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Globally, maize (Zea mays L.) is deemed an important cereal that serves as a staple food and feed for humans and animals, respectively. Across the East African Community, maize is the staple food responsible for providing over one-third of calories in diets. Ideally, stored maize functions as man-made grain ecosystems, with nutritive quality changes influenced predominantly by chemical, biological, and physical factors. Food spoilage and fungal contamination are convergent reasons that contribute to the exacerbation of mycotoxins prevalence, particularly when storage conditions have deteriorated. In Kenya, aflatoxins are known to be endemic with the 2004 acute aflatoxicosis outbreak being described as one of the most ravaging epidemics in the history of human mycotoxin poisoning. In Tanzania, the worst aflatoxin outbreak occurred in 2016 with case fatalities reaching 50%. Similar cases of aflatoxicoses have also been reported in Uganda, scenarios that depict the severity of mycotoxin contamination across this region. Rwanda, Burundi, and South Sudan seemingly have minimal occurrences and fatalities of aflatoxicoses and aflatoxin contamination. Low diet diversity tends to aggravate human exposure to aflatoxins since maize, as a dietetic staple, is highly aflatoxin-prone. In light of this, it becomes imperative to formulate and develop workable control frameworks that can be embraced in minimizing aflatoxin contamination throughout the food chain. This review evaluates the scope and magnitude of aflatoxin contamination in post-harvest maize and climate susceptibility within an East African Community context. The paper also treats the potential green control strategies against Aspergillus spoilage including biocontrol-prophylactic handling for better and durable maize production. [ABSTRACT FROM AUTHOR]

The wide natural variation of Cinnamomum verum morphology in Sri Lanka suggests the chemo-diversity, which must be revealed for efficient utilization in a vast scope of industries. This study was carried out to determine the variation of essential oil constituents of cinnamon bark from major growing areas of Sri Lanka, morphological markers for oil constituents and genetic relationships for the correlations among major chemical constituents. E-Cinnamaldehyde (CM), (E)-Cinnamyl acetate (CA), Eugenol (EU), Benzyl benzoate (BB), linalool (LN) and β-caryophyllene (CR) were the highest abundant six out of 188 constituents reported through GC-MS analysis of bark essential oil. The mean values and 95 % confidence intervals of CM, CA, EU, BB, LN and CR were 65.10 ± 2.4 (60.15, 70.05), 15.93 ± 2.14 (11.51, 20.34), 4.07 ± 0.42 (3.19, 4.95), 3.21 ± 0.52 (2.15, 4.28), 1.44 ± 0.16 (1.09,1.79) and 1.31 ± 0.10 (1.11,1.53) respectively. CM was the highest abundant in 29/30 accessions, while CA was the highest abundant only in 1/30. CM varied with growing district. CM was negatively correlated with CA, (r = 0.81) at the 0.01 % level of significance irrespective of the growing district. The correlation between EU and BB was significant (r = 0.49) at 0.01 % level of significance. Chemical constituents were linked with the morphological characters: The leaf width (LW) was correlated with EU, (r = 0.37) at 0.05 % level of significance and the bark thickness (BT) was in a correlation with CR, (r = 0.54) at 0.01 % level of significance. Gene network model analysis identifying two pathways for CM, CA and BB biosynthesis, and two pathways for EU biosynthesis explained the correlations between major constituents.

