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Computational models of gene regulations help to understand regulatory mechanisms and are extensively used in a wide range of areas, e.g., biotechnology or medicine, with significant benefits. Unfortunately, there are only a few computational gene regulatory models of whole genomes allowing static and dynamic analysis due to the lack of sophisticated tools for their reconstruction. Here, we describe Augusta, an open-source Python package for Gene Regulatory Network (GRN) and Boolean Network (BN) inference from the high-throughput gene expression data. Augusta can reconstruct genome-wide models suitable for static and dynamic analyses. Augusta uses a unique approach where the first estimation of a GRN inferred from expression data is further refined by predicting transcription factor binding motifs in promoters of regulated genes and by incorporating verified interactions obtained from databases. Moreover, a refined GRN is transformed into a draft BN by searching in the curated model database and setting logical rules to incoming edges of target genes, which can be further manually edited as the model is provided in the SBML file format. The approach is applicable even if information about the organism under study is not available in the databases, which is typically the case for non-model organisms including most microbes. Augusta can be operated from the command line and, thus, is easy to use for automated prediction of models for various genomes. The Augusta package is freely available at github.com/JanaMus/Augusta. Documentation and tutorials are available at augusta.readthedocs.io.

Heading date is a critical physiological process in rice that is influenced by both genetic and environmental factors. The photoperiodic pathway is a primary regulatory mechanism for rice heading, with key florigen genes Hd3a (Heading date 3a) and RFT1 (RICE FLOWERING LOCUS T1) playing central roles. Upstream regulatory pathways, including Hd1 and Ehd1, also significantly impact this process. This review aims to provide a comprehensive examination of the localization, cloning, and functional roles of photoperiodic pathway-related genes in rice, and to explore the interactions among these genes as well as their pleiotropic effects on heading date. We systematically review recent advancements in the identification and functional analysis of genes involved in the photoperiodic pathway. We also discuss the molecular mechanisms underlying rice heading date variation and highlight the intricate interactions between key regulatory genes. Significant progress has been made in understanding the molecular mechanisms of heading date regulation through the cloning and functional analysis of photoperiod-regulating genes. However, the regulation of heading date remains complex, and many underlying mechanisms are not yet fully elucidated. This review consolidates current knowledge on the photoperiodic regulation of heading date in rice, emphasizing novel findings and gaps in the research. It highlights the need for further exploration of the interactions among flowering-related genes and their response to environmental signals. Despite advances, the full regulatory network of heading date remains unclear. Further research is needed to elucidate the intricate gene interactions, transcriptional and post-transcriptional regulatory mechanisms, and the role of epigenetic factors such as histone methylation in flowering time regulation. This review provides a detailed overview of the current understanding of photoperiodic pathway genes in rice, setting the stage for future research to address existing gaps and improve our knowledge of rice flowering regulation.

The presence of epistasis in complex traits can be significant and affect the selection of segregating populations undesirably. The purpose of this study was to determine the epistatic genetic components that influence root and shoot traits of common bean and identify their effect on the performance of segregating populations. The field experiment consisted of 49 treatments (backcross progenies, parents and segregating populations in the F2 and F3 generations). The cross P1-BAF53 (Andean) × P2-IPR 88 Uirapuru (Mesoamerican) as reference. Six traits were taken into consideration: root distribution (%), first pod length (cm), number of grains (plot), plant height (cm), reproductive cycle (days) and number of basal branches (plot). For root distribution, first pod length and number of grains, the additive and additive × additive components were significant. On the contrary, for plant height, reproductive cycle and number of basal branches, no interaction component was observed. These results can be explained by the magnitude of variance determined for these traits. The additive × additive epistasis had a negative influence on the mean performance of segregating progenies. Contrariwise, transgressive segregation effects on plant height were observed as a function of genetic dominance deviation. Thus, additive × additive epistasis can hamper the success of a breeding program, for being associated with low-variability traits. This fact may be related with the number of crosses and/or the choice of genetically more distant parents for the program, and possibly with the presence of linked genes. [ABSTRACT FROM AUTHOR]

Appropriate breeding procedure for potential crop improvement relies on components of genetic variation, their estimation and identification of unique allele combinations to develop novel genotypes with desirable quality traits. Therefore, for the success of any breeding programme it is of utmost importance to have knowledge of gene action. The present study was carried out during the rainy seasons of 2017, 2018, and 2019 in mid-hills of the North-western Himalayas, India with an objective to estimate non-allelic interactions governing economically important horticultural traits in eggplant. The generation mean analysis was carried out utilizing six generations (P1, P2, F1, F2, B1 and B2) of four crosses viz., Res-2 × Res-1, Res-2 × Arka Nidhi, Hisar Shyamal × Res-1, and Arka Nidhi × Hisar Shyamal involving four diverse parents of eggplant. The results revealed presence of a duplicate type of epistasis for yield and its contributing traits in majority of crosses. The study also revealed that selection for recombinants suitable for high rainfall with high yield along with the contributing traits for yield may be retrieved through pedigree breeding by delaying the selection to later generations. Furthermore, the nature and magnitude of genic effects varied with respect to cross combinations for different horticultural traits. Hence, an explicit breeding strategy has to be adopted for improving specific traits in a particular cross combination to develop superior cultivars for high rainfall areas. [ABSTRACT FROM AUTHOR]

