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Search Results (1,185)

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18 pages, 12318 KiB  
Article
Genome-Wide Identification and Expression Profiles of Nuclear Factor Y A Transcription Factors in Blueberry Under Abiotic Stress
by Xiuyue Xu, Hong Su, Shuwei Sun, Jing Sun, Xiang Zhang and Jiajie Yu
Int. J. Mol. Sci. 2024, 25(23), 12832; https://doi.org/10.3390/ijms252312832 (registering DOI) - 28 Nov 2024
Abstract
Nuclear Factor Y A (NF-YA) transcription factors are widely involved in multiple plant biological processes, such as embryogenesis, abscisic acid signaling, and abiotic stress response. This study presents a comprehensive genome-wide identification and expression profiling of NF-YA transcription factors in blueberry (Vaccinium [...] Read more.
Nuclear Factor Y A (NF-YA) transcription factors are widely involved in multiple plant biological processes, such as embryogenesis, abscisic acid signaling, and abiotic stress response. This study presents a comprehensive genome-wide identification and expression profiling of NF-YA transcription factors in blueberry (Vaccinium corymbosum), an important economic crop with good adaptability, under abiotic stress conditions. Given the economic significance and health benefits of blueberries, understanding their responses to environmental stresses, such as salt, drought, and temperature extremes, is crucial. A total of 24 NF-YA transcription factors were identified through bioinformatics analyses, including sequence alignment, phylogenetic tree construction, and conserved motif analysis. The expression patterns of these NF-YA genes were evaluated in various tissues (roots, stems, and leaves) and under different stress treatments (abscisic acid, salt, and cold) using quantitative real-time PCR (qRT-PCR). The results indicated that most VcNF-YA genes exhibited higher expression levels in stems and leaves compared to roots. Most VcNF-YAs were responsive to the stress treatment. Furthermore, cis-acting element analysis revealed that the promoters of VcNF-YAs were enriched with elements responsive to abiotic stress, suggesting their pivotal role in stress adaptation. This research unveils the expressional responses of NF-YA transcription factors in blueberry upon abiotic stresses and lays the groundwork for future studies on improving crop adaptation. Full article
(This article belongs to the Special Issue Transcription Factors in Plant Gene Expression Regulation)
15 pages, 1452 KiB  
Article
Genetically Distinct Rice Lines for Specific Characters as Revealed by Gene-Associated Average Pairwise Dissimilarity
by Yong-Bi Fu
Crops 2024, 4(4), 636-650; https://doi.org/10.3390/crops4040044 - 28 Nov 2024
Viewed by 23
Abstract
Broadening the genetic base of an elite breeding gene pool is one important goal in a successful long-term plant breeding program. This goal is largely achieved through the search for and introgression of exotic germplasm with adaptive traits. However, little is known about [...] Read more.
Broadening the genetic base of an elite breeding gene pool is one important goal in a successful long-term plant breeding program. This goal is largely achieved through the search for and introgression of exotic germplasm with adaptive traits. However, little is known about the genetic backgrounds of acquired exotic germplasm, as germplasm selection is mainly based on trait information. Here, we expanded an average pairwise dissimilarity (APD) analysis to samples with SNP genotypes associated with genes for specific characters of breeding interest. Specifically, we explored a gene-associated APD analysis in a genomic characterization of 2643 rice lines based on their published FASTQ data. Published contigs for cloned genes conditioning heat tolerance, cold tolerance, fertility, and seed size were downloaded as gene reference sequences for SNP calling, along with those SNP calls based on the rice reference genome and published indels. Totally, eight SNP or indel data sets were formed for each of three sample groups (All2643, Indica1789, and Japonica854). APD estimation was made for each of the 24 data sets. For each sample group, four novel sets of the 25 most genetically distinct rice lines, each for an assayed character, were identified. Further analyses of APD estimates also revealed some interesting APD properties. Four contig-based SNP data sets for four specific characters displayed similar APD frequency distributions and positive high correlations of APD estimates. Contig-based APD estimates were negatively correlated with genome-based APD estimates and nearly uncorrelated with indel-based APD estimates. These findings are significant for plant germplasm characterization and germplasm utilization in plant breeding. Full article
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<p>Frequency distribution of APD estimates in eight SNP or indel data sets for three sample groups (All2643, Indica1789, and Japonica854). M = mean and R = range.</p>
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<p>Pairwise correlations of APD estimates among eight SNP or indel data sets for all 2643 samples.</p>
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13 pages, 2761 KiB  
Article
Characterization and Expression Patterns of Heat Shock Protein 70 Genes from Paracoccus marginatus in Response to Temperature and Insecticide Stress
by Yanting Chen, Jianwei Zhao, Mengzhu Shi, Fei Ruan, Jianwei Fu, Wanxue Liu and Jianyu Li
Agriculture 2024, 14(12), 2164; https://doi.org/10.3390/agriculture14122164 - 28 Nov 2024
Viewed by 151
Abstract
The objective of this study was to identify the Hsp70s in Paracoccus marginatus and explore their roles in P. marginatus’s resistance to temperature and insecticide stress. The full-length cDNA sequences of PmHsp70s were obtained by PCR cloning and sequencing. The physicochemical and [...] Read more.
The objective of this study was to identify the Hsp70s in Paracoccus marginatus and explore their roles in P. marginatus’s resistance to temperature and insecticide stress. The full-length cDNA sequences of PmHsp70s were obtained by PCR cloning and sequencing. The physicochemical and structural characteristics of PmHsp70s were analyzed, and a phylogenetic tree was constructed. The gene expressions of PmHsp70s were detected using qRT-PCR to explore the impacts of temperature and insecticide stress on P. marginatus. A total of 12 PmHsp70s were identified and cloned. The amino acids encoded by PmHsp70s were found to contain highly conserved regions characteristic of the Hsp70 family. The subcellular localization results showed that the majority of PmHsp70s were located in the cytoplasm. A total of 13 unique conserved motifs were identified for the PmHsp70s, of which 9 were shared motifs. The phylogenetic tree showed that the 12 PmHsp70s could be clustered into five branches, with the closest evolutionary relationship observed with the Phenacoccus solenopsis. The expression of the majority of PmHsp70s was up-regulated in P. marginatus when subjected to heat stress, with the higher expression fold change observed for PmHsp70-9, PmHsp70-11, and PmHsp70-12. The expression of specific PmHsp70s was notably suppressed under cold stress, whereas the expression of others was markedly enhanced. Upon exposure to chlorfenapyr and lambda-cyhalothrin, the expressions of PmHsp70-11 and PmHsp70-12 were significantly up-regulated with the highest expression fold change, respectively. The results revealed the significance of specific PmHsp70s in the resistance of P. marginatus to temperature and insecticide stress. This study improved our understanding of the mechanisms underlying P. marginatus’s adaptive responses to unfavorable environmental conditions. Full article
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<p>Conserved motifs of PmHsp70s in <span class="html-italic">P. marginatus</span>. The PmHsp70 family exhibited a variety of motifs, which are marked by the use of a color box.</p>
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<p>Phylogenetic analysis of Hsp70 from <span class="html-italic">P. marginatus</span> and other insect species based on neighbor-joining method. The classification of the PmHsp70s was based on the number of sub-branches into which they were grouped. The resulting classification was as follows: branch A (PmHsp70-2, PmHsp70-4, PmHsp70-5, PmHsp70-6, and PmHsp70-7), branch B (PmHsp70-9, PmHsp70-11, and PmHsp70-12), branch C (PmHsp70-3 and PmHsp70-10), branch D (PmHsp70-1), and branch E (PmHsp70-8). The different colors represent the different species.</p>
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<p>Expression patterns of <span class="html-italic">PmHsp70s</span> in <span class="html-italic">P. marginatus</span> under temperature stress. The expression of the target gene in <span class="html-italic">P. marginatus</span> at 26 °C was set as the control with a relative expression value = 1. Data are represented as mean ± standard error (SE). Asterisks (*) indicate a statistically significant difference between the control and the treatment (<span class="html-italic">p</span> ≤ 0.05), while double asterisks (**) indicate a highly statistically significant difference between the control and the treatment (<span class="html-italic">p</span> ≤ 0.01).</p>
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<p>Expression patterns of <span class="html-italic">PmHsp70s</span> in <span class="html-italic">P. marginatus</span> under insecticide stress. CK: control; CH: chlorfenapyr; CY: lambda-cyhalothrin. The expression of the target gene in <span class="html-italic">P. marginatus</span> without insecticide treatment was set as the control with a relative expression value = 1. Data are represented as mean ± SE. Asterisks (*) indicate a statistically significant difference between the control and the treatment (<span class="html-italic">p</span> ≤ 0.05), while double asterisks (**) indicate a statistically significant difference between the control and the treatment (<span class="html-italic">p</span> ≤ 0.01).</p>
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12 pages, 1577 KiB  
Review
SVALKA: A Long Noncoding Cis-Natural Antisense RNA That Plays a Role in the Regulation of the Cold Response of Arabidopsis thaliana
by Nicholas M. Kiger and Susan J. Schroeder
Non-Coding RNA 2024, 10(6), 59; https://doi.org/10.3390/ncrna10060059 - 28 Nov 2024
Viewed by 129
Abstract
RNA plays important roles in the regulation of gene expression in response to environmental stimuli. SVALKA, a long noncoding cis-natural antisense RNA, is a key component of regulating the response to cold temperature in Arabidopsis thaliana. There are three mechanisms through [...] Read more.
