(January 2019–December 2021)
This research received no external funding.
Conceptualization, D.M. and G.S.; visualization, G.A.; writing—original draft preparation, G.A., F.F., H.D., A.M., D.M. and G.S.; writing—review and editing G.A., F.F., H.D., A.M., D.M. and G.S.; supervision, G.S. All authors have read and agreed to the published version of the manuscript.
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In this era of climate change and food/water/natural resource crises, it is important that current advancements in technology are made taking into consideration the impact on humanity and the environment. This new volume, Food Technology: Applied Research and Production Techniques , in the Innovations in Agricultural and Biological Engineering book series, looks at recent developments and innovations in food technology and sustainable technologies. Advanced topics in the volume include food processing, preservation, nutritional analysis, quality control and maintenance as well as good manufacturing practices in the food industries. The chapters are highly focused reports to help direct the development of current food- and agriculture-based knowledge into promising technologies.
Food Technology: Applied Research and Production Techniques will be a very useful reference book for food technologists, practicing food engineers, researchers, professors, students of these fields and professionals working in food technology, food science, food processing, and nutrition.
Part | 49 pages, good manufacturing practices and research in food technology, chapter 1 | 26 pages, good manufacturing practices for food processing industries: principles and practical applications, chapter 2 | 21 pages, research planning and funding agencies: focus on food engineering, part | 72 pages, latest food technologies, chapter 3 | 10 pages, food industry: use of plastics of the twenty-first century, chapter 4 | 35 pages, thermal processing in food technology: latest trends, chapter 5 | 24 pages, non-destructive technique of soft x-ray for evaluation of internal quality of agricultural produce, part | 109 pages, role of antioxidants in foods, chapter 6 | 21 pages, in vitro antioxidant efficacy: selected medicinal plants of gujarat, chapter 7 | 16 pages, antioxidant activities of some marine algae: case study from india, chapter 8 | 50 pages, omega-3 pufa from fish oil: silver based solvent extraction, chapter 9 | 19 pages, anti-oxidant and anti- bacterial properties of extracts: terminalia chebula and terminalia bellerica, part | 38 pages, antimicrobial activities in food, chapter 10 | 17 pages, in vitro antimicrobial activity: salvadora species, chapter 11 | 18 pages, antimicrobial properties of leaf extract: polyalthia longifolia var. pendula under in-vitro conditions, part | 77 pages, active constituents of foods, chapter 12 | 39 pages, isolation, validation and characterization of major bioactive constituents from mango ripe seed, chapter 13 | 17 pages, isolation and characterization of lycopene from tomato and its biological activity, chapter 14 | 17 pages, food processing using microbial control system: shea butter.
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June 20, 2024
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by Enayat Moallemi, CSIRO
We're facing rising food insecurity, the cost-of-living squeeze, and ever-changing climate events. It's no wonder our food systems are in urgent need of a reboot.
Research, published in in One Earth , highlights what can be learned from other transitions. This includes how we can effect change, and establish new partnerships to support food system transformations.
Food systems don't just put meals on the table. They also contribute significantly to the economy and the livelihoods of communities. However, climate disruptions and other factors present a challenge. They contribute to increased food costs and the cost-of-living crisis in general. The redirection of food systems towards more sustainable, equitable and nutritious future models is often referred to as 'transformation."
The need to transform Australia's food system was a focal point of a recent parliamentary inquiry into safeguarding the nation's food security. Findings from the UN Food Systems Summit in 2021, the subsequent dialogues in the Food Systems Summit +2 Stocktaking Moment in 2023, and the most recent UN Climate Change Conference in 2023 , reinforce the urgency of the task.
A major challenge to transformative change is that components of our food systems are locked into unsustainable practices. Large-scale food production is linked to almost 80% of global deforestation and 70% of freshwater use.
High food volumes have been achieved by intensifying yields at ever lower costs. In addition, there are various players that currently dominate food systems. They have significant incentives to maintain existing unsustainable practices.
Secondly, technological interventions seeking to optimize farming practices have had unintended consequences. The Green Revolution of the 1960s focused on technological research and development to address poverty and food insecurity in developing countries. However, it also resulted in negative consequences. These included environmental degradation and social inequities due to geography and local capacity.
Thirdly, ambitious solutions—particularly those requiring rapid, widespread, and significant change—are frequently unfeasible in the short-term. They rely on public acceptance, institutional capacity, political tenability, and land availability.
Consider changing diets. This depends on rapid behavioral change in the eating habits of billions of people globally, while overcoming strong cultural and social norms. There is a natural unwillingness to sacrifice in the short term, in order to achieve higher goals in the long term.