ABSTRACT The fitness cost of an antibiotic resistance gene (ARG) can differ across host strains, creating refuges that allow the maintenance of an ARG in the absence of direct selection for its resistance phenotype. Despite the importance of such ARG-host interactions for predicting ARG dynamics, the basis of ARG fitness costs and their variability between hosts are not well understood. We determined the genetic basis of a host-dependent cost of a β-lactamase, blaTEM-116*, that conferred a significant cost in one Escherichia coli strain but was close to neutral in 11 other Escherichia spp. strains. Selection of a blaTEM-116*-encoding plasmid in the strain in which it initially had a high cost resulted in rapid and parallel compensation for that cost through mutations in a P1-like phage gene, relAP1. When the wild-type relAP1 gene was added to a strain in which it was not present and in which blaTEM-116* was neutral, it caused the ARG to become costly. Thus, relAP1 is both necessary and sufficient to explain blaTEM-116* costs in at least some host backgrounds. To our knowledge, these findings represent the first demonstrated case of the cost of an ARG being influenced by a genetic interaction with a phage gene. The interaction between a phage gene and a plasmid-borne ARG highlights the complexity of selective forces determining the maintenance and spread of ARGs and, by extension, encoding phage and plasmids in natural bacterial communities.IMPORTANCEAntibiotic resistance genes (ARGs) play a major role in the increasing problem of antibiotic resistance in clinically relevant bacteria. Selection of these genes occurs in the presence of antibiotics, but their eventual success also depends on the sometimes substantial costs they impose on host bacteria in antibiotic-free environments. We evolved an ARG that confers resistance to penicillin-type antibiotics in one host in which it did confer a cost and in one host in which it did not. We found that costs were rapidly and consistently reduced through parallel genetic changes in a gene encoded by a phage that was infecting the costly host. The unmutated version of this gene was sufficient to cause the ARG to confer a cost in a host in which it was originally neutral, demonstrating an antagonism between the two genetic elements and underlining the range and complexity of pressures determining ARG dynamics in natural populations.

Comprehensive maps of genetic interactions in mammalian cells are daunting to construct because of the large number of potential interactions, ~ 2 × 108 for protein coding genes. We previously used co-inheritance of distant genes from published radiation hybrid (RH) datasets to identify genetic interactions. However, it was necessary to combine six legacy datasets from four species to obtain adequate statistical power. Mapping resolution was also limited by the low density PCR genotyping. Here, we employ shallow sequencing of nascent human RH clones as an economical approach to constructing interaction maps. In this initial study, 15 clones were analyzed, enabling construction of a network with 225 genes and 2,359 interactions (FDR < 0.05). Despite its small size, the network showed significant overlap with the previous RH network and with a protein-protein interaction network. Consumables were ≲$50 per clone, showing that affordable, high quality genetic interaction maps are feasible in mammalian cells.

Feed efficiency (FE), an important economic trait in sheep production, is indirectly assessed by residual feed intake (RFI). However, RFI in sheep is varied, and the molecular processes that regulate RFI are unclear. It is thus vital to investigate the molecular mechanism of RFI to developing a feed-efficient sheep. The miRNAsequencing (RNA-Seq) was utilized to investigate miRNAs in liver tissue of 6 out of 137 sheep with extreme RFI phenotypic values. In these animals, as a typical metric of FE, RFI was used to distinguish differentially expressed miRNAs (DE_miRNAs) between animals with high (n = 3) and low (n = 3) phenotypic values. A total of 247 miRNAs were discovered in sheep, with four differentially expressed miRNAs (DE_miRNAs) detected. Among these DE_miRNAs, three were found to be upregulated and one was downregulated in animals with low residual feed intake (Low_RFI) compared to those with high residual feed intake (High_RFI). The target genes of DE_miRNAs were primarily associated with metabolic processes and biosynthetic process regulation. Furthermore, they were also considerably enriched in the FE related to glycolysis, protein synthesis and degradation, and amino acid biosynthesis pathways. Six genes were identified by co-expression analysis of DE_miRNAs target with DE_mRNAs. These results provide a theoretical basis for us to understand the sheep liver miRNAs in RFI molecular regulation. [ABSTRACT FROM AUTHOR]

In this manuscript, we introduced a French EOAD patient in Korea who carried the presenilin-1 (PSEN1) Glu318Gly mutations with four possible risk variants, including sortilin-related receptor 1 (SORL1) Glu270Lys, ATP-binding cassette subfamily A member 7 (ABCA7) Val1946Met, translocase of outer mitochondrial membrane 40 (TOMM40) Arg239Trp, and granulin (GRN) Ala505Gly. The patient started to present memory decline and behavioral dysfunction in his early 60s. His brain imaging presented amyloid deposits by positron emission tomography (PET-CT). The multimer detection system (MDS) screening test for plasma for amyloid oligomers was also positive, which supported the AD diagnosis. It was verified that PSEN1 Glu318Gly itself may not impact amyloid production. However, additional variants were found in other AD and non-AD risk genes, as follows: SORL1 Glu270Lys was suggested as a risk mutation for AD and could increase amyloid peptide production and impair endosome functions. ABCA7 Val1946Met was a novel variant that was predicted to be damaging. The GRN Ala505Gly was a variant with uncertain significance; however, it may reduce the granulin levels in the plasma of dementia patients. Pathway analysis revealed that PSEN1 Glu318Gly may work as a risk factor along with the SORL1 and ABCA7 variants since pathway analysis revealed that PSEN1 could directly interact with them through amyloid-related and lipid metabolism pathways. TOMM40 and PSEN1 could have common mechanisms through mitochondrial dysfunction. It may be possible that PSEN1 Glu318Gly and GRN Ala505Gly would impact disease by impairing immune-related pathways, including microglia and astrocyte development, or NFkB-related pathways. Taken together, the five risk factors may contribute to disease-related pathways, including amyloid and lipid metabolism, or impair immune mechanisms. [ABSTRACT FROM AUTHOR]

Flavonoids are secondary metabolites that are non-essential for plant growth or survival, and they also provide numerous health benefits to humans. They are antioxidants that shield plants from the ill effects of ultraviolet light, pests, and diseases. They are beneficial to health for several reasons, including lowering inflammation, boosting cardiovascular health, and lowering cancer risk. This study looked into the physicochemical features of these substances to determine the potential pharmacological pathways involved in their protective actions. Potential targets responsible for the protective effects of quercetin, naringenin, and rutin were identified with SwissADME. The associated biological processes and protein–protein networks were analyzed by using the GeneMANIA, Metascape, and STRING servers. All the flavonoids were predicted to be orally bioavailable, with more than 90% targets as enzymes, including kinases and lyases, and with common targets such as NOS2, CASP3, CASP9, CAT, BCL2, TNF, and HMOX1. TNF was shown to be a major target in over 250 interactions. To extract the "biological meanings" from the MCODE networks' constituent parts, a GO enrichment analysis was performed on each one. The most important transcription factors in gene regulation were RELA, NFKB1, PPARG, and SP1. Treatment with quercetin, naringenin, or rutin increased the expression and interaction of the microRNAs' hsa-miR-34a-5p, hsa-miR-30b-5p, hsa-let-7a-5p, and hsa-miR-26a-1-3p. The anticancer effects of hsa-miR-34a-5p have been experimentally confirmed. It also plays a critical role in controlling other cancer-related processes such as cell proliferation, apoptosis, EMT, and metastasis. This study's findings might lead to a deeper comprehension of the mechanisms responsible for flavonoids' protective effects and could present new avenues for exploration. [ABSTRACT FROM AUTHOR]