RNA plays important roles in the regulation of gene expression in response to environmental stimuli. SVALKA, a long noncoding cis-natural antisense RNA, is a key component of regulating the response to cold temperature in Arabidopsis thaliana. There are three mechanisms through which SVALKA fine tunes the transcriptional response to cold temperatures. SVALKA regulates the expression of the CBF1 (C-Repeat Dehydration Binding Factor 1) transcription factor through a collisional transcription mechanism and a dsRNA and DICER mediated mechanism. SVALKA also interacts with Polycomb Repressor Complex 2 to regulate the histone methylation of CBF3. Both CBF1 and CBF3 are key components of the COLD REGULATED (COR) regulon that direct the plant’s response to cold temperature over time, as well as plant drought adaptation, pathogen responses, and growth regulation. The different isoforms of SVALKA and its potential to form dynamic RNA conformations are important features in regulating a complex gene network in concert with several other noncoding RNA. This review will summarize the three mechanisms through which SVALKA participates in gene regulation, describe the ways that dynamic RNA structures support the function of regulatory noncoding RNA, and explore the potential for improving agricultural genetic engineering with a better understanding of the roles of noncoding RNA. Full article
(This article belongs to the Special Issue Non-Coding RNA and Their Regulatory Roles in Plant)
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<p>Illustration of the relative locations of the <span class="html-italic">CBF</span> cluster and <span class="html-italic">SVALKA</span> genes to each other. The two <span class="html-italic">SVALKA</span> isoforms, <span class="html-italic">SVK</span>-L and <span class="html-italic">SVK</span>-S, are shown. The transcription start sites (TSSs) for <span class="html-italic">CBF1</span> and <span class="html-italic">SVALKA</span> are given, and the numbers given are the nucleotides up/downstream of the TSS for <span class="html-italic">CBF1</span>, showing the location of the distal polyadenylation site (DPAS) and proximal polyadenylation site (PPAS) corresponding to <span class="html-italic">SVK</span>-L and <span class="html-italic">SVK</span>-S, respectively. The relative locations of the <span class="html-italic">svk</span>-1 and <span class="html-italic">uns-1</span> (uncoupling <span class="html-italic">SVALKA</span> 1) T-DNA inserts are indicated. Figure drawn approximately to scale. Created with BioRender.com.</p>
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<p>The three different known mechanisms of <span class="html-italic">SVALKA</span> regulation. (<b>A</b>) Mechanism by which <span class="html-italic">SVK</span>-L adopts a dsRNA (double-stranded RNA) conformation with <span class="html-italic">CBF1</span> mRNA and regulates <span class="html-italic">CBF1</span> expression at 22 degrees Celsius. (<b>B</b>) <span class="html-italic">SVK</span>-S RNAPII collision-based mechanism of regulating <span class="html-italic">CBF1</span> in response to cold stress (4–8 h after freezing exposure). Sense/antisense collision of <span class="html-italic">CBF1/SVALKA</span> RNAPII occurs, resulting in premature transcript termination. Note that although they are shown on the same strand here, <span class="html-italic">SVALKA</span> is antisense to <span class="html-italic">CBF1</span>. Prior to Polycomb Repressive Complex 2 (PRC2) recruiting, <span class="html-italic">CBF3</span> is transcribed regularly. <span class="html-italic">SVALKA</span> lies between <span class="html-italic">CBF1</span> and <span class="html-italic">CBF3</span>, but antisense to them. (<b>C</b>) <span class="html-italic">SVALKA</span>-PRC2 mechanism for methylation of CBF3 (24 h after freezing exposure). <span class="html-italic">SVALKA</span> RNA recruits PRC2 to <span class="html-italic">CBF3</span>, where it methylates the gene, thereby making the chromatin inaccessible for transcription. (<b>D</b>) Timeline of the regulators of the cold response in Arabidopsis at 4 °C. <b>i</b>: <span class="html-italic">SVK</span>-S reaches a stable peak 8–12 h after initial cold exposure. <b>ii</b>: <span class="html-italic">CBF1</span> expression peaks 4 h after initial cold exposure (according to some studies). <b>iii</b>: <span class="html-italic">CBF3</span> expression peaks 3 h after initial cold exposure, then decreases. <b>iv</b>: Expression of <span class="html-italic">ICE</span>, a <span class="html-italic">CBF1</span> activator, reaches a steady peak 1–3 h after initial cold exposure. <b>v</b>: Expression of <span class="html-italic">CBF2</span>, a <span class="html-italic">CBF1</span> repressor, peaks three hours after initial cold exposure, then decreases to almost undetectable levels after 6 h. Created with BioRender.com.</p>
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<p>Overview of the <span class="html-italic">ICE/CBF-SVALKA COR</span> signaling pathway. Acronyms are as follows: MAPK, mitogen-activated protein kinase; OST1, Open stomata 1; <span class="html-italic">ICE1/2</span> Inducer of <span class="html-italic">CBF</span> Expression; <span class="html-italic">CBF</span>, C-repeat Binding Factor; <span class="html-italic">COR</span>, Col regulated genes; CRT/DRE, C-repeat/Dehydration Responsive Element; GA, gibberellin; SA, salicylic acid; BR, brassinosteroids; DICER/AGO, Dicer enzyme ARGONAUTE enzyme; PRC2 Polycomb Repressor Complex 2. Figure is updated and adapted from reference [<a href="#B47-ncrna-10-00059" class="html-bibr">47</a>]. Created with BioRender.com.</p>
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<p>CBF is a master regulator of stress response. As shown by the blunt red arrows, <span class="html-italic">SVALKA</span> negatively regulates <span class="html-italic">CBF1</span> and <span class="html-italic">CBF3</span>, and CBF1 in turn negatively regulates biomass production. As shown by green arrows, CBF1 expression positively regulates genes in the cold response, drought response, biotic stress response, and circadian clock pathways. Created with BioRender.com.</p>
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47 pages, 2058 KiB  
Article
A Quantitative Risk Assessment Model for Listeria monocytogenes in Ready-to-Eat Smoked and Gravad Fish
by Ursula Gonzales-Barron, Régis Pouillot, Taran Skjerdal, Elena Carrasco, Paula Teixeira, Matthew J. Stasiewicz, Akio Hasegawa, Juliana De Oliveira Mota, Laurent Guillier, Vasco Cadavez and Moez Sanaa
Foods 2024, 13(23), 3831; https://doi.org/10.3390/foods13233831 - 27 Nov 2024
Viewed by 276
Abstract
This study introduces a quantitative risk assessment (QRA) model aimed at evaluating the risk of invasive listeriosis linked to the consumption of ready-to-eat (RTE) smoked and gravad fish. The QRA model, based on published data, simulates the production process from fish harvest through [...] Read more.
This study introduces a quantitative risk assessment (QRA) model aimed at evaluating the risk of invasive listeriosis linked to the consumption of ready-to-eat (RTE) smoked and gravad fish. The QRA model, based on published data, simulates the production process from fish harvest through to consumer intake, specifically focusing on smoked brine-injected, smoked dry-salted, and gravad fish. In a reference scenario, model predictions reveal substantial probabilities of lot and pack contamination at the end of processing (38.7% and 8.14% for smoked brined fish, 34.4% and 6.49% for smoked dry-salted fish, and 52.2% and 11.1% for gravad fish), although the concentrations of L. monocytogenes are very low, with virtually no packs exceeding 10 CFU/g at the point of sale. The risk of listeriosis for an elderly consumer per serving is also quantified. The lot-level mean risk of listeriosis per serving in the elderly population was 9.751 × 10−8 for smoked brined fish, 9.634 × 10−8 for smoked dry-salted fish, and 2.086 × 10−7 for gravad fish. Risk reduction strategies were then analyzed, indicating that the application of protective cultures and maintaining lower cold storage temperatures significantly mitigate listeriosis risk compared to reducing incoming fish lot contamination. The model also addresses the effectiveness of control measures during processing, such as minimizing cross-contamination. The comprehensive QRA model has been made available as a fully documented qraLm R package. This facilitates its adaptation for risk assessment of other RTE seafood, making it a valuable tool for public health officials to evaluate and manage food safety risks more effectively. Full article
(This article belongs to the Special Issue Quantitative Risk Assessment of Listeria monocytogenes in Foods)
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<p>Schematic of the four-module exposure assessment of <span class="html-italic">L. monocytogenes</span> in smoked fish (left) and gravad fish (right), with indications of the modelled processes: CC, cross-contamination; G, growth; cG, growth in competition with lactic acid bacteria; M, mixing; I, inactivation; P, partitioning.</p>
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<p>Lot-level mean risk (log<sub>10</sub>) associated with the consumption of a 32.5-g serving (slice) of RTE smoked brine-injected fish, as evaluated for the reference and selected scenarios. Vertical lines on density plots indicate the median and interquartile range limits.</p>
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<p>Lot-level mean risk (log<sub>10</sub>) associated with the consumption of a 32.5-g serving (slice) of RTE smoked dry-salted fish, as evaluated for the reference and selected scenarios. Vertical lines on density plots indicate the median and interquartile range limits.</p>
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<p>Lot-level mean risk (log<sub>10</sub>) associated with the consumption of a 32.5-g serving (slice) of RTE gravad fish, as evaluated for the reference and selected scenarios. Vertical lines on density plots indicate the median and interquartile range limits.</p>
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10 pages, 1125 KiB  
Article
Comparison of Gut Microbiota in Overwintering Bees: Apis cerana vs. Apis mellifera
by Heng Chen, Lijiao Gao, Jialin Liu, Conghui Ji, Xiaoqun Dang, Zeyang Zhou and Wenhua Luo
Microbiol. Res. 2024, 15(4), 2425-2434; https://doi.org/10.3390/microbiolres15040163 - 26 Nov 2024
Viewed by 344
Abstract
Bees play important roles in socio-economic development, biodiversity conservation, and ecosystem stability. However, during the cold season, resources become limited, leading to significant losses in bee colonies. Although many studies have described the characteristics of winter bees and demonstrated that notable changes occur [...] Read more.