Approaches to transformative change and its complexities in food systems are still emerging. Transformation of our food systems can be informed by research and practice in other domains. This work is more progressed in areas such as industries' decarbonization.
We can learn how to address complex food system challenges by analyzing different examples from different contexts. There are three obvious areas of overlap.
There is an opportunity to align initiatives in ways that reinforce and catalyze food system transformation. For example, some of the challenges in the grocery retail sector in Australia have been attributed to consumer behavior, labor shortages, and disruptions to supply chains.
Addressing these challenges requires multiple processes to co-align in a way that catalyzes further change. This includes overcoming economic barriers, and changing the institutions that perpetuate current systems. All this needs to occur while harnessing emerging innovation such as shifts in lifestyle and the development of new markets to offer consumers greater choice.
Solutions for sustainability transitions in a range of sectors all point to the need for more integrated approaches. Pressure for land use change can arise from conflicting goals, such as food and fuel production and the risk we may compromise Australia's food security in the process.
Integrated assessments for Sustainable Development Goals teach us to balance solutions without compromising food security. They also offer insights on building resilience.
Experimentation
Australian food systems are neither fully global nor entirely local. This is thanks, in part, to the COVID pandemic, war and conflict in various regions, and an ongoing string of climate events. All of these factors, from local to global, influence our food systems and render it vulnerable in its current state.
Model-based experiments, which use simulations to replicate real-world conditions, can help identify solutions for making food systems resilient to emerging challenges. Social learning experiments show that trying diverse, parallel innovations can help identify and test multiple feasible solutions. Additionally, we can learn from their resilience during future crises.
The growth of the organic food market during the 1990s is an example of experimentation through which businesses learned about consumer demand, preferences, and motives.
These three approaches, drawing on existing research and learning, provide us with a handy shortcut to get the ball rolling. But this is just the tip of the iceberg. There is still much work to be done for learning and informing food system transformations.
New institutions are likely to be required to guide future food system transformations by building new knowledge and learning from past transformations.
Food System Horizons is seeking to implement the findings of the extensive consultation undertaken in building the Reshaping Australian Food Systems roadmap.
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BMC Genomics volume 25 , Article number: 645 ( 2024 ) Cite this article
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Wenchang chickens are one of the most popular local chicken breeds in the Chinese chicken industry. However, the low feed efficiency is the main shortcoming of this breed. Therefore, there is a need to find a more precise breeding method to improve the feed efficiency of Wenchang chickens. In this study, we explored important candidate genes and variants for feed efficiency and growth traits through genome-wide association study (GWAS) analysis.
Estimates of genomic heritability for growth and feed efficiency traits, including residual feed intake (RFI) of 0.05, average daily food intake (ADFI) of 0.21, average daily weight gain (ADG) of 0.24, body weight (BW) at 87, 95, 104, 113 days of age (BW87, BW95, BW104 and BW113) ranged from 0.30 to 0.44. Important candidate genes related to feed efficiency and growth traits were identified, such as PLCE1, LAP3, MED28, QDPR, LDB2 and SEL1L3 genes.
The results identified important candidate genes for feed efficiency and growth traits in Wenchang chickens and provide a theoretical basis for the development of new molecular breeding technology.
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Poultry production is an important entreprise worldwide. Chicken is considered a healthy white meat source that is lower in fat, calories and cholesterol than other red meat sources [ 1 ]. In recent years, the promotion of white meat consumption has gradually become a trend [ 2 ]. As an important part of the white meat market, chickens represent an efficient and inexpensive source of animal protein [ 3 ]. Since the 1980s, with the continuous development of modern broiler production, China has become the second largest country in the world in terms of chicken production and consumption [ 4 ]. The main breeds of chickens are native breeds and broilers [ 5 ]. Wenchang chicken (Fig. 1 ) is one of the most popular chicken breeds in the local chicken industry, originating from Hainan Island in the South China Sea and has been raised for 400 years. It is famous for its excellent meat quality and is one of the four famous dishes from Hainan [ 6 ]. It’s an economic mainstay of animal husbandry in Hainan Province, with an annual output of nearly 100 million chickens and a total output value of 1.78 billion dollars in 2020 [ 7 ].