Key message: Extensive crosstalk exists among ABA and different phytohormones that modulate plant tolerance against different abiotic stress. Being sessile, plants are exposed to a wide range of abiotic stress (drought, heat, cold, salinity and metal toxicity) that exert unwarranted threat to plant life and drastically affect growth, development, metabolism, and yield of crops. To cope with such harsh conditions, plants have developed a wide range of protective phytohormones of which abscisic acid plays a pivotal role. It controls various physiological processes of plants such as leaf senescence, seed dormancy, stomatal closure, fruit ripening, and other stress-related functions. Under challenging situations, physiological responses of ABA manifested in the form of morphological, cytological, and anatomical alterations arise as a result of synergistic or antagonistic interaction with multiple phytohormones. This review provides new insight into ABA homeostasis and its perception and signaling crosstalk with other phytohormones at both molecular and physiological level under critical conditions including drought, salinity, heavy metal toxicity, and extreme temperature. The review also reveals the role of ABA in the regulation of various physiological processes via its positive or negative crosstalk with phytohormones, viz., gibberellin, melatonin, cytokinin, auxin, salicylic acid, jasmonic acid, ethylene, brassinosteroids, and strigolactone in response to alteration of environmental conditions. This review forms a basis for designing of plants that will have an enhanced tolerance capability against different abiotic stress. [ABSTRACT FROM AUTHOR]

The phenotypic effect of perturbing a genes activity depends on the activity level of other genes, reflecting the notion that phenotypes are emergent properties of a network of functionally interacting genes. In the context of cancer, contemporary investigations have primarily focused on just one type of functional relationship between two genes-synthetic lethality (SL). Here, we define the more general concept of survival-associated pairwise gene expression states (SPAGEs) as gene pairs whose joint expression levels are associated with survival. We describe a data-driven approach called SPAGE-finder that when applied to The Cancer Genome Atlas (TCGA) data identified 71,946 SPAGEs spanning 12 distinct types, only a minority of which are SLs. The detected SPAGEs explain cancer driver genes tissue specificity and differences in patients response to drugs and stratify breast cancer tumors into refined subtypes. These results expand the scope of cancer SPAGEs and lay a conceptual basis for future studies of SPAGEs and their translational applications.

Pyramiding multiple resistant genes has been proposed as the most effective way to control wheat rust diseases globally. Identifying the most effective pyramids is challenged by the large pool of rust resistance genes and limited information about their mechanisms of resistance and interactions. Here. using a high-density genetic map, a double haploid population, and multi -rust field testing. we aimed to systematically characterize the most effective gene pyramids for rust resistance from highthe durable multi-rust resistant CIMMYT cultivar Parula. We revealed that the Parula resistance gene pyramid contains L)·34/Yr] 8/Sr57 (L, 34), Lr46/Yr29/S,·58 (L,·46). Li-27/Yr30/S,-2 (Srl). and L,-68. The efficacy. magnitude of effect. and interactions varied for the three I-lIst diseases. A subpopulation mapping approach was applied to characterize the complex interactions of the resistance genes by controlling for the effect of Li·34. Using this approach. we found that Lr34 and L}·68 have a strong additive effect for leaf rust. whereas no additive effects were observed for any rusts between L,·34 and Lr46. Lr34 combined synergistically with Sr\1 from Thatcher for stem rust. whereas the additive effect of Li·34 and Sr2 was dependent on the type of rust and environment. Two novel leaf rust quantitative trait loci (QTLE) from Parula were identified in this study. a stable QTL QLr-7BS and QL,·-5AS. which showed Lr34 dependent expression. With these findings. we propose combining two to three highthe value genes from Canadian wheat (e. g ., Srj2 from Thatcher) with a foundational multi -adult plant resistance cassette for desirable and durable resistance to ai] three rusts in Canadian wheat. [ABSTRACT FROM AUTHOR]

Cellular fates are determined by genes interacting across large, complex biological networks. A critical question is how to identify causal relationships spanning distinct signaling pathways and underlying organismal phenotypes. Here, we address this question by constructing a Boolean model of a well-studied developmental network and analyzing information flows through the system. Depending on environmental signals Caenorhabditis elegans develop normally to sexual maturity or enter a reproductively delayed, developmentally quiescent 'dauer' state, progressing to maturity when the environment changes. The developmental network that starts with environmental signal and ends in the dauer/no dauer fate involves genes across 4 signaling pathways including cyclic GMP, Insulin/IGF-1, TGF-β and steroid hormone synthesis. We identified three stable motifs leading to normal development, each composed of genes interacting across the Insulin/IGF-1, TGF-β and steroid hormone synthesis pathways. Three genes known to influence dauer fate, daf-2, daf-7 and hsf-1, acted as driver nodes in the system. Using causal logic analysis, we identified a five gene cyclic subgraph integrating the information flow from environmental signal to dauer fate. Perturbation analysis showed that a multifactorial insulin profile determined the stable motifs the system entered and interacted with daf-12 as the switchpoint driving the dauer/no dauer fate. Our results show that complex organismal systems can be distilled into abstract representations that permit full characterization of the causal relationships driving developmental fates. Analyzing organismal systems from this perspective of logic and function has important implications for studies examining the evolution and conservation of signaling pathways. [ABSTRACT FROM AUTHOR]

Cellular fates are determined by genes interacting across large, complex biological networks. A critical question is how to identify causal relationships spanning distinct signaling pathways and underlying organismal phenotypes. Here, we address this question by constructing a Boolean model of a well-studied developmental network and analyzing information flows through the system. Depending on environmental signals Caenorhabditis elegans develop normally to sexual maturity or enter a reproductively delayed, developmentally quiescent ‘dauer’ state, progressing to maturity when the environment changes. The developmental network that starts with environmental signal and ends in the dauer/no dauer fate involves genes across 4 signaling pathways including cyclic GMP, Insulin/IGF-1, TGF-β and steroid hormone synthesis. We identified three stable motifs leading to normal development, each composed of genes interacting across the Insulin/IGF-1, TGF-β and steroid hormone synthesis pathways. Three genes known to influence dauer fate, daf-2, daf-7 and hsf-1, acted as driver nodes in the system. Using causal logic analysis, we identified a five gene cyclic subgraph integrating the information flow from environmental signal to dauer fate. Perturbation analysis showed that a multifactorial insulin profile determined the stable motifs the system entered and interacted with daf-12 as the switchpoint driving the dauer/no dauer fate. Our results show that complex organismal systems can be distilled into abstract representations that permit full characterization of the causal relationships driving developmental fates. Analyzing organismal systems from this perspective of logic and function has important implications for studies examining the evolution and conservation of signaling pathways.

