Baobab Index

Aflatoxin contamination of maize remains a global food safety and public health challenge, particularly because it is a dietary staple food and feed ingredient produced across diverse agroecological zones. Although numerous mitigation strategies have been developed, contamination levels remain variable. This reflects complex interactions among climate, soil properties, crop management practices, plant physiological stress and rhizosphere microbial communities. This review synthesizes evidence on key agroecological drivers of aflatoxin contamination in maize, highlighting climate variability, soil properties, crop and nutrient management, cropping systems, and rhizosphere microbial communities as primary determinants. We examine how these factors influence

Genome and metagenome mining have uncovered large repertoires of predicted bacteriocin loci, but ecological interpretation has lagged behind discovery. This review uses bacteriocins as a focused model for a broader problem in antimicrobial gene prediction: sequence identifies encoded potential, but it does not establish expression, product deployment, target engagement, or community-level consequence. We distinguish four forms of bacteriocin "silence": transcriptional silence, where loci are not detectably expressed under tested conditions; conditional silence, where expression or activity emerges only under specific environmental or social cues; phenotypic silence, where expression or product formation does not yield detectable activity in routine assays; and ecological silence, where activity is observed but its consequences for coexistence, exclusion, colonization, or community assembly remain unresolved. We also clarify how this framework extends previous reviews of bacteriocin diversity and microbiome-shaping roles by providing an inference-centered taxonomy that separates encoded potential from demonstrated ecological function. Finally, we propose a function-first roadmap for bacteriocin research, including condition-resolved transcriptomics and proteomics, promoter and cue-dissection experiments, expanded target panels, spatially structured assays, defined consortia and microcosms, producer/non-producer comparisons, and explicit tests of resistance, immunity, and fitness consequences. Treating predicted bacteriocin loci as hypotheses rather than conclusions will improve how the field moves from gene catalogs to causal ecological understanding.

The global spread of carbapenemase- and extended-spectrum β-lactamase (ESBL)-producing Gram-negative bacteria represents a major One Health challenge, driven by interconnected human, animal, and environmental reservoirs. However, genomic data on antimicrobial-resistant bacteria associated with aquatic and terrestrial animals in the United Arab Emirates (UAE) remain scarce, limiting assessment of their potential role in resistance dissemination. This study investigated the phenotypic and genomic characteristics of resistant Gram-negative bacteria recovered from these animal reservoirs. Sixty aquarium-associated samples were collected, yielding 69 Gram-negative isolates. Antimicrobial susceptibility testing, phenotypic detection of ESBL and carbapenemase activity, molecular screening of resistance genes, and whole-genome sequencing of selected isolates were performed. Phylogenetic analyses were conducted to assess evolutionary relatedness with global strains. The isolates were mainly identified as Aquatic and terrestrial animals in the UAE serve as important reservoirs for clinically relevant antimicrobial resistance. These findings underscore the need for integrated One Health genomic surveillance to mitigate the transboundary spread of high-risk resistant lineages.