Picture for Wenchang chicken. ( a ), roosters ( b ), hens
However, the primary shortcoming of Wenchang chickens is their low feed efficiency [ 8 ]. Therefore, there is an urgent need to find a more reliable breeding method to improve the feed efficiency of Wenchang chickens. Feed represents more than 70% of the total cost of poultry production, and improving feed efficiency has consistently been the goal of any chicken breeding strategy [ 9 ]. Feed efficiency is contingent upon the relation between the feed intake (FI) and the growth (or bodyweight gain) of an animal and is quantified by several indexes, such as RFI and feed conversion rate (FCR) [ 10 , 11 , 12 ]. Feed efficiency is influenced by several factors, including the breed and its sex, age, diet, and management [ 13 , 14 ].The RFI is an important index measuring feed efficiency, and it is defined as the difference between the actual and expected feed intake [ 15 ].In 1963, the concept of RFI was first proposed in beef cattle research [ 16 ], and it was first applied to chickens by Luiting in 1991 [ 17 ], covering the calculation methods of RFI, heritability calculation, and the phenotypic and genetic correlations with relevant traits. Since RFI reflects the variation in feed efficiency, it appears to be independent of growth traits [ 15 ]. Studies have shown that RFI is moderately heritable in poultry [ 12 , 18 ]. The study by Bai et al. demonstrates that selecting for low RFI can improve poultry feed efficiency without compromising growth performance [ 19 ]. Growth traits are key in poultry breeding, and the properties of growth traits need to be considered in breeding for feed efficiency. In recent years, GWAS has been applied in poultry to explore the association between host genetics and economic traits [ 20 ]. Earlier studies identified candidate genes including NSUN3 , EPHA6 , and AGK for broilers RFI, while LAP3 was identified as a candidate gene for broiler body weight [ 21 , 22 , 23 , 24 , 25 , 26 ]. By deeply studying the functions and regulatory mechanisms of these genes, our aim is to provide more efficient and sustainable solutions for the chicken breeding technologies [ 4 ].
In the breeding process of this study, the focus will be on maintaining the excellent meat quality while moderately improving the feed efficiency and growth rate. The objectives of this study were (1) to determine the inheritance pattern of feed efficiency and growth traits to provide a basis for formulating a better breeding method for Wenchang chickens, and (2) to find key variants affecting feed efficiency and growth traits of Wenchang chickens and also to elucidate the molecular mechanism of feed efficiency and growth traits.
Experimental birds.
The study uses 1,547 chickens hatched and raised by Hainan Tanniu Wenchang Chicken Co., Ltd. These birds are from a commercial line of Wenchang chicken selected for 18 generations. All birds were raised in three-tier battery cages (one bird per cage) under the same management and nutritional conditions. The diet was formulated based on the National Research Council (NRC) requirements [ 27 ] and the Feeding Standards of Chickens established by the Ministry of Agriculture, Beijing, China [ 28 ]. The chickens were raised from 87 to 113 days to collect data on individual phenotypes.
The phenotypes measured in this study included body weight and feed intake of chickens.
For body weight measurements, an electronic scale with a precision of 0.1 g was utilized. The trough was emptied of any remaining feed 12 h prior to weighing, and body weights were recorded at 87, 95, 104, and 113 days of age.
Feed intake in this experiment was recorded for 26 days. Fresh feed was added at a fixed time every day and recorded. In addition to the above measured phenotypes, the phenotypes calculated from the measured phenotypes included FI, ADFI, ADG, Metabolic weight in the middle of the test (MWT), and RFI.
RFI, g/d: The RFI was obtained from the multiple regression equation of average daily feed intake and metabolic weight in the middle of the test and average daily weight gain [ 18 ]. The equation was as follows:
where ADFI represents the mean average daily feed intake, µ is the intercept, sex is a fixed effect, MWT and ADG are as defined above, β1 and β2 represent the partial regression coefficient.
At 112 days of age, 2 mL of blood was collected from the wing vein, placed in an anticoagulant tube containing EDTA-K2, mixed, and stored at -20℃ until later analysis. Genomic DNA was extracted from blood samples with the phenol-chloroform method. Genotyping was conducted with a customized chicken 55 K SNP array (Beijing Compass Biotechnology Co., Ltd., Beijing, China) [ 29 ]. QC of the generated genotype data was achieved using PLINK (V2.0) ( https://www.cog-genomics.org/plink/2.0/ ) software. The specific process setting the individual genotype detection rate at ≥ 90%, single SNP detection rate at ≥ 90%, minimum allele frequency (MAF) of 95%, and retention of SNPs on autosomes 1–28. A total of 45,278 SNPs in 1,479 chickens (762 males and 717 females) passed the QC. Whole genome resequencing of 247 individuals from the 1,479 Wenchang chickens, including 25 males and 222 females was carried out. The sequencing generated 150 bp paired end reads on an Illumina NovaSeq 6000 platform with the average depth of approximately 10×, at the Shenzhen BGI Co., Ltd. The QC standards were as follows: setting the detection rate of individual genotype at ≥ 90%, the detection rate of single SNP site at ≥ 90%, a MAF of 95%, and Hardy-Weinberg equilibrium (HWE) at P < 0.000001. A total of 12,590,784 autosomal SNPs in 247 individuals were passed the QC.