Pancreatic cancer is a highly fatal disease and an increasing common cause of cancer mortality. Mounting evidence now indicates that molecular heterogeneity in pancreatic cancer significantly impacts its clinical features. However, the dynamic nature of gene expression pattern makes it difficult to rely solely on gene expression alterations to estimate disease status. By contrast, biological networks tend to be more stable over time under different situations. In this study, we used a gene interaction network from a new point of view to explore the subtypes of pancreatic cancer based on individual-specific edge perturbations calculated by relative gene expression value. Our study shows that pancreatic cancer patients from the TCGA database could be separated into four subtypes based on gene interaction perturbations at the individual level. The new network-based subtypes of pancreatic cancer exhibited substantial heterogeneity in many aspects, including prognosis, phenotypic traits, genetic mutations, the abundance of infiltrating immune cell, and predictive therapeutic efficacy (chemosensitivity and immunotherapy efficacy). The new network-based subtypes were closely related to previous reported molecular subtypes of pancreatic cancer. This work helps us to better understand the heterogeneity and mechanisms of pancreatic cancer from a network perspective. [ABSTRACT FROM AUTHOR]

Pathway analysis plays a critical role in bioinformatics, enabling researchers to identify biological pathways associated with various conditions by analyzing gene expression data. However, the rise of large, multi-center datasets has highlighted limitations in traditional methods like Over-Representation Analysis (ORA) and Functional Class Scoring (FCS), which struggle with low signal-to-noise ratios (SNR) and large sample sizes. To tackle these challenges, we use a deep learning-based classification method, Gene PointNet, and a novel $P$-value computation approach leveraging the confusion matrix to address pathway analysis tasks. We validated our method effectiveness through a comparative study using a simulated dataset and RNA-Seq data from The Cancer Genome Atlas breast cancer dataset. Our method was benchmarked against traditional techniques (ORA, FCS), shallow machine learning models (logistic regression, support vector machine), and deep learning approaches (DeepHisCom, PASNet). The results demonstrate that GPNet outperforms these methods in low-SNR, large-sample datasets, where it remains robust and reliable, significantly reducing both Type I error and improving power. This makes our method well suited for pathway analysis in large, multi-center studies. The code can be found at https://github.com/haolu123/GPNet&#95;pathway">https://github.com/haolu123/GPNet&#95;pathway., (© The Author(s) 2025. Published by Oxford University Press.)

HMA (heavy metal associated) is a member of the ATPases protein family involved in metal transport in plants. This study characterizes several HMA3 homologs and infers their molecular functions in different plant species. Arabidopsis AtHMA3 (AT4G30120) was used as a reference to retrieve 11 HMA3 homologs having 97–100% query cover, 535–542 residues, 56,983 to 58,642 (Da) molecular weight, and 5.74 to 8.16 pI value, 29.10 to 33.89 instability index, and 0.222 to 0.380 grand average of hydropathicity. Topological analyses showed 4 transmembrane domains in these HMA3 homologs positioned similarly in terms of cytoplasmic and non-cytoplasmic regions along with ∼22–28% α-helices, ∼22–28% extended strands, and ∼50% random coils. HMA3 protein of Arabidopsis lyrata subsp. lyrata and Eutrema salsugineum are located at chromosome 2, while others are positioned at chromosome 4. All these HMA3 homologs are localized in the plasma membrane sharing a few common biological and molecular functions. Besides, these HMA3 genes contain 8–9 exons in which promoter positions are varied among the homologs. The cis-acting elements of HMA3 genes were projected to be involved with stress response, anaerobic induction, and light-responsive regulation in plants. Three out of five motifs encode E1-E2_ATPase involved in proton-pumping in the plasma membrane. The Arabidopsis thaliana HMA3 protein clustered with Camelina sativa and Capsella rubella show a close phylogenetic relationship. Also, AtHMA3 exhibits a close association with AtHMA3 with MTPA2, ZAT, NRAMP3, IRT2, and NRAMP2 under the local network of AtHMA3 linked to metal transport. Further, AtHMA3 is most potentially expressed during senescence, germinating seed, seedlings, young rosette, bolting, and young flower. In addition, AtHMA3 showed a significant upregulation (>6.0 fold) under Fe-deficiency. These findings may provide essential background to perform wet-lab experiments to understand the role of HMA3 in metal homeostasis.

MiR-10b, miR-335, and miR-21 are classes of microRNAs (miRNAs) that are overexpressed in breast cancer. Thus, in our study we aimed to test the hypothesis that miRNAs may have direct interactions with proteins and the possibility to inhibit/activate the functional site of proteins and enzymes. For this purpose, we choose three miRNAs involved in breast cancer to study interactions between some proteins and genes, including BRCA1 and PTEN, by processing the docking and matching tools using the Hex8 and HADDOCK server. Mathematically, the hidden Markov models were created by using MATLAB script according to the algorithm in order to study and validate the interactions and bonds between proteins and miRNAs. The main results demonstrate the ability of miR-10b, miR-335, and miR-21 to create direct interactions with 3D protein structures. Furthermore, these results may lead to another pathway of research, i.e. the direct interaction between proteins and their sub-units, to highlight the data obtained previously and demonstrate that proteins may directly interact with ncRNA instead of mRNA. Moreover, our study suggests developing research on different pathways of association proteins-miRNAs as a part of epigenetic extra-nuclear regulation. Taken together, our study provides the first evidence of direct interactions between miRNAs and proteins.