Bees play important roles in socio-economic development, biodiversity conservation, and ecosystem stability. However, during the cold season, resources become limited, leading to significant losses in bee colonies. Although many studies have described the characteristics of winter bees and demonstrated that notable changes occur in their gut microflora, the underlying mechanisms remain yet to be fully elucidated. Therefore, this study was conducted to compare the gut microbiota dynamics of overwintering bees. Sample acquisition involved randomly selecting ten colonies each from three bee farms containing Apis cerana (AC) and Apis mellifera (AM), followed by dissection for further analysis. DNA was extracted, and 16S rDNA sequencing, along with various bioinformatics tools, was used to assess microbial diversity, functional differences, and species comparisons between AC and AM gut microbiota. AC exhibited lower β diversity in the gut microbiota than AM during winter. Moreover, Gilliamella and Apibacter were relatively more abundant in AC. Regarding microbial functions, key pathways included the phosphotransferase system, galactose metabolism, the pentose phosphate pathway, and carbohydrate transport and metabolism. These results suggest the presence of microbial diversity differences between AC and AM, with the differential microbial functions mainly enriched in metabolic pathways that facilitate adaptation to cold environmental stress. Full article
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<p>Gut microbial diversity in AM and AC. Alpha diversity was evaluated based on the Chao (<b>A</b>), Simpson (<b>B</b>), and Shannon (<b>C</b>) indices of the OTU levels. PCoA analysis of beta diversity was based on the Bray–Curtis results (<b>D</b>) and weighted UniFrac distance analysis of similarities (ANOSIM) grouping test (<b>E</b>).</p>
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<p>Gut microbiota composition profiles in AM and AC. (<b>A</b>) Summary of the relative abundances of bacterial genera detected in AM and AC. (<b>B</b>) Genus level bacteria that were significantly different between the AM and AC. Statistical analysis was performed by the Wilcoxon rank-sum test. (<b>C</b>) Cladogram generated from the LEfSe analysis indicating the phylogenetic distribution from phylum to genus of the microbiota of AM and AC. (<b>D</b>) Histogram of LDA scores to identify differentially abundant bacterial genera between AM and AC (LDA score ≥ 2.5).</p>
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<p>Functional difference analysis. KEGG functional difference analysis (<b>A</b>). Functional difference analysis of eggNOG (<b>B</b>). Statistical analysis was performed by the Wilcoxon rank-sum test. <span class="html-italic">p</span>-value &lt; 0.05.</p>
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18 pages, 3244 KiB  
Article
Characteristics of Meteorological Drought Evolution in the Yangtze River Basin
by Wenchuan Bai, Cicheng Zhang, Xiong Xiao, Ziying Zou, Zelin Liu, Peng Li, Jiayi Tang, Tong Li, Xiaolu Zhou and Changhui Peng
Water 2024, 16(23), 3391; https://doi.org/10.3390/w16233391 - 25 Nov 2024
Viewed by 395
Abstract
Amid global climate change, recurrent drought events pose significant challenges to regional water resource management and the sustainability of socio-economic growth. Thus, understanding drought characteristics and regional development patterns is essential for effective drought monitoring, prediction, and the creation of robust adaptation strategies. [...] Read more.
Amid global climate change, recurrent drought events pose significant challenges to regional water resource management and the sustainability of socio-economic growth. Thus, understanding drought characteristics and regional development patterns is essential for effective drought monitoring, prediction, and the creation of robust adaptation strategies. Most prior research has analyzed drought events independently in spatial and temporal dimensions, often overlooking their dynamic nature. In this study, we employ a three-dimensional methodology that accounts for spatiotemporal continuity to identify and extract meteorological drought events based on a 3-month standardized precipitation evapotranspiration index (SPEI3). Measured by the SPEI3 index, the incidence of drought increased in the middle part of the basin, especially in some parts of Sichuan and Yunnan province, and the frequency of drought events decreased in the upper reaches. We evaluate drought events within the Yangtze River basin from 1980 to 2016 by examining five variables: chronology, extent, severity, duration, and epicenter locations. The results show that a total of 97 persisting drought events lasting at least 3 months have been identified in Yangtze River basin. Most events have a duration between 4 and 7 months. The findings indicate that while the number of drought events in the Yangtze River basin has remained unchanged, the intensity, duration, and severity of these events have shown a slight increase from 1980 to 2016. The drought events gradually moved from the western and southeastern parts of the basin to the central region. The most severe drought event occurred between January 2011 and October 2011, with a duration of 10 months and an affected area of 0.94 million km2, impacting over fifty percent of the basin. Changes in wetness and dryness in the Yangtze River basin are closely related to El Niño/Southern Oscillation (ENSO) events, with a positive correlation between the intensity of cold events and the probability of extreme drought. This study enhances our understanding of the dynamics and evolution of drought events in the Yangtze River basin, providing crucial insights for better managing water resources and developing effective adaptation strategies. Full article
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<p>Schematic representation of the continuity of drought area in time.</p>
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<p>(<b>a</b>) Time variation. (<b>b</b>) Spatial variation in SPEI3 in the Yangtze River Basin, 1980–2016.</p>
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<p>Frequency and probability distribution of the following drought characteristic variables: (<b>a</b>) duration, (<b>b</b>) intensity, (<b>c</b>) severity, and (<b>d</b>) area. The horizontal axis represents these variables, with each interval width appropriately divided based on the data range and distribution characteristics. The vertical axis indicates the frequency of each variable interval.</p>
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<p>Statistics summary of the following identified three-dimensional SPEI3 drought events: (<b>a</b>) drought number, (<b>b</b>) drought severity, (<b>c</b>) drought intensity, and (<b>d</b>) drought area. The linear trend line is represented by the blue line.</p>
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<p>Spatial distribution of the centroid of drought events in the Yangtze River Basin are as follows: (<b>a</b>) 1980–1989, (<b>b</b>) 1990–1999, (<b>c</b>) 2000–2009, and (<b>d</b>) 2010–2016. Events lasting for at least 3 months are shown.</p>
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<p>Relationship between drought-affected area (A), severity (S), and intensity (I) with duration (D).</p>
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<p>Typical drought progression from January 2011 to November 2011, with the dynamic evolution process of the critical period as follows (<b>a</b>): from a three-dimensional perspective, and (<b>b</b>): from the latitude–longitude plane. The bottom layer of <a href="#water-16-03391-f007" class="html-fig">Figure 7</a>a shows the spatial distribution of cumulated SPEI3 during the drought event. The arrows indicate the migration trajectory of the drought centroid in <a href="#water-16-03391-f007" class="html-fig">Figure 7</a>b.</p>
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21 pages, 10488 KiB  
Article
Genome-Wide Identification and Exogenous Hormone and Stress Response Expression Analysis of the GARP Gene Family in Soybean (Glycine max)
by Lijun Cai, Zhenhua Guo, Junjie Ding, Zhijia Gai, Jingqi Liu, Qingying Meng, Xu Yang, Na Zhang and Qingsheng Wang
Agriculture 2024, 14(12), 2109; https://doi.org/10.3390/agriculture14122109 - 22 Nov 2024
Viewed by 375
Abstract
The GARP transcription factor family is integral to the regulation of plant growth, development, hormone signaling pathways, circadian rhythms, and responses to both biotic and abiotic stressors. Despite its recognized importance, a comprehensive characterization of the GARP gene family in Glycine max remains [...] Read more.
