a discipline and the Protein Data Bank (PDB), established in 1971 as the first open-access data resource in biology by like-minded structural scientists. As the PDB archive grew in size and scope to encompass macromolecular crystallography, NMR spectroscopy, and cryo-electron microscopy, new technologies were developed to ingest, validate, curate, store, and distribute the information. Community engagement ensured that the needs of structural biologists (data depositors) and data consumers were met. Today, the archive houses more than 230,000 experimentally determined structures of proteins, nucleic acids, and macromolecular machines and their complexes with one another and small-molecule ligands. Aggregate costs of PDB data preservation are ~1% of the cost of structure determination. The enormous impact of PDB data on basic and applied research and education across the natural and medical sciences is presented and highlighted with illustrative examples. Enablement of de novo protein structure prediction (AlphaFold2, RoseTTAfold, OpenFold, etc.) is the most widely appreciated benefit of having a corpus of rigorously validated, expertly curated 3D biostructure data.

In working towards meeting the rapidly rising demand for livestock products in the face of challenges such as climate change, limited forage land availability and inadequacies in water availability and quality, it is imperative to consider sustainability in farm or grazing land management and water resources conservation as well as biodiversity management and conservation, etc. Geophysics, GIS, remote sensing, etc., have been useful tools. Emerging technologies such as biotechnology, advanced sensor technologies, machine learning algorithms, internet of things, artificial intelligence, unmanned aerial vehicles, robotics, etc., are also being employed in agriculture and other aspects of human concerns. There are potentials for better utilization of these emerging technologies and more in livestock production and management. However, a limitation is that relevant knowledge and skills are still relatively inadequate, especially in developing countries; hence the need for this review, which is an enhancement of knowledge for research and improved productivity. Efforts should be made to advance in knowledge and skills acquisition so as to optimize this development for improved livestock production and management.

The GABA type A receptor (GABAAR) belongs to the family of pentameric ligand-gated ion channels and plays a key role in inhibition in adult mammalian brains. Dysfunction of this macromolecule may lead to epilepsy, anxiety disorders, autism, depression, and schizophrenia. GABAAR is also a target for multiple physiologically and clinically relevant modulators, such as benzodiazepines (BDZs), general anesthetics, and neurosteroids. The first GABAAR structure appeared in 2014, but the past years have brought a particularly abundant surge in structural data for these receptors with various ligands and modulators. Although the open conformation remains elusive, this novel information has pushed the structure–function studies to an unprecedented level. Electrophysiology, mutagenesis, photolabeling, and in silico simulations, guided by novel structural information, shed new light on the molecular mechanisms of receptor functioning. The main goal of this review is to present the current knowledge of GABAAR functional and structural properties. The review begins with an outline of the functional and structural studies of GABAAR, accompanied by some methodological considerations, especially biophysical methods, enabling the reader to follow how major breakthroughs in characterizing GABAAR features have been achieved. The main section provides a comprehensive analysis of the functional significance of specific structural elements in GABAARs. We additionally summarize the current knowledge on the binding sites for major GABAAR modulators, referring to the molecular underpinnings of their action. The final chapter of the review moves beyond examining GABAAR as an isolated macromolecule and describes the interactions of the receptor with other proteins in a broader context of inhibitory plasticity. In the final section, we propose a general conclusion that agonist binding to the orthosteric binding sites appears to rely on local interactions, whereas conformational transitions of bound macromolecule (gating) and allosteric modulation seem to reflect more global phenomena involving vast portions of the macromolecule.

Toxoplasmosis is a significant public health concern with limited therapeutic options. The medicines for malaria venture (MMV) developed the pandemic response box (PRB) containing 400 drug-like molecules with broad pathogen activity. The aim of this work is to evaluate PRB compounds for their anti-Toxoplasma gondii activity and identify promising candidates for further evaluation. Screening identified 42 selective compounds with half effective concentration (EC50) ranging from 2.4 to 913.1 nm and half cytotoxic concentration (CC50) ranging from 6 μm to >50 μm. Selectivity index (SI) values (CC50/EC50) ranged from 11 to 17 708. Based on its in silico and in vitro profile and its commercial availability, RWJ-67657 was selected for further studies. Molecular docking analysis showed RWJ-67657 is predicted to bind to T. gondii p38 mitogen-activated protein kinase (TgMAPK). Oral administration of RWJ-67657 (20 mg kg day−1/10 days) significantly reduced parasite burden in chronically infected mice compared to mock-treated group (P < 0.01). These findings highlight the PRB as a promising source for anti-T. gondii compounds, with several showing favourable drug properties, including MMV1634492, MMV002731, MMV1634491, MMV1581551, MMV011565, MMV1581558, MMV1578577, MMV233495 and MMV1580482, firstly described here as anti-T. gondii agents. RWJ-67657 emerges as a valuable drug candidate for experimental chronic cerebral toxoplasmosis therapy.

Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph–based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly “Question-of-the-Month (QotM) Challenge” series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and ATP1A3-related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system.

Genomic epidemiology is routinely used worldwide to interrogate infectious disease dynamics. Multiple computational tools exist that reconstruct transmission networks by coupling genomic data with epidemiological models. Resulting inferences can improve our understanding of pathogen transmission dynamics, and yet the performance of these tools has not been evaluated for tuberculosis (TB), a disease process with complex epidemiology including variable latency and within-host heterogeneity. Here, we performed a systematic comparison of six publicly available transmission reconstruction models, evaluating their accuracy when predicting transmission events in simulated and real-world Mycobacterium tuberculosis outbreaks. We observed variability in the number of transmission links that were predicted with high probability (P ≥ 0.5) and low accuracy of these predictions against known transmission in simulated outbreaks. We also found a low proportion of epidemiologically supported case–contact pairs were identified in our real-world TB clusters. The specificity of all models was high, and a relatively high proportion of the total transmission events predicted by some models were true links, notably with TransPhylo, Outbreaker2, and Phybreak. Our findings may inform the choice of tools in TB transmission analyses and underscore the need for caution when interpreting transmission networks produced using probabilistic approaches.

Precision Medicine is an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle. Autoimmune diseases are those in which the body’s natural defense system loses discriminating power between its own cells and foreign cells, causing the body to mistakenly attack healthy tissues. These conditions are very heterogeneous in their presentation and therefore difficult to diagnose and treat. Achieving precision medicine in autoimmune diseases has been challenging due to the complex etiologies of these conditions, involving an interplay between genetic, epigenetic, and environmental factors. However, recent technological and computational advances in molecular profiling have helped identify patient subtypes and molecular pathways which can be used to improve diagnostics and therapeutics. This review discusses the current understanding of the disease mechanisms, heterogeneity, and pathogenic autoantigens in autoimmune diseases gained from genomic and transcriptomic studies and highlights how these findings can be applied to better understand disease heterogeneity in the context of disease diagnostics and therapeutics.

Tetraspanins are a superfamily of transmembrane proteins that in flatworms have structural roles in the development, maturation or stability of the tegument. Several tetraspanins are considered as potential candidates for vaccines or drugs against helminths. Monopisthocotylean monogeneans are ectoparasites of fish that are health hazards for farmed fish. The aim of this study was to identify in silico putative tetraspanins in the genomic datasets of four monopisthocotylean species. The analysis predicted and classified 40 tetraspanins in Rhabdosynochus viridisi, 39 in Scutogyrus longicornis, 22 in Gyrodactylus salaris and 13 in Neobenedenia melleni, belonging to 13 orthologous groups. The high divergence of tetraspanins made it difficult to annotate their function. However, a conserved group was identified in different metazoan taxa. According to this study, metazoan tetraspanins can be divided into 17 monophyletic groups. Of the 114 monogenean tetraspanins, only seven were phylogenetically close to tetraspanins from non-platyhelminth metazoans, which suggests that this group of proteins shows rapid sequence divergence. The similarity of the monopisthocotylean tetraspanins was highest with trematodes, followed by cestodes and then free-living platyhelminths. In total, 27 monopisthocotylean-specific and 34 flatworm-specific tetraspanins were identified. Four monogenean tetraspanins were orthologous to TSP-1, which is a candidate for the development of vaccines and a potential pharmacological target in trematodes and cestodes. Although studies of tetraspanins in parasitic flatworms are scarce, this is an interesting group of proteins for the development of new methods to control monogeneans.

Maize is one of the three staple foods in the world. The white variety represents 60% of the maize importation with a world consumption of 1125 million tons in 2019/2020. Currently, new technologies could contribute to the analysis of this seed, supporting quality control and improvement. This study aims to carry out the morphological and proteomic comparison between the hybrid MR2008 and its parental lines LUG03 and CML491 through mass spectrometry and bioinformatics analysis. Herein, we identified that 34.8% of the hybrid proteome differs from the parental proteome. Also, ontological and morphological analyses determined that the hybrid exhibits more characteristics related to CML491 than LUG03, for example, metabolic pathways and enzymes, such as anthocyanidin 3-O-glucosyltransferase (UniProt P16166). This analysis allowed the identification of dominant characters, metabolic pathways and confirms the utility of this methodology in agricultural practices, mainly in processes of selection and quality control of a crop.