Antimicrobial resistance (AMR) is a growing global public health threat, particularly in low- and middle-income countries (LMICs), where delayed antimicrobial susceptibility testing (AST) and limited diagnostic capacity complicate timely treatment and antimicrobial stewardship (AMS). Although large-scale surveillance programmes routinely collect antimicrobial susceptibility data, these datasets remain underutilised for predictive analytics. We developed a surveillance-driven machine learning (ML) framework using isolate-level data from African sites participating in the Pfizer Antimicrobial Testing Leadership and Surveillance (ATLAS) programme between 2019 and 2023. Seven antibiotic-specific Extreme Gradient Boosting (XGBoost) models were developed to predict antimicrobial susceptibility using routinely collected microbiological, demographic, clinical, and geographical metadata. Model development included structured data preprocessing, RandomOverSampler-based class balancing restricted to the training data, hyperparameter optimisation using stratified cross-validation, and evaluation on a held-out test dataset. A rule-based MIC interpretation system and interactive dashboard were developed to demonstrate implementation of the analytical workflow. The antibiotic-specific models demonstrated moderate predictive performance, with test accuracies ranging from 57% to 76%. Ceftazidime-Avibactam achieved the highest test accuracy (76%), followed by Gentamicin (65%) and Imipenem (64%), while Amikacin showed the lowest performance (57%). Feature-importance analysis identified bacterial species as consistently among the most influential predictors. In contrast, bacterial family, country of isolate collection, specimen source, clinical specialty, and demographic characteristics contributed to varying degrees across antibiotics. Performance was generally stronger for the more frequently represented susceptible class than for intermediate and resistant isolates. Routinely collected AMR surveillance data can support antibiotic-specific ML predictions without requiring genomic sequencing or detailed patient-level clinical information. This study provides a proof-of-concept framework for surveillance-driven predictive analytics that could complement conventional AMR surveillance and AMS. External validation, calibration, prospective clinical evaluation, and implementation studies are required before routine deployment. Antimicrobial resistance (AMR) occurs when bacteria no longer respond to medicines used to treat infections, making illnesses harder to treat and increasing the risk of severe outcomes. This is a growing global health problem, particularly in Low- and Middle-Income Countries (LMICs) where access to rapid diagnostic testing and Antimicrobial Stewardship (AMS) programs may be limited. In many healthcare settings, laboratory testing to determine which antibiotics are effective can take 48–72 hours. During this time, patients are often treated with broad-spectrum antibiotics without knowing whether they will work. This can lead to unnecessary antibiotic use, higher treatment costs, and the development of further resistance. This study explored whether routinely collected AMR surveillance data can be used to predict antibiotic effectiveness. Using data from the Pfizer ATLAS surveillance program, we developed computer-based models that estimate the likelihood that a specific antibiotic will be effective against a bacterial infection. These predictions are based on factors such as the type of bacteria, geographic location, and sample source. We also developed a rule-based tool that interprets laboratory measurements into standard susceptibility categories and an interactive dashboard that allows users to explore resistance trends and model predictions in a user-friendly way. The results show that surveillance data can be used to generate meaningful predictions about antibiotic effectiveness, even without advanced laboratory or genomic data. These tools could support earlier and more informed treatment decisions, improve antibiotic use, and strengthen monitoring of resistance patterns. However, the accuracy of these predictions depends on the quality and completeness of available data, and further validation is needed before routine clinical use. Overall, this study demonstrates how existing surveillance data can be transformed into practical tools to support better use of antibiotics and help slow the spread of antimicrobial resistance, particularly in resource-limited settings.

The root microbiome includes fungal pathogens capable of colonizing multiple plant hosts, yet the underlying genetic determinants remain unknown. Here we report that Plectosphaerella cucumerina is a core member of the Arabidopsis thaliana root microbiota, which displays pathogenic potential across multiple hosts. Using a collection of 72 Plectosphaerella isolates and whole-genome sequencing, we observed subtle phenotypic and genotypic variation associated with fungal phylogeny but not host plant identity. Transcriptome profiling of a P. cucumerina isolate in roots of diverse plants revealed core and host-specific fungal responses, including induction of carbohydrate-active enzymes (CAZymes) involved in root cell wall deconstruction. A fungal gene encoding a candidate β-1,3-glucanase (GH64) was identified as a key genetic factor driving multihost infection. This gene is present across plant-colonizing fungi and functions as a disease determinant in both P. cucumerina and a Colletotrichum root pathogen. Our results indicate that host-induced CAZymes can couple fungal virulence with multihost compatibility.