The GARP transcription factor family is integral to the regulation of plant growth, development, hormone signaling pathways, circadian rhythms, and responses to both biotic and abiotic stressors. Despite its recognized importance, a comprehensive characterization of the GARP gene family in Glycine max remains unexplored. In this study, we identified 126 GmGARP genes across the 16 chromosomes of G. max and elucidated their diverse physicochemical properties. Phylogenetic classification grouped the GmGARP genes into eight distinct subfamilies, based on conserved motifs and gene structures, suggesting functional and evolutionary conservation within these clusters. The discovery of 56 segmentally duplicated gene pairs highlights gene duplication as a key driver of family expansion. Promoter analysis revealed various cis-regulatory elements, while expression profiling demonstrated that GmGARP genes possess significant tissue specificity. Furthermore, qRT-PCR analysis indicated that GmGARP genes are highly responsive to exogenous hormones, such as ABA, MeJA, and GA, as well as to abiotic stresses, including cold, salt, and drought conditions. Notably, GmGARP120 and GmGARP98 contain specific cis-elements linked to hormone responses, with their interaction verified through yeast two-hybrid (Y2H) and bimolecular fluorescence complementation (BiFC) assays. Additionally, 11,195 potential target genes were predicted, underscoring the regulatory potential of the GmGARP transcription factors. These findings provide significant insights into the GmGARP gene family, laying a strong foundation for future studies on its role in G. max development and adaptive responses to environmental stressors. Full article
(This article belongs to the Special Issue Genetic Diversity Assessment and Phenotypic Characterization of Crops)
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<p>Neighbor-joining phylogenetic tree of <span class="html-italic">Glycine max</span> and <span class="html-italic">A. thaliana GARP</span> family members. The eight colors correspond to eight regional groupings, and members of genes in the same colored region are in the same grouping. The red solid circles in the evolutionary tree molecules represent high or low bootstrap values, and the solid circles from small to large indicate low to high bootstrap values.</p>
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<p>Phylogenetic tree, motif, and gene structure of <span class="html-italic">GmGARP</span> family members. (<b>A</b>): Phylogenetic tree of <span class="html-italic">GmGARP</span> family members NJ. The eight colored regions represent eight subgroups and the gene members in the same colored region are the same subgroup. (<b>B</b>): <span class="html-italic">GmGARP</span> family member protein motif type and position distribution. Ten colored squares represent ten motifs each, and each motif is randomly distributed in position on the protein sequence. (<b>C</b>): Exon and intron position distribution and number. The blue squares represent UTR, the yellow squares represent CDS (Exon), and the grey lines between the yellow squares represent introns. The lower scale in the picture represents the number of amino acids (<b>B</b>) and nucleotide length (<b>C</b>), respectively.</p>
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<p>Number and location of <span class="html-italic">GmGARP</span> family members on chromosomes. The left scale represents chromosome length. 1 Mb = 1,000,000 bp. Blue to red represents gene density from low to high, and white areas indicate regions of gene vacancy. Red linkages represent tandem duplicate gene pairs.</p>
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<p><span class="html-italic">GmGARP</span> family member replication analysis. (<b>A</b>): Distribution of <span class="html-italic">GmGARP</span> family member fragment replication gene pairs on chromosomes; red connecting lines indicate fragment replication gene pairs, and the numbers outside the circles represent chromosome lengths. 1 Mb = 1,000,000 bp. (<b>B</b>): Information on the chromosomes, start positions, and termination positions of the 57 fragment replication gene pairs. (<b>C</b>): <span class="html-italic">Glycine max</span> and <span class="html-italic">A. thaliana GARP</span> family member homologous gene pairs. (<b>D</b>): <span class="html-italic">Glycine max</span> and <span class="html-italic">Oryza sativa</span> GARP family member homologous gene pairs. (<b>E</b>): <span class="html-italic">Glycine max</span> and <span class="html-italic">Zea mays</span> GARP family member homologous gene pairs. The red connecting lines indicate homologous gene pairs. The honey colour rectangle represents the <span class="html-italic">Glycine max</span> chromosome, the green rectangle represents the <span class="html-italic">A. thaliana</span> chromosome, the orange rectangle represents the <span class="html-italic">Oryza sativa</span> chromosome, and the purple rectangle represents the <span class="html-italic">Zea mays</span> chromosome.</p>
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<p>Analysis of <span class="html-italic">cis</span>-acting elements of <span class="html-italic">GmGARP</span> family members. (<b>A</b>): The location distribution of <span class="html-italic">cis</span>-elements related to hormone regulation, stress response, and growth and development. (<b>B</b>): The number of <span class="html-italic">cis</span>-elements related to hormone regulation, stress response, and growth and development corresponding to each gene. Green to red indicates that the number of <span class="html-italic">cis</span>-elements increases from small to large.</p>
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<p>Analysis of expression patterns of <span class="html-italic">GmGARP</span> family members. Normalized by row, blue to red indicates low expression to high expression. Values greater than or equal to 0.8 indicate significant expression.</p>
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<p><span class="html-italic">GmGARP</span> family member protein interactions network. Dashed lines between genes represent potential interactions, and red font indicates core genes.</p>
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<p>Target gene analysis. (<b>A</b>): ARR11 binding site illustration. (<b>B</b>): GO enrichment statistics. The red bar represents Biological Process, the green bar represents Cellular Component, and the blue bar represents Molecular Function. (<b>C</b>): KEGG pathway enrichment statistics.</p>
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<p>The relative expression levels of the <span class="html-italic">GmGARP</span> family under hormone treatment; 0 h, 1 h, 2 h, 4 h, 8 h, 16 h, and 24 h represent the sampling time points after treatment. Different colored boxes indicate different expression levels.</p>
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<p>The relative expression levels of the <span class="html-italic">GmGARP</span> family under abiotic stress. 0 h, 1 h, 2 h, 4 h, 8 h, 16 h, and 24 h represent the sampling time points after treatment. Different colored boxes indicate different expression levels.</p>
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<p>Protein and protein interactions. (<b>a</b>) Yeast two-hybrid validation of molecular interactions between <span class="html-italic">GmGARP120</span> and <span class="html-italic">GmGARP98</span>. pGBKT7-Lam/pGADT7-T- and pGBKT7–53/pGADT7-T-co-transformed yeast cells were used as negative and positive control, respectively. (<b>b</b>) BiFC assay confirms nucleus interactions between <span class="html-italic">GmGARP120</span> and <span class="html-italic">GmGARP98</span>.</p>
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13 pages, 277 KiB  
Article
Effect of Grazing on the Welfare of Dairy Cows Raised Under Different Housing Conditions in Compost Barns
by Beatriz Danieli, Maksuel Gatto de Vitt, Ana Luiza Bachmann Schogor, Maria Luísa Appendino Nunes Zotti, Patrícia Ferreira Ponciano Ferraz and Aline Zampar
Animals 2024, 14(23), 3350; https://doi.org/10.3390/ani14233350 - 21 Nov 2024
Viewed by 358
Abstract
There is currently no established information for assessing the general welfare conditions and behavior of dairy cows housed in compost-bedded pack barns (CBPs) that allow access to pasture. Therefore, the objective of this study was to evaluate and classify the welfare and behavior [...] Read more.
There is currently no established information for assessing the general welfare conditions and behavior of dairy cows housed in compost-bedded pack barns (CBPs) that allow access to pasture. Therefore, the objective of this study was to evaluate and classify the welfare and behavior of dairy cows in three different housing conditions within CBPs in southern Brazil. During both the cold and hot seasons, nine farms were divided into three groups: CONV (conventional, large, full-time barns), ADAP (conventionally adapted, full-time barns), and PART (part-time barns). The European Welfare Quality® (WQ®) protocol takes into account the characteristics of the animals, animal housing, and farm management to set an overall score to assess animal welfare, which is why WQ® was used in this study. Daytime behavior was monitored over a period of four consecutive hours on two days. The 29 WQ® measures were grouped into 11 criteria, then into four principles, and finally into the general welfare category. The experimental design employed was a randomized block design in a 2 × 3 factorial scheme (two climatic seasons and three groups), with the means of the measures, principles, and criteria for each group, season, and interaction (group × season) compared using the Tukey test. The diurnal behavior of the cows was described by the average absolute frequency of each observed behavioral measure. There were no differences among the groups in any of the measures assessed by the WQ® protocol. However, there was a significant increase in both the incidence of diarrhea and the duration of lying down during the cold season. Only the principle of appropriate behavior varied among the groups, with the PART group demonstrating superior scores. Regardless of the season, the welfare of dairy cows maintained in CBPs was classified as “improved”. No abnormalities in behavior were observed among cows housed in the different groups or seasons. Cows in the PART group laid down less frequently during the hot season. Overall, the CBP system provided favorable welfare and behavioral conditions for cows in Brazil, and access to grazing further enhanced the welfare of animals housed in the PART group. Full article
(This article belongs to the Collection Monitoring of Cows: Management and Sustainability)
18 pages, 19536 KiB  
Review
Helicases at Work: The Importance of Nucleic Acids Unwinding Under Cold Stress
by Theetha L. Pavankumar, Navneet Rai, Pramod K. Pandey and Nishanth Vincent
DNA 2024, 4(4), 455-472; https://doi.org/10.3390/dna4040031 - 15 Nov 2024
Viewed by 381
Abstract
Separation of duplex strands of nucleic acids is a vital process in the nucleic acid metabolism and survival of all living organisms. Helicases are defined as enzymes that are intended to unwind the double-stranded nucleic acids. Helicases play a prominent role in the [...] Read more.