Hybrid male sterility (HMS) is a form of postmating postzygotic isolation among closely related species that can act as an effective barrier to gene flow. The Dobzhansky-Muller model provides a framework to explain how gene interactions can cause HMS between species. Genomics highlights the preponderance of non-coding DNA targets that could be involved in gene interactions resulting in gene expression changes and the establishment of isolating barriers. However, we have limited knowledge of changes in gene expression associated with HMS, gene interacting partners linked to HMS, and whether substitutions in DNA regulatory regions (cis) causes misexpression (i.e., expression of genes beyond levels found in parental species) of HMS genes in sterile hybrids. A previous transcriptome survey in a pair of D. pseudoobscura species found male reproductive tract (MRT) proteases as the largest class of genes misregulated in sterile hybrids. Here we assay gene expression in backcross (BC) and introgression (IG) progeny, along with site of expression within the MRT, to identify misexpression of proteases that might directly contribute to HMS. We find limited evidence of an accumulation of cis-regulatory changes upstream of such candidate HMS genes. The expression of four genes was differentially modulated by alleles of the previously characterized HMS gene Ovd.

Comprehensive maps of genetic interactions in mammalian cells are daunting to construct because of the large number of potential interactions, ~ 2 × 108 for protein coding genes. We previously used co-inheritance of distant genes from published radiation hybrid (RH) datasets to identify genetic interactions. However, it was necessary to combine six legacy datasets from four species to obtain adequate statistical power. Mapping resolution was also limited by the low density PCR genotyping. Here, we employ shallow sequencing of nascent human RH clones as an economical approach to constructing interaction maps. In this initial study, 15 clones were analyzed, enabling construction of a network with 225 genes and 2,359 interactions (FDR < 0.05). Despite its small size, the network showed significant overlap with the previous RH network and with a protein-protein interaction network. Consumables were ≲$50 per clone, showing that affordable, high quality genetic interaction maps are feasible in mammalian cells.

Comprehensive maps of genetic interactions in mammalian cells are daunting to construct because of the large number of potential interactions, ~ 2 × 108 for protein coding genes. We previously used co-inheritance of distant genes from published radiation hybrid (RH) datasets to identify genetic interactions. However, it was necessary to combine six legacy datasets from four species to obtain adequate statistical power. Mapping resolution was also limited by the low density PCR genotyping. Here, we employ shallow sequencing of nascent human RH clones as an economical approach to constructing interaction maps. In this initial study, 15 clones were analyzed, enabling construction of a network with 225 genes and 2,359 interactions (FDR < 0.05). Despite its small size, the network showed significant overlap with the previous RH network and with a protein-protein interaction network. Consumables were ≲$50 per clone, showing that affordable, high quality genetic interaction maps are feasible in mammalian cells. [ABSTRACT FROM AUTHOR]

Biological information regarding markers and gene association may be used to attribute different weights for single nucleotide polymorphism (SNP) in genome‐wide selection. Therefore, we aimed to evaluate the predictive ability and the bias of genomic prediction using models that allow SNP weighting in the genomic relationship matrix (G) building, with and without incorporating biological information to obtain the weights. Firstly, we performed a genome‐wide association studies (GWAS) in data set containing single‐ (SL) or a multi‐line (ML) pig population for androstenone, skatole and indole levels. Secondly, 1%, 2%, 5%, 10%, 30% and 50% of the markers explaining the highest proportions of the genetic variance for each trait were selected to build gene networks through the association weight matrix (AWM) approach. The number of edges in the network was computed and used to derive weights for G (AWM‐WssGBLUP). The single‐step GBLUP (ssGBLUP) and weighted ssGBLUP (WssGBLUP) were used as standard scenarios. All scenarios presented predictive abilities different from zero; however, the great overlap in their confidences interval suggests no differences among scenarios. Most of scenarios of based on AWM provide overestimations for skatole in both SL and ML populations. On the other hand, the skatole and indole prediction were no biased in the ssGBLUP (S1) in both SL and ML populations. Most of scenarios based on AWM provide no biased predictions for indole in both SL and ML populations. In summary, using biological information through AWM matrix and gene networks to derive weights for genomic prediction resulted in no increase in predictive ability for boar taint compounds. In addition, this approach increased the number of analyses steps. Thus, we can conclude that ssGBLUP is most appropriate for the analysis of boar taint compounds in comparison with the weighted strategies used in the present work. [ABSTRACT FROM AUTHOR]

Objectives: Bipolar disorder (BPD) is a highly heritable psychiatric disorder whose genetic complexity and pathogenetic mechanisms are still being unraveled. The main goal of this work was to characterize BPD risk‐gene candidates (identified by Nurnberger et al., JAMA Psychiatry 71:657, 2014, and Stahl et al., Nat. Genet. 51:793, 2019) with respect to their evolutionary conservation, associated phenotypes, and extent of gene‐gene interactions. Methods: Database searches and BLAST were used to identify homologous counterparts of human BPD risk genes in C. elegans, zebrafish, and Drosophila. Phenotypes associated with the C. elegans genes were annotated and searched. With GeneMANIA, we characterized and quantified gene‐gene interactions among members of the BPD gene set in comparison to randomly chosen gene sets of the same size. Results: BPD risk genes are highly conserved across species and are enriched for essential genes and genes associated with lethality and altered life span. They are significantly more interactive with each other in comparison to random genes. We identified syntenic blocks of risk genes, which provided potential insights into molecular pathways and co‐morbidities associated with BPD including coronary disease, obesity, and decreased life expectancy. Conclusions: BPD risk genes appear to be special in terms of their degree of conservation, interconnectedness, and pleiotropic effects that extend beyond a role in brain function. Key hub genes or pleiotropic regulatory components may represent attractive targets for future drug discovery. [ABSTRACT FROM AUTHOR]

To date, only the five most posterior groups of Hox genes, Hox9-Hox13, have demonstrated loss-of-function roles in limb patterning. Individual paralog groups control proximodistal patterning of the limb skeletal elements. Hox9 genes also initiate the onset of Hand2 expression in the posterior forelimb compartment, and collectively, the posterior HoxA/D genes maintain posterior Sonic Hedgehog (Shh) expression. Here we show that an anterior Hox paralog group, Hox5, is required for forelimb anterior patterning. Deletion of all three Hox5 genes (Hoxa5, Hoxb5, and Hoxc5) leads to anterior forelimb defects resulting from derepression of Shh expression. The phenotype requires the loss of all three Hox5 genes, demonstrating the high level of redundancy in this Hox paralogous group. Further analyses reveal that Hox5 interacts with promyelocytic leukemia zinc finger biochemically and genetically to restrict Shh expression. These findings, along with previous reports showing that point mutations in the Shh limb enhancer lead to similar anterior limb defects, highlight the importance of Shh repression for proper patterning of the vertebrate limb.