Swine dysentery, spirochetal colitis, and salmonellosis are production-limiting enteric diseases of global importance to the swine industry. Despite decades of efforts, mitigation of these diseases still relies on antibiotic therapy. A common knowledge gap among the 3 agents is the early B-cell response to infection in pigs. Thus, this study aimed to characterize the porcine B-cell response to Brachyspira hyodysenteriae, Brachyspira hampsonii (virulent and avirulent strains), Brachyspira pilosicoli, and Salmonella Typhimurium, the agents of the syndromes mentioned above. Immortalized porcine B-cell line derived from a crossbred pig with lymphoma were co-incubated for 8 h with each pathogen, as well as E. coli lipopolysaccharide (LPS) and a sham-inoculum (n = 3/treatment). B-cell viability following treatments was evaluated using trypan blue, and the expression levels of B-cell activation-related genes was profiled using reverse transcription quantitative PCR. Only S. Typhimurium and LPS led to increased B-cell mortality. B. pilosicoli downregulated B-lymphocyte antigen (CD19), spleen associated tyrosine Kinase (syk), tyrosine-protein kinase (lyn), and Tumour Necrosis Factor alpha (TNF-α), and elicited no change in immunoglobulin-associated beta (CD79b) and swine leukocyte antigen class II (SLA-DRA) expression levels, when compared to the sham-inoculated group. In contrast, all other treatments significantly upregulated CD79b and stimulated responses in other B-cell downstream genes. These findings suggest that B. pilosicoli does not elicit an immediate T-independent B-cell response, nor does it trigger antigen-presenting mechanisms. All other agents activated at least one trigger within the T-independent pathways, as well as peptide antigen presenting mechanisms. Future research is warranted to verify these findings in vivo. [ABSTRACT FROM AUTHOR]

The anthurium is a popular cut flower worldwide having red, pink, coral, white, green, or brown spathes. There is a constant demand for new flower colours in the commercial market. Flower colour in plants is mainly determined by anthocyanins. Understanding anthocyanin variation and other factors affecting anthurium spathe colour is important for genetic engineering approaches. Therefore, our objectives were to assess the factors affecting colour variation of selected commercially available cut flower anthuriums and to determine the associated regulatory networks and transcription factors (TFs). Nineteen commercial cut flower anthurium cultivars were selected for this purpose. The colour of the spathe surface, anthocyanin location, anthocyanidin type and vacuolar pH were recorded. Anthocyanin associated Gene Network Model generation and analysis were carried out. The CIELAB colourimeter procedure indicated the colour variation among the selected 19 cultivars in terms of colour type, colour intensity, chroma, and hue angle. The location of anthocyanin was limited to mesophyll and epidermal cells. Cyanidin was detected in tested anthurium cultivars as the main anthocyanidin. The pH gradient in pigment extracts indicated a variation with a range of 4.6 to 4.94. The gene pathways of anthocyanin biosynthesis and transport were associated with that of the vacuolar pH/H+ pump according to Gene Network Model. Three pathways were regulated by an R2R3-MYB transcription factor. Although, cyanidin was the only pigment in all the tested cultivars, different pH levels by R2R3-MYB regulated V-H+ synthase was suggested to be the cause of the high colour variation in addition to the anthocyanidin type and location. Our results indicate the application of R2R3-MYB transcription factor genes for desirable vacuolar pH maintenance in genetic engineering of the blue anthurium in the future. [ABSTRACT FROM AUTHOR]

Rapid advances in genomic technologies have led to a wealth of diverse data, from which novel discoveries can be gleaned through the application of robust statistical and computational methods. Here, we describe GeneFishing, a semisupervised computational approach to reconstruct context-specific portraits of biological processes by leveraging gene-gene coexpression information. GeneFishing incorporates multiple high-dimensional statistical ideas, including dimensionality reduction, clustering, subsampling, and results aggregation, to produce robust results. To illustrate the power of our method, we applied it using 21 genes involved in cholesterol metabolism as "bait" to "fish out" (or identify) genes not previously identified as being connected to cholesterol metabolism. Using simulation and real datasets, we found that the results obtained through GeneFishing were more interesting for our study than those provided by related gene prioritization methods. In particular, application of GeneFishing to the GTEx liver RNA sequencing (RNAseq) data not only reidentified many known cholesterol-related genes, but also pointed to glyoxalase I (GLO1) as a gene implicated in cholesterol metabolism. In a follow-up experiment, we found that GLO1 knockdown in human hepatoma cell lines increased levels of cellular cholesterol ester, validating a role for GLO1 in cholesterol metabolism. In addition, we performed pantissue analysis by applying GeneFishing on various tissues and identified many potential tissue-specific cholesterol metabolism-related genes. GeneFishing appears to be a powerful tool for identifying related components of complex biological systems and may be used across a wide range of applications.