Separation of duplex strands of nucleic acids is a vital process in the nucleic acid metabolism and survival of all living organisms. Helicases are defined as enzymes that are intended to unwind the double-stranded nucleic acids. Helicases play a prominent role in the cold adaptation of plants and bacteria. Cold stress can increase double-strand DNA breaks, generate reactive oxygen species, cause DNA methylation, and stabilize the secondary structure of RNA molecules. In this review, we discuss how helicases play important roles in adaptive responses to cellular stress caused by low temperature conditions, particularly in bacteria and plants. We also provide a glimpse of the eminence of helicase function over nuclease when an enzyme has both helicase and nuclease functions. Full article
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<p>Classification of helicases and translocases based on their characteristic domains and motifs. (<b>A</b>). The domain architecture and motif arrangements of SF1 to SF6 superfamilies of helicases. A (motif I) and B (motif II) are the Walker motifs, and R (motif IV) is the arginine motif. Domains shown in green and red colors are the RecA-like domain-1 (or AAA+ -like domain) and RecA-like domain-2, respectively. (<b>B</b>). Schematic representation of the domain organization of SF1 and SF2 helicases and SF3 to SF6 (hexamer) helicases. (<b>C</b>). Subclassification of helicases depending on the directionality of translocation: 3′ → 5′ translocation (A-type) and 5′ → 3′ translocation (B-type) on either duplex or single-stranded nucleic acid substrate. (<b>D</b>). Subclassification of helicases based on the usage of single-stranded (α) or double-stranded (β) nucleic acid substrates for translocation.</p>
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<p>The subfamilies of superfamily helicases. The identified subfamilies of SF1 and SF2 superfamilies based on the presence or absence of distinct sequence features are shown. The three subfamilies of SF1, and the nine subfamilies and one group of SF2 proteins are indicated.</p>
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<p>Crystal structure of the <span class="html-italic">E. coli</span> RecBCD-DNA complex (PDB ID: 6sjb). The cartoon structure of RecBCD with RecB (in orange and yellow), RecC (in blue), and RecD (in green) subunits, and the duplex DNA (in cyan) is shown. RecB is a 3′ → 5′ translocase (SF1A helicase), RecD is a 5′ → 3′ translocase (SF1B helicase), and RecC is a defunct SF1 helicase. The RecB and RecC subunits bind to the 3′- and 5′-ended ssDNA of a partially opened duplex DNA and translocate in the same direction with opposite polarities.</p>
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<p>Various adaptive mechanisms under cold stress in bacteria. (<b>A</b>). Low-temperature-dependent stabilized RNA structures are either destabilized, unwound, rearranged, or processed by cold shock proteins (Csps)/DEAD-box helicases/exoribonucleases to restore the cellular functions at low temperatures. (<b>B</b>). A modeled cartoon structure of <span class="html-italic">P. syringae</span> exoribonuclease RNase R with the cold-shock domain (CSD1 and CSD2; blue in color), the nuclease domain (S1; red in color), and the RNB domain with a triple-helix region (cyan and yellow colors) is shown.</p>
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<p>Roles of DEAD box helicases in molecular processes during cold stress in plants. Diverse roles of plant RNA helicases (RH) in splicing, RNA processing, ribosome biogenesis, chloroplast development, mRNA transport, and decay are depicted.</p>
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15 pages, 1784 KiB  
Article
A Study on the Vehicle Routing Planning Method for Fresh Food Distribution
by Yuxuan Wang, Yajun Wang and Junyu Leng
Appl. Sci. 2024, 14(22), 10499; https://doi.org/10.3390/app142210499 - 14 Nov 2024
Viewed by 423
Abstract
Aimed at the high cost of cold chain distribution of fresh agricultural products within a specified time window, a joint optimization method based on a bi-level programming model for cold chain logistics is proposed for the location of front warehouses and distribution path [...] Read more.
Aimed at the high cost of cold chain distribution of fresh agricultural products within a specified time window, a joint optimization method based on a bi-level programming model for cold chain logistics is proposed for the location of front warehouses and distribution path planning. At the upper level of the bi-level programming model, k-means clustering analysis is used to obtain all accurate information about alternative locations for the front warehouse for site selection, thereby providing the corresponding foundation for the lower level algorithm. At the lower level of the model, a fusion algorithm of particle swarm optimization (PSO) and a genetic algorithm (GA) is used for solving. To accelerate the convergence speed of the population and lower the running time of the algorithm, the parameter values in the algorithm are determined adaptively. An adaptive hybrid algorithm combining the particle swarm optimization algorithm and the genetic algorithm (APSOGA) is used to reallocate the location information on backup points for the front-end warehouse, ultimately determining the facility location of the front-end warehouse and planning the end path from the front-end warehouse to the customer point, achieving joint optimization of the front-end warehouse’s location and path. A comparative analysis of algorithm optimization shows that using the APSOGA hybrid algorithm can reduce the total cost of the logistics network by 14.57% compared to a traditional single-algorithm PSO solution and reduce it by 5.21% compared to using a single GA. This proves the effectiveness of the APSOGA hybrid algorithm in solving location and path planning problems for cold chain logistics distribution companies. Full article
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<p>Supply chain mode of front warehouse.</p>
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<p>Cluster analysis flowchart.</p>
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<p>The overall algorithm steps.</p>
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<p>Cluster analysis results.</p>
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<p>Trunk delivery route.</p>
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<p>Iterative convergence graph of different algorithms.</p>
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16 pages, 5113 KiB  
Article
Analysis of Lipid Metabolism in Adipose Tissue and Liver of Chinese Soft-Shelled Turtle Pelodiscus sinensis During Hibernation
by Feng Jin, Yunfei You, Junliang Wan, Huaiyi Zhu, Kou Peng, Zhenying Hu, Qi Zeng, Beijuan Hu, Junhua Wang, Jingjing Duan and Yijiang Hong
Int. J. Mol. Sci. 2024, 25(22), 12124; https://doi.org/10.3390/ijms252212124 - 12 Nov 2024
Viewed by 373
Abstract
Hibernation serves as an energy-conserving strategy that enables animals to withstand harsh environments by reducing their metabolic rate significantly. However, the mechanisms underlying energy adaptation in hibernating ectotherms, such as Pelodiscus sinensis, remain contentious. This paper first reports the decrease in lipid [...] Read more.
Hibernation serves as an energy-conserving strategy that enables animals to withstand harsh environments by reducing their metabolic rate significantly. However, the mechanisms underlying energy adaptation in hibernating ectotherms, such as Pelodiscus sinensis, remain contentious. This paper first reports the decrease in lipid levels and the expression of metabolism-related genes in P. sinensis during hibernation. The results of physiological and biochemical analysis showed that adipocyte cell size was reduced and liver lipid droplet (LD) contents were decreased during hibernation in P. sinensis. Concurrently, serum levels of triglycerides (TGs), total cholesterol (TC), non-esterified fatty acids (NEFAs), high-density lipoprotein cholesterol (HDLC), and low-density lipoprotein cholesterol (LDLC) were diminished (n = 8, p < 0.01), while an increase in serum glucose (Glu) (n = 8, p < 0.01) was noted among hibernating P. sinensis. These observations suggest a shift in energy metabolism during hibernation. To gain insights into the molecular mechanisms, we performed integrated transcriptomic and lipidomic analyses of adipose tissue and livers from summer-active versus overwintering P. sinensis, which revealed downregulation of free fatty acids (FFAs), triglycerides (TGs), diglycerides (DGs), and ceramides (Cers) during hibernation. The results of GSEA analysis showed that metabolic pathways associated with lipid metabolism, including glycerolipid metabolism and regulation of lipolysis in adipocytes, were suppressed significantly. Notably, acute cold exposure induced significant downregulation of genes related to lipolysis such as PNPLA2, ABHD5, LPL, CPT1A, and PPARα. The results indicate that lipolysis is suppressed during hibernation in P. sinensis. Collectively, these findings deepen our understanding of survival mechanisms and elucidate the unique energy adaptation strategies employed by hibernating ectotherms. Future research should explore the implications of these findings for the conservation of ectotherms and the applications for artificially inducing hibernation. Full article
(This article belongs to the Section Biochemistry)
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<p>Fat masses in adipose tissue and livers, and lipid indexes in serum of <span class="html-italic">Pelodiscus sinensis</span> in four seasons. (<b>A</b>,<b>B</b>) Images of <span class="html-italic">P. sinensis</span> in non-hibernation (<b>A</b>) and hibernation (<b>B</b>) states under different environmental temperatures. (<b>C</b>–<b>E</b>) The 3D images from X-ray microcomputed tomography (micro-CT) scanning of <span class="html-italic">P. sinensis</span>. For more detail from the micro-CT 3D images, see <a href="#app1-ijms-25-12124" class="html-app">Supplementary Video S1</a>. (<b>F</b>–<b>I</b>) Representative hematoxylin and eosin (H&amp;E) staining images of adipose tissue from inguinal region. Scale bars, 10 μm. (<b>J</b>–<b>M</b>) Representative oil red O staining images of liver. Scale bars, 10 μm. (<b>N</b>) Quantification of the sizes of individual adipocytes (n = 30). (<b>O</b>) Percentage of lipid droplet area in the total sample area (n = 30). (<b>P</b>–<b>U</b>) The levels of biochemical indexes related to lipid metabolism in serum. TG: triglyceride; TC: total cholesterol; NEFA: non-esterified fatty acid; HDLC: high-density lipoprotein cholesterol; LDLC: low-density lipoprotein cholesterol; Glu: glucose (n = 8). **, <span class="html-italic">p</span> &lt; 0.01 (relative to winter).</p>