Echinococcus granulosus sensu lato (s.l.) is a zoonotic parasite that causes cystic echinococcosis (CE) in humans. However, E. granulosus sensu stricto (s.s.) is considered the predominant species in CE infections worldwide. According to the population genetic diversity and structure of E. granulosus s.l., gene flow can explain the parasite drift among the neighbouring countries of Pakistan. The mitochondrial (mt) co1 (n = 47), nadh1 (n = 37) and cytb (n = 35) nucleotide sequences of E. granulosus s.l. isolates from Pakistan, Iran, China and India were retrieved from the National Centre for Biotechnology Information database to determine the genealogical relationships. The sequences were grouped as the mt-co1 (genotypes G1 and G3, G6-G7), mt-cytb (genotypes G1 and G3), and mt-nadh1(genotypes G1 and G3). The data were analysed using bioinformatic tools. A total of 19 polymorphic sites for the mt-co1 sequence (374 bp) were observed of which 31.6% (6/19) were parsimony-informative sites. Unique singleton haplotypes within the E. granulosus s.s. haplotype network based on the mt-co1 gene were highly prevalent (68.4%; 13/19) in Pakistani isolates followed by Chinese, Indian and Iranian isolates; four polymorphic sites were detected in the E. canadensis (G6/G7). In E. canadensis mt-co1 haplotype network, 75% (3/4) unique singleton haplotypes were from the Iranian isolates. Twelve polymorphic sites were found using the mt-cytb sequence (547 bp); 25% (3/12) were parsimony-informative and there were 66.7% (8/12) unique singleton haplotypes within the mt-cytb haplotype network in E. granulosus s.s. with the most reported from Pakistan followed by Iran and China. 20 polymorphic sites were detected in E. granulosus s.s. mt-nadh1 sequences (743 bp); 20% (4/20) were parsimony-informative. There were 66.7% (8/12) main single haplotypes within the mt-nadh1 haplotype network, with the most reported from Pakistan followed by that from India, Iran and China. The sequence analyses show low nucleotide diversity and high haplotype diversity in general.

Kinetoplastid parasites are responsible for both human and animal diseases across the globe where they have a great impact on health and economic well-being. Many species and life cycle stages are difficult to study due to limitations in isolation and culture, as well as to their existence as heterogeneous populations in hosts and vectors. Single-cell transcriptomics (scRNA-seq) has the capacity to overcome many of these difficulties, and can be leveraged to disentangle heterogeneous populations, highlight genes crucial for propagation through the life cycle, and enable detailed analysis of host–parasite interactions. Here, we provide a review of studies that have applied scRNA-seq to protozoan parasites so far. In addition, we provide an overview of sample preparation and technology choice considerations when planning scRNA-seq experiments, as well as challenges faced when analysing the large amounts of data generated. Finally, we highlight areas of kinetoplastid research that could benefit from scRNA-seq technologies.

The Expanded Program for Immunization Consortium – Human Immunology Project Consortium study aims to employ systems biology to identify and characterize vaccine-induced biomarkers that predict immunogenicity in newborns. Key to this effort is the establishment of the Data Management Core (DMC) to provide reliable data and bioinformatic infrastructure for centralized curation, storage, and analysis of multiple de-identified “omic” datasets. The DMC established a cloud-based architecture using Amazon Web Services to track, store, and share data according to National Institutes of Health standards. The DMC tracks biological samples during collection, shipping, and processing while capturing sample metadata and associated clinical data. Multi-omic datasets are stored in access-controlled Amazon Simple Storage Service (S3) for data security and file version control. All data undergo quality control processes at the generating site followed by DMC validation for quality assurance. The DMC maintains a controlled computing environment for data analysis and integration. Upon publication, the DMC deposits finalized datasets to public repositories. The DMC architecture provides resources and scientific expertise to accelerate translational discovery. Robust operations allow rapid sharing of results across the project team. Maintenance of data quality standards and public data deposition will further benefit the scientific community.

Africa plays a central importance role in the human origins, and disease susceptibility, agriculture and biodiversity conservation. Nigeria as the most populous and most diverse country in Africa, owing to its 250 ethnic groups and over 500 different native languages is imperative to any global genomic initiative. The newly inaugurated Nigerian Bioinformatics and Genomics Network (NBGN) becomes necessary to facilitate research collaborative activities and foster opportunities for skills’ development amongst Nigerian bioinformatics and genomics investigators. NBGN aims to advance and sustain the fields of genomics and bioinformatics in Nigeria by serving as a vehicle to foster collaboration, provision of new opportunities for interactions between various interdisciplinary subfields of genomics, computational biology and bioinformatics as this will provide opportunities for early career researchers. To provide the foundation for sustainable collaborations, the network organises conferences, workshops, trainings and create opportunities for collaborative research studies and internships, recognise excellence, openly share information and create opportunities for more Nigerians to develop the necessary skills to exceed in genomics and bioinformatics. NBGN currently has attracted more than 650 members around the world. Research collaborations between Nigeria, Africa and the West will grow and all stakeholders, including funding partners, African scientists, researchers across the globe, physicians and patients will be the eventual winners. The exponential membership growth and diversity of research interests of NBGN just within weeks of its establishment and the unanticipated attendance of its activities suggest the significant importance of the network to bioinformatics and genomics research in Nigeria.