The characteristics of inflorescences and spikelets of rice(Oryza sativa L.) help determine grain yield. The sterile lemmas are special organs of rice not found in other grasses. The genes regulating the development of sterile lemmas are also involved in regulating grain yield-associated traits. Understanding the molecular mechanisms of sterile lemma development is pivotal for elucidating the mechanisms of rice flower development. In this paper, we characterized 12 known genes that directly affect the development of sterile lemma. The expression patterns of the 12 genes were determined in sterile lemma and other rice flower organs. On the basis of the results and reasonable assumptions, we proposed a working model to better understand the molecular mechanisms of sterile lemma development in rice, and the research area worth further exploration were put forward. [ABSTRACT FROM AUTHOR]

About 30% of the world population is infected with Mycobacterium tuberculosis (MTB). It is well known that the gene expression in MTB is highly variable, thus screening of traditional single‐gene in MTB has been incapable to meet the desires of clinical diagnosis. In this report, the authors systemically analysed the transcription regulatory network (TRN) in MTB H37Rv. The complex interplay of these gene interactions has been revealed using exhaustive topological and global analysis of TRN using parameters including indegree, outdegree, degree, directed and undirected average path length (APL), and randomly performed. Results from indegree analysis reveal a set of important genes, including papA5 and Rv0177 which are associated with high indegree values. Gene ontology analysis suggested their importance in the virulence of MTB. In addition, APL and analysis of highly significant genes further identified some critical genes with different APL values. Among the list of genes identified, thecsoR gene has the shortest directed APL score and high outdegree value, thus suggesting their importance in maintaining network topology. This study provides a comprehensive analysis of TRN and offers a good basis of understanding for developing experimental study in search of new therapeutic targets against MTB H37Rv pathogen. [ABSTRACT FROM AUTHOR]

Six generations (P1, P2, F1, F2, BC1P1 and BC1P2) of cucumber were developed from crossing gynoecious parent (Gy-14) with three commercial varieties (Pusa Uday, Punjab Naveen and Pant Kheera-1) during Feb-May 2017 and September-December 2017 and were evaluated during Feb-May 2018 at Punjab Agricultural University, Ludhiana to find out the genetics of quality characters in cucumber. From the ABCD scaling test and three parameter model of joint scaling test, the absence of non-allelic interactions was detected for total soluble solids, ascorbic acid in Gy-14 × Pusa Uday and Gy-14 × Pant Kheera-1 and mineral matter, improved by selection of desirable segregants followed by pedigree method. Among epistatic interactions, in trait such as ascorbic acid in Gy-14 × Punjab Naveen, dominance × dominance interaction was higher in magnitude, therefore these traits can be improved by heterosis breeding. [ABSTRACT FROM AUTHOR]

The leader gene approach is a data mining method based on the systematic search for genes involved in a specific process and their ranking according to the number of interconnections with the other genes identified. The genes with the strongest interconnections are termed leader genes, since they may be supposed to play an important role in the process. The potential of malignant progression of OLP to oral squamous cell carcinoma (OSCC) is still not completely clear. In this study, the leader gene approach is applied to investigate the association between OLP and OSCC at a molecular level. Results were integrated with those obtained in an experimental analysis (see paper 1 of this series). Genes involved in OLP and OSCC were identified by systematic queries to dedicated databases. Interconnections among identified genes were calculated and given a confidence value using STRING database. Leader genes were identified by clustering genes according to their interconnections. This theoretical analysis shows that OLP and OSCC share two leader genes: TP53 and CDKN1A, involved in the PI3K signalling events mediated by AKT pathway. This finding and those obtained in the experimental analysis suggest the possible involvement of some key genes/proteins LCK, PIK3CA, BIRC5, TP53 and CDKN1A in the malignant progression from OLP to OSCC. Moreover, these findings support the role of some molecular pathways, namely IL2 signalling events mediated by PI3K, PI3K signalling events mediated by AKT, and, possibly, Aurora A signalling in the association between OLP and OSCC.

This review investigates the current state of nutrigenomics in the zebrafish animal models. The zebrafish animal model has been used extensively in the study of disease onset and progression and associated molecular changes. In this review, we provide a synopsis of nutrigenomics using the zebrafish animal model. Obesity and dyslipidemia studies describe the genomics of dietary-induced obesity in relation to high-fat/high-calorie diets. Inflammation and cardiovascular studies describe dietary effects on the expression of acute inflammatory markers and resulting chronic inflammatory issues including atherosclerosis. We also evaluated the genomic response to bioactive dietary compounds associated with metabolic disorders. Carbohydrate metabolism and β-cell function studies describe the impacts of high-carbohydrate dietary challenges on nutritional programming. We also report tumorigenesis in relation to dietary carcinogen exposure studies that can result in permanent genomic changes. Vitamin and mineral deficiency studies demonstrate transgenerational genomic impacts of micronutrients in the diet and temporal expression changes. Circadian rhythm studies describe the relation between metabolism and natural temporal cycles of gene expression that impacts health. Bone formation studies describe the role of dietary composition that influences bone reabsorption regulation. Finally, this review provides future directions in the use of the zebrafish model for nutrigenomic and nutrigenetic research. [ABSTRACT FROM AUTHOR]