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<p>Lipidomic analysis of differentially accumulated lipids (DALs) during non-hibernation and hibernation. (<b>A</b>,<b>B</b>) Principal component analysis (PCA) score plots of adipose tissue (<b>A</b>) and liver (<b>B</b>) lipid profiles. (<b>C</b>,<b>D</b>) Volcano map of lipid detection results in adipose tissue (<b>C</b>) and livers (<b>D</b>). (<b>E</b>,<b>F</b>) The fold change in concentration of all quantified lipid species between non-hibernation and hibernation groups in adipose tissue (<b>E</b>) and livers (<b>F</b>). Each dot represents a lipid species, and the dot size indicates significance. The different lipid classes are color-coded. (<b>G</b>,<b>H</b>) KEGG functional pathway annotation of differentially accumulated lipids in adipose tissue (<b>G</b>) and livers (<b>H</b>).</p>
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<p>Distinct lipid profile of non-hibernating and hibernating <span class="html-italic">P. sinensis</span>. (<b>A</b>–<b>D</b>) Heatmap analysis of FFA species (<b>A</b>), DG species (<b>B</b>), TG species (<b>C</b>), and Cer species (<b>D</b>) in adipose tissue. (<b>E</b>–<b>G</b>) Heatmap analysis of FFA species (<b>E</b>), DG species (<b>F</b>), and Cer species (<b>G</b>) in livers. FFA: free fatty acid; DG: diacylglycerol; Cer: ceramide.</p>
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<p>RNA-seq analysis of DEGs during non-hibernation and hibernation. (<b>A</b>,<b>B</b>) Principal component analysis (PCA) score plot of adipose tissue (<b>A</b>) and liver (<b>B</b>) transcription profiles. (<b>C</b>,<b>D</b>) Volcano map of gene detection results in adipose tissue (<b>C</b>) and livers (<b>D</b>). (<b>E</b>,<b>F</b>) KEGG enrichment analysis of differentially expressed genes in adipose tissue (<b>E</b>) and livers (<b>F</b>).</p>
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<p>Lipid-metabolism-related pathways are suppressed during hibernation. (<b>A</b>) The Venn diagram shows the overlapping KEGG pathways of transcriptome and lipidome from adipose tissue. Glycerolipid metabolism and regulation of lipolysis in adipocyte pathways are the overlapping KEGG pathways related to lipid metabolism. (<b>B</b>,<b>C</b>) Gene set enrichment analysis (GSEA) of glycerolipid metabolism (<b>B</b>) and regulation of lipolysis in adipocytes (<b>C</b>) found in gene sets from adipose tissue. (<b>D</b>) The Venn diagram shows the overlapping KEGG pathways of the transcriptome and lipidome from livers. Glycerolipid metabolism and regulation of lipolysis in adipocyte pathways are the overlapping KEGG pathways related to lipid metabolism. (<b>E</b>,<b>F</b>) GSEA of glycerolipid metabolism (<b>E</b>) and regulation of lipolysis in adipocytes (<b>F</b>) found in gene sets from livers.</p>
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<p>Low-temperature exposure in <span class="html-italic">P. sinensis</span>. (<b>A</b>,<b>B</b>) The relative mRNA expression of lipolysis-related genes (<span class="html-italic">PNPLA2</span>, <span class="html-italic">ABHD5</span>, <span class="html-italic">LPL</span>, <span class="html-italic">CPT1A</span>, and <span class="html-italic">PPARα</span>) in adipose tissue (<b>A</b>) and livers (<b>B</b>) (n = 3) during non-hibernation and hibernation. (<b>C</b>) Schematic of crawling distance measurement of <span class="html-italic">P. sinensis</span> under cold exposure. (<b>D</b>,<b>E</b>) The relative mRNA expression of lipolysis-related genes (<span class="html-italic">PNPLA2</span>, <span class="html-italic">ABHD5</span>, <span class="html-italic">LPL</span>, <span class="html-italic">CPT1A</span>, and <span class="html-italic">PPARα</span>) in adipose tissue (<b>D</b>) and livers (<b>E</b>) from 32 °C group and 10 °C group.</p>
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21 pages, 4521 KiB  
Article
Effects of Temperature, Precipitation, and Sunshine on Cold-Tolerant Wheat Yield Under Warming Trends: A 20-Year Study in Hokkaido, Japan
by Zenta Nishio, Masatomo Kurushima, Takeshi Suzuki, Seiji Shimoda and Tomoyoshi Hirota
Plants 2024, 13(22), 3165; https://doi.org/10.3390/plants13223165 - 11 Nov 2024
Viewed by 492
Abstract
To clarify the adaptation strategies of cold-tolerant wheat against global warming, this study examined the effects of daily temperature, precipitation, and sunshine duration on wheat yield in Hokkaido, Japan, over 13 years (2011–2023). Yield components were also analyzed over 20 years (2004–2023). The [...] Read more.
To clarify the adaptation strategies of cold-tolerant wheat against global warming, this study examined the effects of daily temperature, precipitation, and sunshine duration on wheat yield in Hokkaido, Japan, over 13 years (2011–2023). Yield components were also analyzed over 20 years (2004–2023). The number of snow-cover days decreased by about 24 days over the 20-year period. As a result, the growth of overwintered wheat accelerated, with the heading and maturity of plants advancing by about 8 and 5 days, respectively, and the grain-filling period extending from about 44 to about 48 days. Multiple regression analysis was conducted using wheat yield as the objective variable and weather conditions as explanatory variables. Three weather conditions were selected: precipitation for 8 days from 27 March, sunshine hours for 8 days from 21 March, and sunshine hours for 12 days from 13 June, which yielded a coefficient of determination of 0.953. Despite the highest mean summer temperatures on record being registered in 2023, high yields were ensured by the number of sunshine hours, which were approximately 1.5 times the normally recorded hours. This highlights the importance of this parameter in mitigating the impact of high summer temperatures. Full article
(This article belongs to the Special Issue Wheat Breeding for Global Climate Change)
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<p>Trends in wheat and rice yields in Hokkaido from 1958 to 2023. The dashed lines represent the liner regression curve. ** <span class="html-italic">p</span> &lt; 0.01.</p>
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<p>Tokachi Plain and location of Tokachi agricultural experimental station (TAES) and automated meteorological data acquisition system (AMEDAS) in Obihiro and Memuro. The black dots indicate the respective locations.</p>
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<p>Trends in monthly mean temperature (<b>A</b>), precipitation (<b>B</b>), and sunshine hours (<b>C</b>) in the Tokachi Plain (Obihiro) from 2004 to 2023. The dashed lines represent the liner regression curve. ** <span class="html-italic">p</span> &lt; 0.01, n.s. not significant.</p>
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<p>Trends in monthly mean temperature (<b>A</b>), precipitation (<b>B</b>), and sunshine hours (<b>C</b>) in the Tokachi Plain (Obihiro) from 2004 to 2023. The dashed lines represent the liner regression curve. ** <span class="html-italic">p</span> &lt; 0.01, n.s. not significant.</p>
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<p>The beginning and end of the snow-cover period in Memuro and the transition of heading and maturity dates of the wheat cultivar Kitahonami from 2004 to 2023. The dashed lines represent the liner regression curve. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, n.s. not significant.</p>
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<p>Correlation coefficients between the moving mean temperature over 7 to 14 days and mean wheat yield in the Tokachi Plain (<b>A</b>), number of spikes (<b>B</b>), single-spike weight (<b>C</b>), and maturing date (<b>D</b>) from 2011 to 2023. Plots above the dashed line for positive values or below the dashed line for negative values indicate statistical significance at the 1% level. White arrows indicate weather conditions that are statistically significantly correlated, and black arrows indicate weather conditions that were selected as statistically significant explanatory variables.</p>
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<p>Correlation coefficients between the moving mean temperature over 7 to 14 days and mean wheat yield in the Tokachi Plain (<b>A</b>), number of spikes (<b>B</b>), single-spike weight (<b>C</b>), and maturing date (<b>D</b>) from 2011 to 2023. Plots above the dashed line for positive values or below the dashed line for negative values indicate statistical significance at the 1% level. White arrows indicate weather conditions that are statistically significantly correlated, and black arrows indicate weather conditions that were selected as statistically significant explanatory variables.</p>
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<p>Correlation coefficients between the moving mean precipitation over 7 to 14 days and mean wheat yield in the Tokachi Plain (<b>A</b>), number of spikes (<b>B</b>), single-spike weight (<b>C</b>), and maturing date (<b>D</b>) from 2011 to 2023. Plots above the dashed line for positive values or below the dashed line for negative values indicate statistical significance at the 1% level. White arrows indicate weather conditions that are statistically significantly correlated, and black arrows indicate weather conditions that were selected as statistically significant explanatory variables.</p>