Genetic diversity present in rice genotypes plays a crucial role in improving breeding strategies and developing high-quality cultivars. This study evaluated genetic diversity, agronomic performance, and disease resistance across 110 unique rice cultivars over two growing seasons (2021-2022) using an alpha-lattice design. Ten morpho-physiological and yield characteristics were assessed along with resistance to brown leaf spot (BLS) in natural field conditions. The combined analysis of variance indicated highly significant genotypic effects (P < 0.01) across all traits, with broad-sense heritability values varying from 8.83% for days to maturity to 45.56% for plant height. K-means clustering effectively categorized genotypes into five distinct groups. Cluster 4 demonstrated the highest yield at 10.64 t ha⁻¹, characterized by early flowering, whereas Cluster 5 attained significant productivity of 10.2 t ha⁻¹ due to its large grain size. The distribution of BLS resistance demonstrated considerable variation across clusters (χ² = 50.1, P < 0.001). Cluster 1 presented 60% highly resistant genotypes, while Cluster 2 displayed 60% resistant genotypes, with no entries classified as susceptible. Notable performers comprised NGSR.18 with a multi-trait score of 70.77%, GSR1 achieving a yield of 12.76 t ha⁻¹, and GSR34, GSR49, GSR50 exhibiting high resistance to BLS. The correlation analysis demonstrated genetic independence between yield and disease resistance, thereby supporting the feasibility of simultaneous improvement. The findings offer significant genetic resources for the development of high-yielding, disease-resistant rice cultivars.

To synthesise biological biomarker evidence in proliferative verrucous leukoplakia/proliferative leukoplakia and quantify malignant transformation using eligible longitudinal evidence. Primary human biomarker studies and longitudinal studies reporting extractable malignant-transformation events and denominators were systematically reviewed. Biomarker evidence was synthesised narratively. Malignant-transformation proportions were pooled using a random-effects model, with conservative handling of potentially overlapping cohorts. Forty-one publications contributed to the biomarker synthesis, spanning genetic, genomic, DNA-ploidy, epigenetic, transcriptomic, proteomic, immunohistochemical, immunologic, viral, microbiome, salivary, and circulating markers. No biomarker had sufficient independent validation for clinical diagnostic or prognostic use. Twenty independent or conservatively non-overlapping study estimates comprising 745 patients and 243 malignant-transformation events contributed to the meta-analysis. The pooled malignant-transformation proportion was 34.13% (95% confidence interval 26.62-42.53), with substantial heterogeneity and a 95% prediction interval of 13.85%-62.56%. Results remained stable in sensitivity and leave-one-out analyses. Diverse biological alterations have been reported, but none currently supports routine biomarker-based diagnosis or individual risk stratification. Malignant transformation is frequent, although substantial between-study variability limits interpretation of the pooled estimate as an individual patient risk.

During a study on representatives of Diplectanidae (Monopisthocotyla) infecting Chaetodon hoefleri, a marine teleost from Senegal, we collected individuals belonging to a species of Lamellodiscinae, which showed a unique arrangement of the haptoral lamellodiscs, so far unreported in the literature, with the first lamella of the ventral lamellodisc open anteriorly, unlike the dorsal lamellodisc, which is closed. Due to this arrangement, we describe the species Lamellodiscus louiseuzeti n. sp.. Apart from the morphology of the lamellodiscs, the new species differs from its congeners by having a two-part male copulatory organ; one part featuring a well-developed distal hook and one, morphologically very variable, exhibiting a distal tongue-shaped part. Phylogenetic analyses of a concatenated alignment of partial 18S rDNA, ITS1, and COI sequences (1,315 bp), and a separate 28S rDNA alignment (882 bp) of this new species and other species of Lamellodiscinae with available DNA sequence data resulted in phylogenetic trees with congruent topologies that indicated three main lineages. These clades correspond to the morphological groups of Lamellodiscus suggested in previous studies. Based on phylogenetic analyses, L. louiseuzeti n. sp. forms a well-supported group with other species of Lamellodiscus. However, its internal phylogenetic position within the genus remains unresolved. We opted to consider L. louiseuzeti n. sp. a member of Lamellodiscus. We also emended the generic diagnosis to incorporate the new species and propose a new morphological group: Heterolamellae. Lamellodiscus louiseuzeti n. sp. (Monopisthocotyla, Diplectanidae, Lamellodiscinae), un parasite de Chaetodon hoefleri (Teleostei, Chaetodontidae) présentant une morphologie distincte de l’organe de fixation. Lors d’une étude sur des représentants de la famille des Diplectanidae (Monopisthocotyla) parasitant Chaetodon hoefleri, un téléostéen marin du Sénégal, nous avons récolté des individus appartenant à une espèce de Lamellodiscinae. Ces spécimens présentaient une disposition unique des lamellodisques du hapteur, jamais décrite auparavant dans la littérature, caractérisée par une première lamelle du lamellodisque ventral ouverte antérieurement, contrairement à celle du lamellodisque dorsal qui est fermée. En raison de cette disposition, nous décrivons l’espèce Lamellodiscus louiseuzeti n. sp.. Outre la morphologie des lamellodisques, cette nouvelle espèce se distingue de ses congénères par un organe copulateur mâle composé de deux parties, l’une dotée d’un crochet distal bien développé et l’autre, morphologiquement très variable, présentant une partie distale en forme de languette. Les analyses phylogénétiques, fondées sur un alignement concaténé de séquences partielles d’ADNr 18S, d’ITS1 et de COI (1315 pb) ainsi que sur un alignement distinct d’ADNr 28S (882 pb) incluant cette nouvelle espèce et d’autres Lamellodiscinae dont les séquences d’ADN sont disponibles, ont produit des arbres phylogénétiques aux topologies congruentes, révélant trois lignées principales. Ces clades correspondent aux groupes morphologiques de Lamellodiscus suggérés dans des études antérieures. D’après ces analyses, L. louiseuzeti n. sp. forme un groupe bien soutenu avec d’autres espèces de Lamellodiscus, bien que sa position phylogénétique interne au sein du genre reste indéterminée. Nous avons choisi de classer L. louiseuzeti n. sp. dans le genre Lamellodiscus. Nous avons également amendé la diagnose générique pour y intégrer cette nouvelle espèce et proposons un nouveau groupe morphologique : Heterolamellae.