Connecting neural mechanisms of behavior to their underlying molecular and genetic substrates has important scientific and clinical implications. However, despite rapid growth in our knowledge of the functions and computational properties of neural circuitry underlying behavior in a number of important domains, there has been much less progress in extending this understanding to their molecular and genetic substrates, even in an age marked by exploding availability of genomic data. Here we describe recent advances in analytical strategies that aim to overcome two important challenges associated with studying the complex relationship between genes and behavior: (i) reducing distal behavioral phenotypes to a set of molecular, physiological, and neural processes that render them closer to the actions of genetic forces, and (ii) striking a balance between the competing demands of discovery and interpretability when dealing with genomic data containing up to millions of markers. Our proposed approach involves linking, on one hand, models of neural computations and circuits hypothesized to underlie behavior, and on the other hand, the set of the genes carrying out biochemical processes related to the functioning of these neural systems. In particular, we focus on the specific example of value-based decision-making, and discuss how such a combination allows researchers to leverage existing biological knowledge at both neural and genetic levels to advance our understanding of the neurogenetic mechanisms underlying behavior.

Cancer is a highly complex disease characterized by genetic and phenotypic heterogeneity among individuals. In the era of precision medicine, understanding the genetic basis of these individual differences is crucial for developing new drugs and achieving personalized treatment. Despite the increasing abundance of cancer genomics data, predicting the relationship between cancer samples and drug sensitivity remains challenging. In this study, we developed an explainable graph neural network framework for predicting cancer drug sensitivity (XGraphCDS) based on comparative learning by integrating cancer gene expression information and drug chemical structure knowledge. Specifically, XGraphCDS consists of a unified heterogeneous network and multiple sub-networks, with molecular graphs representing drugs and gene enrichment scores representing cell lines. Experimental results showed that XGraphCDS consistently outperformed most state-of-the-art baselines (R 2  = 0.863, AUC = 0.858). We also constructed a separate in vivo prediction model by using transfer learning strategies with in vitro experimental data and achieved good predictive power (AUC = 0.808). Simultaneously, our framework is interpretable, providing insights into resistance mechanisms alongside accurate predictions. The excellent performance of XGraphCDS highlights its immense potential in aiding the development of selective anti-tumor drugs and personalized dosing strategies in the field of precision medicine., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Rapid advances in genomic technologies have led to a wealth of diverse data, from which novel discoveries can be gleaned through the application of robust statistical and computational methods. Here, we describe GeneFishing, a semisupervised computational approach to reconstruct context-specific portraits of biological processes by leveraging gene-gene coexpression information. GeneFishing incorporates multiple high-dimensional statistical ideas, including dimensionality reduction, clustering, subsampling, and results aggregation, to produce robust results. To illustrate the power of our method, we applied it using 21 genes involved in cholesterol metabolism as "bait" to "fish out" (or identify) genes not previously identified as being connected to cholesterol metabolism. Using simulation and real datasets, we found that the results obtained through GeneFishing were more interesting for our study than those provided by related gene prioritization methods. In particular, application of GeneFishing to the GTEx liver RNA sequencing (RNAseq) data not only reidentified many known cholesterol-related genes, but also pointed to glyoxalase I (GLO1) as a gene implicated in cholesterol metabolism. In a follow-up experiment, we found that GLO1 knockdown in human hepatoma cell lines increased levels of cellular cholesterol ester, validating a role for GLO1 in cholesterol metabolism. In addition, we performed pantissue analysis by applying GeneFishing on various tissues and identified many potential tissue-specific cholesterol metabolism-related genes. GeneFishing appears to be a powerful tool for identifying related components of complex biological systems and may be used across a wide range of applications. [ABSTRACT FROM AUTHOR]

