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.

The storage root of alligatorweed [Alternanthera philoxeroides (Mart.) Griseb.] growing in terrestrial habitats is an important metamorphic organ for its propagation, overwintering, and spread. However, the regulatory mechanism adventitious root expansion to form storage roots is still unclear. To reveal the changes accompanying the root-swelling process, we quantified sugar, soluble protein, and phytohormone content in adventitious and storage roots. Results demonstrated that sucrose, fructose, and soluble protein increased in storage roots, whereas abscisic acid (ABA), indoleacetic acid (IAA), brassinosteroid (BR), gibberellin, jasmonic acid, and cytokinin (trans-zeatin [tZ] and isopentenyladenine [iP] and the corresponding ribosides tZR and iPR). tZ-type (tZR and tZ) content decreased, suggesting the involvement of sugars and hormones in the formation of storage roots. To further reveal the molecular basis of A. philoxeroides’s ability to form storage roots and provide candidate genes for molecular function analyses, we assembled a de novo transcriptome of A. philoxeroides based on four sets of RNA-sequencing data. According to functional annotation and expression profiling, 42 unigenes involved in sucrose synthesis and hydrolysis were identified, in addition to 70, 58, and 78 unigenes in ABA, BR, and IAA signal transduction, respectively. The quantitative reverse transcriptase polymerase chain reaction analysis revealed 21 unigenes involved in sugar metabolism and hormone signal transduction were differentially expressed during the formation of storage roots. These results revealed metabolic changes during the formation of storage roots and provide candidate genes involved in sugar and phytohormone metabolism in A. philoxeroides.

Badh2 of rice is considered to be the major gene responsible for the fragrance in rice. The wild type badh2 allele encodes betaine aldehyde dehydrogenase 2 (BADH2) enzyme while the mutated version of badh2 gene encodes non-functional BADH2 enzyme that leads to the accumulation of 2-acetyl-1-pyrroline (2AP), the principal fragrant compound in rice. There are many mutated recessive alleles causing fragrance in global rice germplasm, although the badh2.1 allele present in Basmati type rice is the most well-known among breeders. In this study, we attempted to reveal potential fragrance causing mutations, and the respective varieties carrying them, through in silico analysis based on the sequences available in the Rice SNP-Seek-Database of International Rice Research Institute. The sequences of 1878 rice accessions from 22 countries were analysed to identify mutations in each exon of badh2 comparatively with the non-fragrant ‘wildtype’ GenBank sequence in Nanjing11, Oryza sativa indica (EU770319.1). Results revealed that 63 varieties from 12 countries possessed the most prevalent allele, badh2.1 having an 8 bp deletion and three single nucleotide polymorphisms in the 7th exon. The second most prevalent allele in genotypes from Asia was badh2.7 having a ‘G’ insertion in the 14th exon. A novel allele with a T deletion in 9th exon was detected in a Thai rice accession. Rice varieties containing either badh2.1 or badh2.7 alleles could be identified with DNA markers for badh2.1 (frg) and badh2.7 (Bad2.7CAPS). The marker, Bad2.7CAPS, co-segregated with the fragrance phenotype in two crosses, confirming the possibility of employing it in marker assisted breeding.