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<p>Correlation coefficients between the moving mean precipitation over 7 to 14 days and mean wheat yield in the Tokachi Plain (<b>A</b>), number of spikes (<b>B</b>), single-spike weight (<b>C</b>), and maturing date (<b>D</b>) from 2011 to 2023. Plots above the dashed line for positive values or below the dashed line for negative values indicate statistical significance at the 1% level. White arrows indicate weather conditions that are statistically significantly correlated, and black arrows indicate weather conditions that were selected as statistically significant explanatory variables.</p>
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<p>Correlation coefficients between the moving mean precipitation over 7 to 14 days and mean wheat yield in the Tokachi Plain (<b>A</b>), number of spikes (<b>B</b>), single-spike weight (<b>C</b>), and maturing date (<b>D</b>) from 2011 to 2023. Plots above the dashed line for positive values or below the dashed line for negative values indicate statistical significance at the 1% level. White arrows indicate weather conditions that are statistically significantly correlated, and black arrows indicate weather conditions that were selected as statistically significant explanatory variables.</p>
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<p>Correlation coefficients between the moving mean sunshine hours over 7 to 14 days and mean wheat yield in the Tokachi Plain (<b>A</b>), number of spikes (<b>B</b>), single-spike weight (<b>C</b>), and maturing date (<b>D</b>) from 2011 to 2023. Plots above the dashed line for positive values or below the dashed line for negative values indicate statistical significance at the 1% level. White arrows indicate weather conditions that are statistically significantly correlated, and black arrows indicate weather conditions that were selected as statistically significant explanatory variables.</p>
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<p>Correlation coefficients between the moving mean sunshine hours over 7 to 14 days and mean wheat yield in the Tokachi Plain (<b>A</b>), number of spikes (<b>B</b>), single-spike weight (<b>C</b>), and maturing date (<b>D</b>) from 2011 to 2023. Plots above the dashed line for positive values or below the dashed line for negative values indicate statistical significance at the 1% level. White arrows indicate weather conditions that are statistically significantly correlated, and black arrows indicate weather conditions that were selected as statistically significant explanatory variables.</p>
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<p>Correlation coefficients between the moving mean sunshine hours over 7 to 14 days and mean wheat yield in the Tokachi Plain (<b>A</b>), number of spikes (<b>B</b>), single-spike weight (<b>C</b>), and maturing date (<b>D</b>) from 2011 to 2023. Plots above the dashed line for positive values or below the dashed line for negative values indicate statistical significance at the 1% level. White arrows indicate weather conditions that are statistically significantly correlated, and black arrows indicate weather conditions that were selected as statistically significant explanatory variables.</p>
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20 pages, 25566 KiB  
Article
Reassortants of the Highly Pathogenic Influenza Virus A/H5N1 Causing Mass Swan Mortality in Kazakhstan from 2023 to 2024
by Kulyaisan T. Sultankulova, Takhmina U. Argimbayeva, Nurdos A. Aubakir, Arailym Bopi, Zamira D. Omarova, Aibarys M. Melisbek, Kobey Karamendin, Aidyn Kydyrmanov, Olga V. Chervyakova, Aslan A. Kerimbayev, Yerbol D. Burashev, Yermukhanmet T. Kasymbekov and Mukhit B. Orynbayev
Animals 2024, 14(22), 3211; https://doi.org/10.3390/ani14223211 - 8 Nov 2024
Viewed by 556
Abstract
In the winter of 2023/2024, the mass death of swans was observed on Lake Karakol on the eastern coast of the Caspian Sea. From 21 December 2023 to 25 January 2024, 1132 swan corpses (Cygnus olor, Cygnus cygnus) were collected [...] Read more.
In the winter of 2023/2024, the mass death of swans was observed on Lake Karakol on the eastern coast of the Caspian Sea. From 21 December 2023 to 25 January 2024, 1132 swan corpses (Cygnus olor, Cygnus cygnus) were collected and disposed of on the coast by veterinary services and ecologists. Biological samples were collected from 18 birds for analysis at different dates of the epizootic. It was found that the influenza outbreak was associated with a high concentration of migrating birds at Lake Karakol as a result of a sharp cold snap in the northern regions. At different dates of the epizootic, three avian influenza A/H5N1 viruses of clade 2.3.4.4.b were isolated from dead birds and identified as highly pathogenic viruses (HPAIs) based on the amino acid sequence of the hemagglutinin multi-base proteolytic cleavage site (PLREKRRRKR/G). A phylogenetic analysis showed that the viruses isolated from the swans had reassortations in the PB2, PB1, and NP genes between highly pathogenic (HP) and low-pathogenic (LP) avian influenza viruses. Avian influenza viruses A/Cygnus cygnus/Karakol lake/01/2024(H5N1) and A/Mute swan/Karakol lake/02/2024(H5N1) isolated on 10 January 2024 received PB2, PB1, and NP from LPAIV, while A/Mute swan/Mangystau/9809/2023(H5N1) isolated on 26 December 2023 received PB1 and NP from LPAIV, indicating that the H5N1 viruses in this study are new reassortants. All viruses showed amino acid substitutions in the PB2, PB1, NP, and NS1 segments, which are critical for enhanced virulence or adaptation in mammals. An analysis of the genomes of the isolated viruses showed that bird deaths during different periods of the epizootic were caused by different reassortant viruses. Kazakhstan is located at the crossroads of several migratory routes of migratory birds, and the possible participation of wild birds in the introduction of various pathogens into the regions of Kazakhstan requires further study. Full article
(This article belongs to the Special Issue Interdisciplinary Perspectives on Wildlife Disease Ecology)
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Figure 1
<p>Wild bird death site in the winter of 2023/2024.</p>
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<p>Dynamics of swan mortality on Lake Karakol in the winter of 2023/2024 (according to data from the veterinary service of the Mangystau region).</p>
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<p>Remains of a swan’s corpse.</p>
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<p>Swan corpse with signs of diarrhea and without a right leg (with a gnawed leg).</p>
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<p>Stray dog on the shore of Lake Karakol.</p>
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<p>Whooper swan (<span class="html-italic">Cygnus cygnus</span>) (an adult).</p>
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<p>Whooper swan (<span class="html-italic">Cygnus olor</span>) (a cygnet).</p>
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<p>Sick bird (a cygnet).</p>
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<p>Heart. Haemorrhages in the myocard.</p>
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<p>Haemorrhages in the liver.</p>
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<p>Lung edema.</p>
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<p>Phylogenetic trees, including complete PB2 (<b>A</b>), PB1 (<b>B</b>), PA (<b>C</b>), HA (<b>D</b>), NP (<b>E</b>), NA (<b>F</b>), M (<b>G</b>), and NS (<b>H</b>) genes, of Kazakhstani HPAIV H5N1 strains isolated from swans on the coast of Lake Karakol, located on the eastern shore of the Kazakhstani part of the Caspian Sea from 2023 to 2024 and publicly available sequences (GenBank). The strains investigated in this study are marked with triangles, squares, and circles: <span class="html-fig-inline" id="animals-14-03211-i001"><img alt="Animals 14 03211 i001" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i001.png"/></span>—A/<span class="html-italic">Mute swan</span>/Mangystau/9809/2023(H5N1); <span class="html-fig-inline" id="animals-14-03211-i002"><img alt="Animals 14 03211 i002" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i002.png"/></span>—A/<span class="html-italic">Cygnus cygnus</span>/Karakol lake/01/2024(H5N1); <span class="html-fig-inline" id="animals-14-03211-i003"><img alt="Animals 14 03211 i003" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i003.png"/></span>—A/Mute swan/Karakol lake/02/2024(H5N1); <span class="html-fig-inline" id="animals-14-03211-i004"><img alt="Animals 14 03211 i004" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i004.png"/></span>—A/mute swan/Mangystau/1-S24R-2/2024(H5N1) (virus isolated at NVRC and KazNARU by Tabynov K et al. in 2024 [<a href="#B27-animals-14-03211" class="html-bibr">27</a>]).</p>
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<p>Phylogenetic trees, including complete PB2 (<b>A</b>), PB1 (<b>B</b>), PA (<b>C</b>), HA (<b>D</b>), NP (<b>E</b>), NA (<b>F</b>), M (<b>G</b>), and NS (<b>H</b>) genes, of Kazakhstani HPAIV H5N1 strains isolated from swans on the coast of Lake Karakol, located on the eastern shore of the Kazakhstani part of the Caspian Sea from 2023 to 2024 and publicly available sequences (GenBank). The strains investigated in this study are marked with triangles, squares, and circles: <span class="html-fig-inline" id="animals-14-03211-i001"><img alt="Animals 14 03211 i001" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i001.png"/></span>—A/<span class="html-italic">Mute swan</span>/Mangystau/9809/2023(H5N1); <span class="html-fig-inline" id="animals-14-03211-i002"><img alt="Animals 14 03211 i002" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i002.png"/></span>—A/<span class="html-italic">Cygnus cygnus</span>/Karakol lake/01/2024(H5N1); <span class="html-fig-inline" id="animals-14-03211-i003"><img alt="Animals 14 03211 i003" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i003.png"/></span>—A/Mute swan/Karakol lake/02/2024(H5N1); <span class="html-fig-inline" id="animals-14-03211-i004"><img alt="Animals 14 03211 i004" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i004.png"/></span>—A/mute swan/Mangystau/1-S24R-2/2024(H5N1) (virus isolated at NVRC and KazNARU by Tabynov K et al. in 2024 [<a href="#B27-animals-14-03211" class="html-bibr">27</a>]).</p>