Several methods were developed to mine gene-gene relationships from expression data. Examples include correlation and mutual information methods for coexpression analysis, clustering and undirected graphical models for functional assignments, and directed graphical models for pathway reconstruction. Using an encoding for gene expression data, followed by deep neural networks analysis, we present a framework that can successfully address all of these diverse tasks. We show that our method, convolutional neural network for coexpression (CNNC), improves upon prior methods in tasks ranging from predicting transcription factor targets to identifying disease-related genes to causality inference. CNNC's encoding provides insights about some of the decisions it makes and their biological basis. CNNC is flexible and can easily be extended to integrate additional types of genomics data, leading to further improvements in its performance. [ABSTRACT FROM AUTHOR]

MiR-10b, miR-335, and miR-21 are classes of microRNAs (miRNAs) that are overexpressed in breast cancer. Thus, in our study we aimed to test the hypothesis that miRNAs may have direct interactions with proteins and the possibility to inhibit/activate the functional site of proteins and enzymes. For this purpose, we choose three miRNAs involved in breast cancer to study interactions between some proteins and genes, including BRCA1 and PTEN, by processing the docking and matching tools using the Hex8 and HADDOCK server. Mathematically, the hidden Markov models were created by using MATLAB script according to the algorithm in order to study and validate the interactions and bonds between proteins and miRNAs. The main results demonstrate the ability of miR-10b, miR-335, and miR-21 to create direct interactions with 3D protein structures. Furthermore, these results may lead to another pathway of research, i.e. the direct interaction between proteins and their sub-units, to highlight the data obtained previously and demonstrate that proteins may directly interact with ncRNA instead of mRNA. Moreover, our study suggests developing research on different pathways of association proteins-miRNAs as a part of epigenetic extra-nuclear regulation. Taken together, our study provides the first evidence of direct interactions between miRNAs and proteins. [ABSTRACT FROM AUTHOR]

Soybean is an important crops in the world due to its high seed protein and oil contents, while the contents of seed protein and oil attributed to the quantitative traits which were controlled by multiple minor-genes. Though there were many related QTLs reported, most of them only focused on the single additive QTLs, and few of them payed attentions to the epistatic QTLs. In this paper, a soybean RIL population (ZL) was used to study the epistatic QTLs of seed protein and oil contents by analyzing ZL population using Illumina BARCSoySNP6K Beadchip and detecting the phenotypes under four different environments. The results showed that there were 48 pairs of epistatic QTLs associated with protein content and 55 pairs of epistatic QTLs related to oil content in soybean seeds. Furthermore, there were 19 pairs of co-located epistatic QTLs controlling the protein and oil contents simultaneously. Among these co-located epistatic QTLs, 12 pairs of them shared the same QTL mapping regions, 2 pairs of them shared one same marker and five pairs of them had the QTLs regions not above 5 cM genetic distance, which could be regarded as the same QTL. Meanwhile, the results also showed that these epistatic QTLs located on nineteen chromosomes of soybean except for the chromosome 11. Thus, this study identified many epistatic QTLs for soybean seed protein and oil contents, which not only could be used for soybean quality genetic improvements in future, but also offered some evidences for reveal the negative relationships between seed protein and oil contents in soybean. [ABSTRACT FROM AUTHOR]

Interactions between multiple genes are involved in the development of complex diseases. However, there are few analyses of gene interactions associated with papillary thyroid cancer (PTC). Weighted gene co‑expression network analysis (WGCNA) is a novel and powerful method that detects gene interactions according to their co‑expression similarities. In the present study, WGCNA was performed in order to identify functional genes associated with PTC using R package. First, differential gene expression analysis was conducted in order to identify the differentially expressed genes (DEGs) between PTC and normal samples. Subsequently, co‑expression networks of the DEGs were constructed for the two sample groups, respectively. The two networks were compared in order to identify a poorly preserved module. Concentrating on the significant module, validation analysis was performed to confirm the identified genes and combined functional enrichment analysis was conducted in order to identify more functional associations of these genes with PTC. As a result, 1062 DEGs were identified for network construction. A brown module containing 118 highly related genes was selected as it exhibited the lowest module preservation. After validation analysis, 61 genes in the module were confirmed to be associated with PTC. Following the enrichment analysis, two PTC‑related pathways were identified: Wnt signal pathway and transcriptional misregulation in cancer. LRP4, KLK7, PRICKLE1, ETV4 and ETV5 were predicted to be candidate genes regulating the pathogenesis of PTC. These results provide novel insights into the etiology of PTC and the identification of potential functional genes. [ABSTRACT FROM AUTHOR]

Following the argument of a short story appeared in The Narnia Chronicles saga by C. S. Lewis, we discuss certain processes explained by two evolutionary theories which are highly unknown by biologists, despite the fact they have been created by two of the most distinguished authors of the Evolutionary Biology of the last century: Sewall Wright and Ernst Mayr. In the first case, we show the effect of inbreeding, gene drift and some extent of gene flow in the constitution of populations of domesticated animals. In the second one, we show the famous founder effect as a cause of speciation in small natural populations of animals living in geographic isolation. Afterwards, we analyze the resemblance between the two theories, even though they were proposed independently from each other. Finally, we entertain the plausibility that these theories could be used combined to explain the origins of the different groups which form the modern human species. [ABSTRACT FROM AUTHOR]

Summary: We introduce a marginal version of the nested Dirichlet process to cluster distributions or histograms. We apply the model to cluster genes by patterns of gene–gene interaction. The proposed approach is based on the nested partition that is implied in the original construction of the nested Dirichlet process. It allows simulation exact inference, as opposed to a truncated Dirichlet process approximation. More importantly, the construction highlights the nature of the nested Dirichlet process as a nested partition of experimental units. We apply the proposed model to inference on clustering genes related to DNA mismatch repair (DMR) by the distribution of gene–gene interactions with other genes. Gene–gene interactions are recorded as coefficients in an auto‐logistic model for the co‐expression of two genes, adjusting for copy number variation, methylation and protein activation. These coefficients are extracted from an online database, called Zodiac, computed based on The Cancer Genome Atlas (TCGA) data. We compare results with a variation of k‐means clustering that is set up to cluster distributions, truncated NDP and a hierarchical clustering method. The proposed inference shows favorable performance, under simulated conditions and also in the real data sets. [ABSTRACT FROM AUTHOR]