During oogenesis, hundreds of maternal RNAs are selectively localized to the animal or vegetal pole, including determinants of somatic and germline fates. Although microarray analysis has identified localized determinants, it is not comprehensive and is limited to known transcripts. Here, we utilized high-throughput RNA-sequencing analysis to comprehensively interrogate animal and vegetal pole RNAs in the fully grown Xenopus laevis oocyte. We identified 411 (198 annotated) and 27 (15 annotated) enriched mRNAs at the vegetal and animal pole, respectively. Ninety were novel mRNAs over 4-fold enriched at the vegetal pole and six were over 10-fold enriched at the animal pole. Unlike mRNAs, microRNAs were not asymmetrically distributed. Whole-mount in situ hybridization confirmed that all 17 selected mRNAs were localized. Biological function and network analysis of vegetally enriched transcripts identified protein-modifying enzymes, receptors, ligands, RNA-binding proteins, transcription factors and co-factors with five defining hubs linking 47 genes in a network. Initial functional studies of maternal vegetally localized mRNAs show that sox7 plays a novel and important role in primordial germ cell (PGC) development and that ephrinB1 (efnb1) is required for proper PGC migration. We propose potential pathways operating at the vegetal pole that highlight where future investigations might be most fruitful. [ABSTRACT FROM AUTHOR]

Abstract: All-trans retinoic acid (ATRA) is the most important active metabolite of vitamin A controlling segmentation in the developing organism and the homeostasis of various tissues in the adult. ATRA as well as natural and synthetic derivatives, collectively known as retinoids, are also promising agents in the treatment and chemoprevention of different types of neoplasia including breast cancer. The major aim of the present article is to review the basic knowledge acquired on the anti-tumor activity of classic retinoids, like ATRA, in mammary tumors, focusing on the underlying cellular and molecular mechanisms and the determinants of retinoid sensitivity/resistance. In the first part, an analysis of the large number of pre-clinical studies available is provided, stressing the point that this has resulted in a limited number of clinical trials. This is followed by an overview of the knowledge acquired on the role played by the retinoid nuclear receptors in the anti-tumor responses triggered by retinoids. The body of the article emphasizes the potential of ATRA and derivatives in modulating and in being influenced by some of the most relevant cellular pathways involved in the growth and progression of breast cancer. We review the studies centering on the cross-talk between retinoids and some of the growth-factor pathways which control the homeostasis of the mammary tumor cell. In addition, we consider the cross-talk with relevant intra-cellular second messenger pathways. The information provided lays the foundation for the development of rational and retinoid-based therapeutic strategies to be used for the management of breast cancer. [Copyright &y& Elsevier]

The genetic architecture of skeletal biomechanical performance has tremendous potential to advance our knowledge of the biological mechanisms that drive variation in skeletal fragility and osteoporosis risk. Research using traditional approaches that focus on specific gene pathways is increasing our understanding of how and to what degree those pathways may affect population-level variation in fracture susceptibility, and shows that known pathways may affect bone fragility through unsuspected mechanisms. Non-traditional approaches that incorporate a new appreciation for the degree to which bone traits co-adapt to functional loading environments, using a wide variety of redundant compensatory mechanisms to meet both physiological and mechanical demands, represent a radical departure from the dominant reductionist paradigm and have the potential to rapidly advance our understanding of bone fragility and identification of new targets for therapeutic intervention. [ABSTRACT FROM AUTHOR]