Heat stress is an increasing challenge for cattle welfare and biological efficiency under climate change. Locally adapted and less intensively selected breeds may represent models for investigating resilience mechanisms, as they may retain adaptive traits useful for the selection of heat-resilient animals. This study characterized the response of Marchigiana cattle, a local Italian beef breed adapted to semi-extensive environments, in comparison with two cosmopolitan breeds with different productive aptitudes, Limousine and Holstein. Precision Livestock Farming, hematological profiling, and blood transcriptomics were integrated to investigate population- and breed-associated responses to heat stress (HS) under the specific farming conditions examined. IoT collars were applied only to Marchigiana cattle to record activity patterns and animal-proximal environmental variables. Blood samples from the three cattle populations were collected during summer HS and thermoneutral conditions, with sampling days selected according to the temperature-humidity index (THI). Sensor-derived data in Marchigiana cattle identified distinct clusters with differences in movement intensity and animal-proximal THI dynamics during HS. Hematological analyses revealed population-specific responses: Holstein showed increased neutrophils and platelets, consistent with a stronger inflammatory profile, whereas Marchigiana exhibited higher red blood cell counts and hemoglobin under HS, suggesting better maintenance of systemic homeostasis. Transcriptomic analysis identified both shared and population-specific responses to HS. Although all populations activated core immune- and stress-related pathways, the magnitude and organization of the response differed markedly. Holstein displayed the broadest inflammatory, immunometabolic, and tissue-remodeling signature, Limousine showed an intermediate profile, whereas Marchigiana exhibited a more coordinated response characterized by immune modulation, redox protection, metabolic buffering, and less pronounced extracellular matrix remodeling. We performed expression quantitative trait loci (eQTL) mapping using gene expression data and whole-genome imputed or non-imputed genotypes. Using imputed or non-imputed genotypes, we identified 43 and 65 cis-eQTLs influencing the expression of 45 and 73 genes, respectively. Our findings indicate that Marchigiana cattle exhibited hematological and transcriptomic profiles consistent with a potentially more balanced adaptive response to HS under the investigated conditions. However, because each breed was sampled on a different farm and direct physiological or productive measures of resilience were not included, the results do not establish an exclusively genetic breed effect or definitively demonstrate superior resilience. Integrating sensor-derived phenotypes with blood-based biomarkers may nevertheless help identify biological signatures associated with inter-individual responses. In addition, integrating transcriptomics and genomics identified cis-eQTLs that provide candidate regulatory markers for future studies of heat adaptation.