The review essentially describes genetic and non-genetic variables contributing to the onset and progression of exudative Age-related Macular Degeneration (AMD) in Italian population. In particular, AMD susceptibility within Italian population is contributed to by genetic variants, accounting for 23% of disease and non-genetic variants, accounting for 10% of AMD. Our data highlighted prominent differences concerning genetic and non-genetic contributors to AMD in our cohort with respect to worldwide populations. Among genetic variables, SNPs of CFH, ARMS2, IL-8, TIMP3, SLC16A8, RAD51B, VEGFA and COL8A1 were significantly associated with the risk of AMD in the Italian cohort. Surprisingly, other susceptibility variants described in European, American and Asiatic populations, did not reach the significance threshold in our cohort. As expected, advanced age, smoking and dietary habits were associated with the disease. In addition, we also describe a number of gene-gene and gene-phenotype interactions. In fact, AMD-associated genes may be involved in the alteration of Bruch's membrane and induction of angiogenesis, contributing to exacerbate the damage caused by aging and environmental factors. Our review provides an overview of genetic and non-genetic factors characterizing AMD susceptibility in Italian population, outlining the differences with respect to the worldwide populations. Altogether, these data reflect historical, geographic, demographic and lifestyle peculiarities of Italian population. The role of epigenetics, pharmacogenetics, comorbities and genetic counseling in the management of AMD patients have been described, in the perspective of the application of a "population-specific precision medicine" approach addressed to prevent AMD onset and improve patients' quality of life. [ABSTRACT FROM AUTHOR]

Darwinian fitness is a central concept in evolutionary biology. In practice, however, it is hardly possible to measure fitness for all genotypes in a natural population. Here, we present quantitative tools to make inferences about epistatic gene interactions when the fitness landscape is only incompletely determined due to imprecise measurements or missing observations. We demonstrate that genetic interactions can often be inferred from fitness rank orders, where all genotypes are ordered according to fitness, and even from partial fitness orders. We provide a complete characterization of rank orders that imply higher order epistasis. Our theory applies to all common types of gene interactions and facilitates comprehensive investigations of diverse genetic interactions. We analyzed various genetic systems comprising HIV-1, the malaria-causing parasite Plasmodium vivax, the fungus Aspergillus niger, and the TEM-family of [Formula: see text]-lactamase associated with antibiotic resistance. For all systems, our approach revealed higher order interactions among mutations.

Selecting rootstocks for high nitrogen acquisition ability may allow decreased N fertilizer application without reducing tomato yields, minimizing environmental nitrate pollution. A commercial hybrid tomato variety was grafted on a genotyped population of 130 recombinant inbred lines (RILs) derived from Solanum pimpinellifolium, and compared with self- and non-grafted controls under contrasting nitrate availabilities (13.8 vs 1.0 mM) in the nutrient solution. Grafting itself altered xylem sap composition under N-sufficient conditions, particularly Na + (8.75-fold increase) concentration. N deprivation decreased shoot dry weight by 72.7% across the grafted RIL population, and one RIL rootstock allowed higher total leaf N content than the best of controls, suggesting more effective N uptake. Sixty-two significant QTLs were detected by multiple QTL mapping procedure for leaf N concentration (LNC), vegetative growth, and the xylem sap concentrations of Mn and four phytohormone groups (cytokinins, gibberellins, salicylic acid and jasmonic acid). Only three LNC QTLs could be common between nitrogen treatments. Clustering of rootstock QTLs controlling LNC, leaf dry weight and xylem sap salicylic acid concentration in chromosome 9 suggests a genetic relationship between this rootstock phytohormone and N uptake efficiency. Some functional candidate genes found within 2 Mbp intervals of LNC and hormone QTLs are discussed. [ABSTRACT FROM AUTHOR]

To identify differentially expressed genes (DEGs) in analysis of microarray data, a majority of existing filter methods rank gene individually. Such a paradigm could overlook the genes with trivial individual discriminant powers but significant powers of discrimination in their combinations. This paper proposed an impurity metric in which the number of split intervals for each feature is considered as a parameter to be optimized for gaining maximal discrimination. The proposed method was first evaluated by applying to a synthesized noisy rectangular grid dataset, in which the significant feature pair which forms a rectangular grid pattern was successfully recognized. Furthermore, applying to the identification of DEGs on colon microarray data, the proposed method demonstrated that it could become an alternative to Fisher's test for the prescreening of genes which led to better performance of the SVM-RFE method. [ABSTRACT FROM AUTHOR]

The domino theory of gene loss states that when some particular gene loses its function and cripples a cellular function, selection will relax in all functionally related genes, which may allow for the non-functionalization and loss of these genes. Here we study the role of epistasis in determining the pattern of gene losses in a set of genes participating in cell envelope biogenesis in the endosymbiotic bacteria Buchnera aphidicola. We provide statistical evidence indicating pairs of genes in B. aphidicola showing correlated gene loss tend to have orthologs in Escherichia coli known to have alleviating epistasis. In contrast, pairs of genes in B. aphidicola not showing correlated gene loss tend to have orthologs in E. coli known to have aggravating epistasis. These results suggest that during the process of genome reduction in B. aphidicola by gene loss, positive or alleviating epistasis facilitates correlated gene losses while negative or aggravating epistasis impairs correlated gene losses. We interpret this as evidence that the reduced proteome of B. aphidicola contains less pathway redundancy and more compensatory interactions, mimicking the situation of E. coli when grown under environmental constrains.

Abstract The effects of additive, dominance, additive by dominance, additive by additive and dominance by dominance genetic effects on age at first service, non-return rates and interval from calving to first service were estimated. Practical considerations of computing additive and dominance relationships using the genomic relationship matrix are discussed. The final strategy utilized several groups of 1000 animals (heifers or cows) in which all animals had a non-zero dominance relationship with at least one other animal in the group. Direct inversion of relationship matrices was possible within the 1000 animal subsets. Estimates of variances were obtained using Bayesian methodology via Gibbs sampling. Estimated non-additive genetic variances were generally as large as or larger than the additive genetic variance in most cases, except for non-return rates and interval from calving to first service for cows. Non-additive genetic effects appear to be of sizeable magnitude for fertility traits and should be included in models intended for estimating additive genetic merit. However, computing additive and dominance relationships for all possible pairs of individuals is very time consuming in populations of more than 200 000 animals.