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<p>Phylogenetic trees, including complete PB2 (<b>A</b>), PB1 (<b>B</b>), PA (<b>C</b>), HA (<b>D</b>), NP (<b>E</b>), NA (<b>F</b>), M (<b>G</b>), and NS (<b>H</b>) genes, of Kazakhstani HPAIV H5N1 strains isolated from swans on the coast of Lake Karakol, located on the eastern shore of the Kazakhstani part of the Caspian Sea from 2023 to 2024 and publicly available sequences (GenBank). The strains investigated in this study are marked with triangles, squares, and circles: <span class="html-fig-inline" id="animals-14-03211-i001"><img alt="Animals 14 03211 i001" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i001.png"/></span>—A/<span class="html-italic">Mute swan</span>/Mangystau/9809/2023(H5N1); <span class="html-fig-inline" id="animals-14-03211-i002"><img alt="Animals 14 03211 i002" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i002.png"/></span>—A/<span class="html-italic">Cygnus cygnus</span>/Karakol lake/01/2024(H5N1); <span class="html-fig-inline" id="animals-14-03211-i003"><img alt="Animals 14 03211 i003" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i003.png"/></span>—A/Mute swan/Karakol lake/02/2024(H5N1); <span class="html-fig-inline" id="animals-14-03211-i004"><img alt="Animals 14 03211 i004" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i004.png"/></span>—A/mute swan/Mangystau/1-S24R-2/2024(H5N1) (virus isolated at NVRC and KazNARU by Tabynov K et al. in 2024 [<a href="#B27-animals-14-03211" class="html-bibr">27</a>]).</p>
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<p>Phylogenetic trees, including complete PB2 (<b>A</b>), PB1 (<b>B</b>), PA (<b>C</b>), HA (<b>D</b>), NP (<b>E</b>), NA (<b>F</b>), M (<b>G</b>), and NS (<b>H</b>) genes, of Kazakhstani HPAIV H5N1 strains isolated from swans on the coast of Lake Karakol, located on the eastern shore of the Kazakhstani part of the Caspian Sea from 2023 to 2024 and publicly available sequences (GenBank). The strains investigated in this study are marked with triangles, squares, and circles: <span class="html-fig-inline" id="animals-14-03211-i001"><img alt="Animals 14 03211 i001" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i001.png"/></span>—A/<span class="html-italic">Mute swan</span>/Mangystau/9809/2023(H5N1); <span class="html-fig-inline" id="animals-14-03211-i002"><img alt="Animals 14 03211 i002" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i002.png"/></span>—A/<span class="html-italic">Cygnus cygnus</span>/Karakol lake/01/2024(H5N1); <span class="html-fig-inline" id="animals-14-03211-i003"><img alt="Animals 14 03211 i003" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i003.png"/></span>—A/Mute swan/Karakol lake/02/2024(H5N1); <span class="html-fig-inline" id="animals-14-03211-i004"><img alt="Animals 14 03211 i004" src="/animals/animals-14-03211/article_deploy/html/images/animals-14-03211-i004.png"/></span>—A/mute swan/Mangystau/1-S24R-2/2024(H5N1) (virus isolated at NVRC and KazNARU by Tabynov K et al. in 2024 [<a href="#B27-animals-14-03211" class="html-bibr">27</a>]).</p>
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<p>Hypothetical reassortment events of the A/<span class="html-italic">Cygnus cygnus</span>/Karakol lake/01/2024(H5N1) viruses. The eight genes are shown in <a href="#animals-14-03211-t001" class="html-table">Table 1</a> and are as follows: PB2, PB1, PA, HA, NP, NA, M, and NS. The colors of the bars indicate the different sources of the gene segments.</p>
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<p>Hypothetical reassortment events of the A/<span class="html-italic">mute swan</span>/Mangystau/1-S24R-2/2024(H5N1) viruses.</p>
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<p>Hypothetical reassortment events of the A/<span class="html-italic">Mute swan</span>/Mangystau/9809/2023(H5N1) viruses.</p>
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15 pages, 6523 KiB  
Article
Complete Mitogenomes of Xinjiang Hares and Their Selective Pressure Considerations
by Ruijie Wang, Mayinur Tursun and Wenjuan Shan
Int. J. Mol. Sci. 2024, 25(22), 11925; https://doi.org/10.3390/ijms252211925 - 6 Nov 2024
Viewed by 389
Abstract
Comparative analysis based on the mitogenomes of hares in Xinjiang, China, is limited. In this study, the complete mitochondrial genomes of seven hare samples including four hare species and their hybrids from different environments were sequenced, assembled, and annotated. Subsequently, we performed base [...] Read more.
Comparative analysis based on the mitogenomes of hares in Xinjiang, China, is limited. In this study, the complete mitochondrial genomes of seven hare samples including four hare species and their hybrids from different environments were sequenced, assembled, and annotated. Subsequently, we performed base content and bias analysis, tRNA analysis, phylogenetic analysis, and amino acid sequence analysis of the annotated genes to understand their characteristics and phylogenetic relationship. Their mitogenomes are circular molecules (from 16,691 to 17,598 bp) containing 13 protein-coding genes, two rRNA genes, 22 tRNA genes, and a control region, which are similar with other Lepus spp. worldwide. The relative synonymous codon usage analysis revealed that the adaptation of Lepus yarkandensis to its unique arid and hot environment might be associated with synthesizing amino acids like alanine, leucine, serine, arginine, and isoleucine and the terminator caused by the different usage of codons. Further, we utilized the MEME model and identified two positive selection genes (ND4, ND5) in Lepus tibetanus pamirensis and one (ND5) in L. yarkandensis that might be important to their adaptation to the plateau and dry and hot basin environments, respectively. Meanwhile, Lepus tolai lehmanni and Lepus timidus may have evolved different adaptive mechanisms for the same cold environment. This study explored the evolutionary dynamics of Xinjiang hares’ mitochondrial genomes, providing significant support for future research into their adaptation mechanisms in extreme environments. Full article
(This article belongs to the Special Issue Molecular Insights into Zoology)
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<p>Global distribution of the <span class="html-italic">Lepus</span> species samples analyzed in this study. The red and yellow areas represent China, and the yellow area represents Xinjiang, China (GS (2024) 0650). Blue represents hares cited in GenBank, and yellow represents the Xinjiang hares. 1. <span class="html-italic">L. tibetanus pamirensis</span>, 2. Yarkand–Desert hare, 3. <span class="html-italic">L. yarkandensis</span>, 4. <span class="html-italic">Lepus timidus</span>, 5. <span class="html-italic">L. tolai lehmanni</span>, 6. <span class="html-italic">L. tolai centrasiaticus</span>, 7. Yarkand–Tolai hare, 8. <span class="html-italic">L. alleni</span>, 9. <span class="html-italic">L. americanus</span>, 10. <span class="html-italic">L. townsendii</span>, 11. <span class="html-italic">L. granatensis</span>, 12. <span class="html-italic">L. europaeus</span>, 13. <span class="html-italic">L. oiostolus</span>, 14. <span class="html-italic">L. capensis</span>, 15. <span class="html-italic">L. coreanus</span>.</p>
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<p>Mitochondrial genomes of Xinjiang hare species. (<b>a</b>) Circular structure maps of the mitochondrial genomes of 7 Xinjiang hares (16,691–17,598 bp). (<b>b</b>) Whole length and the lengths of protein-coding, tRNA, and rRNA genes, and the <span class="html-italic">D-loop</span> in the mitochondrial genomes of Xinjiang hares. (<b>c</b>) Secondary structure prediction diagram of <span class="html-italic">tRNA-Ser</span>.</p>
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<p>Mitochondrial genome base composition. (<b>a</b>) <span class="html-italic">Lepus</span> species mitochondrial genome base content. (<b>b</b>) <span class="html-italic">Lepus</span> species mitochondrial genome base bias. (<b>c</b>) Xinjiang hares’ protein-coding genes’ AT skew. (<b>d</b>) Xinjiang hares’ protein-coding genes’ GC skew.</p>
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<p>The relative synonymous codon usage (RSCU) in <span class="html-italic">Lepus</span> species worldwide. The columns from left to right show <span class="html-italic">Lepus timidus</span>, <span class="html-italic">L. tolai lehmanni</span>, <span class="html-italic">L. tolai centrasiaticus</span>, <span class="html-italic">L. tibetanus pamirensis</span>, Yarkand–Tolai hare, Yarkand–Desert hare, <span class="html-italic">L. yarkandensis</span>, <span class="html-italic">L. coreanus</span>, <span class="html-italic">L. oiostolus</span>, <span class="html-italic">L. europaeus</span>, <span class="html-italic">L. capensis</span>, <span class="html-italic">L. townsendii</span>, <span class="html-italic">L. granatensis</span>, <span class="html-italic">L. americanus</span>, and <span class="html-italic">L. alleni</span>.</p>
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<p>Phylogenetic tree, generated by IQ-Tree, and mitochondrial genome structure. The seven Xinjiang hares are shaded yellow. Each of the rectangular blocks represents a specific PCG in the mitochondrial genome, while the gray elliptical areas indicate non-coding regions.</p>
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<p>Shared selection pressure results in the protein-coding genes in <span class="html-italic">Lepus</span> species. The Xinjiang hares are enclosed in the red box. The bottom part shows the positively selected sites (<span class="html-italic">p</span> &lt; 0.1). The gene location information of <span class="html-italic">L. alleni</span> was used as the reference sequence for the positively selected sites in the other species. The background color represents the amino acid properties. [Threonine (T); isoleucine (I); leucine (L); asparagine (N); valine (V); glutamine (Q); phenylalanine (F); tyrosine (Y); proline (P); alanine (A)].</p>
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