Abstract: We present a new method for the detection of gene pathways associated with a multivariate quantitative trait, and use it to identify causal pathways associated with an imaging endophenotype characteristic of longitudinal structural change in the brains of patients with Alzheimer''s disease (AD). Our method, known as pathways sparse reduced-rank regression (PsRRR), uses group lasso penalised regression to jointly model the effects of genome-wide single nucleotide polymorphisms (SNPs), grouped into functional pathways using prior knowledge of gene–gene interactions. Pathways are ranked in order of importance using a resampling strategy that exploits finite sample variability. Our application study uses whole genome scans and MR images from 99 probable AD patients and 164 healthy elderly controls in the Alzheimer''s Disease Neuroimaging Initiative (ADNI) database. 66,182 SNPs are mapped to 185 gene pathways from the KEGG pathway database. Voxel-wise imaging signatures characteristic of AD are obtained by analysing 3D patterns of structural change at 6, 12 and 24months relative to baseline. High-ranking, AD endophenotype-associated pathways in our study include those describing insulin signalling, vascular smooth muscle contraction and focal adhesion. All of these have been previously implicated in AD biology. In a secondary analysis, we investigate SNPs and genes that may be driving pathway selection. High ranking genes include a number previously linked in gene expression studies to β-amyloid plaque formation in the AD brain (PIK3R3, PIK3CG, PRKCA and PRKCB), and to AD related changes in hippocampal gene expression (ADCY2, ACTN1, ACACA, and GNAI1). Other high ranking previously validated AD endophenotype-related genes include CR1, TOMM40 and APOE. [Copyright &y& Elsevier]

Abstract: A major goal in post-genomic biology is the description of physiological functions in terms of gene pathway behavior. In this work, we present the first investigation of expression alterations in ninety-three pathways representing physiological functions of the plant A. thaliana induced by a P. parasitica elicitor (NLPpp) using microarray data publicly available at the AtGenExpress database. Using a novel statistical analysis developed to detect pathway alterations, we identified the gene pathways, hereby called groups of functionally associated genes (defined according to the TAIR/Gene Ontology and other databases), that are significantly altered in response to the elicitor. Instead of looking at individual gene responses, our analysis allowed a detailed characterization of the time ordering of pathways alterations in response to the NLPpp, their physiological implications and specificity. We also observed the activation of genes associated with vesicle trafficking and ROS production implying the initiation of the senescence of the wounded plant tissue. [Copyright &y& Elsevier]

Sialoadhesin (Sn) is the prototypic member of the Siglecs, a family of receptors mainly involved in cell-cell interactions. For several Siglecs, but not for Sn, intracellular signaling functions have been described. Because antibody-mediated cross-linking of surface transmembrane proteins is a powerful technique to investigate cell-molecular events, Sn expressed on porcine alveolar macrophages (PAM) was cross-linked with the antibody 41D3, and the expression profiles were compared with mock-treated macrophages by microarray analysis. Gene ontology analysis of 479 differentially expressed transcripts identified gene categories related to membrane localization, signal transduction, receptor and communication activities. Analyses of the human KEGG pathway database identified MAP kinase signaling, regulation of actin cytoskeleton, adipocytokine signaling, and wnt signaling as significantly altered pathways, supporting a role for Sn as intracellular signaling molecule. Real-time PCR of a subset of modulated genes confirmed these results and highlighted the reliability of a short-term cross-linking treatment for transcriptomic analysis of receptor functions. [ABSTRACT FROM AUTHOR]

Whole transcriptome shotgun sequencing (RNA-Seq) is a useful tool for analyzing the transcriptome of a biological sample. With appropriate statistical and bioinformatic processing, this platform is capable of identifying significant differences in gene expression within the transcriptome and permits pathway and network analyses to determine how these genes interact biologically. In this study, we examined gene expression in two lung adenocarcinoma cell lines (H358 and A459) that were treated with transforming growth factor-β (TGF-β) as a model for induction of the epithelial-to-mesenchymal transition (EMT), commonly associated with disease progression. We performed this study in order to illustrate a workflow for identifying interesting genes and processes that are regulated early in EMT and to determine their gene pathway/network relationships and regulation. With this, we identified 137 upregulated and 32 downregulated genes common to both cell lines after TGF-β treatment that represent components of multiple canonical pathways and biological networks associated with the induction of EMT. These findings were also verified against reposited Affymetrix U133a expression profiles from multiple trials examining metastatic progression in patient cohorts (n = 731 total) to further establish the clinical relevance and translational significance of the model system. Together, these findings help validate the relevance of the TGF-β model for the study of EMT and provide new insights into early events in EMT.