Beagle 5.2 software was used to impute the 55 K chip data to the whole-genome sequence (WGS) level [ 30 ]. Before imputation, inconsistencies between the target panel and the reference panel were checked using conform-gt software ( http://faculty.washington.edu/browning/conform-gt.html ). Then, the 55 K SNP chip data were populated to the resequencing level using Beagle 5.2 software. The QC condition: HWE at P < 1.00e-6, setting the detection rate of individual genotype at ≥ 90%, the detection rate of single SNP site at ≥ 90%, and a MAF of ≥ 0.05. A total of 12,184,765 autosomal SNPs from 1,479 samples passed QC.
Phenotypic correlation coefficients were calculated using ggpairs within the GGally R package, and then genetic parameter estimation was performed using restricted maximum likelihood (REML) of GCTA v1.93.2 beta software [ 31 , 32 ]. The statistical model used was:
where, \(\text{y}\) is a vector of observations, \(\text{b}\) is a vector of fixed effects (i.e., sex), \({\upalpha }\) is the random vector representing the genomic effects, \(e\) is the vector of random residual effects, \(\text{X}\) and \(\text{Z}\) are incidence matrices. The distribution of the random animal effect \(\alpha\) is \(\alpha \ \sim N(0,\,{\rm{G}}\sigma _a^2)\) with \(\text{G}\) is being the genomic relationship matrix, and \({\sigma }_{a}^{2}\) being the additive genetic variance.
A GWAS analysis was carried out between all the genotyped SNPs and feed efficiency and growth traits using a mixed linear model (MLM). The MLM for feed efficiency and growth traits was performed using sex (female or male) as a fixed effect and the top three principal components (PCs) as covariates. All association tests were performed using the MLM option in GCTA based on the following model [ 33 ]:
where y is a vector of observations, X and Z are incidence matrices for the vectors for parameters b and µ, b is a vector of fixed effects including the sex and three eigenvectors from principal component analysis (PCA), µ is the vector of the additive genetic effect of the candidate SNP to be tested for association, and e is the vector of the residual effect.
The Bonferroni correction method was used in this study to determine the significance thresholds, and the formula for performing the Bonferroni corrected multiple tests was as follows:
Where \(P\) is the corrected significance threshold, \(\alpha\) represents the significance threshold for a single test, and N represents the number of multiple hypothesis tests, i.e., the number of SNPs analysed by GWAS. We calculated the number of genome-wide independent markers using the PLINK (V1.9) command -indep-pairwise, with a window size of 25 SNPs, a step of five SNPs, and an r 2 threshold of 0.2. Manhattan and quantile‒quantile (Q-Q) plots were derived from the GWAS results using the qqman ( https://cran.r-project.org/web/packages/qqman/ ) and Cairo ( http://www.rforge.net/Cairo/ ) packages within R software ( http://www.r-project.org/ ). LD blocks of target regions were performed using Haploview v4.2 software [ 34 ]. For additive and dominance effects of important SNPs on traits, the calculation process in this study was done in ASReml v4.1 software ( https://asreml.kb.vsni.co.uk/knowledge-base/asreml_documentation ). The SNP positions were updated according to the newest release from Ensembl ( https://asia.ensembl.org/index.html ). Identification of the closest genes to genome-wide significant and suggestive variants was obtained using the ChIPpeakAnno package ( https://www.bioconductor.org/packages/devel/bioc/vignettes/ChIPpeakAnno/inst/doc/pipeline.html ). Gene function enrichment analysis was performed using bioinformatics ( https://www.bioinformatics.com.cn ).
In this study, the RFI or BW of 127 broiler chickens was ranked from low to high. Using individuals with the highest ( n = 15) and lowest RFI ( n = 15) phenotypes, and individuals with the highest ( n = 15) and lowest ( n = 15) BW. The Wenchang chickens used in this study were a mix of males and females, so we did not differentiate between genders in the 30 chickens selected. Gene expression of important candidate genes in the different groups can demonstrate the reliability of the GWAS results. Figures were generated using GraphPad Prism 8 [ 4 ].
Descriptive statistics of the feed efficiency traits of Wenchang chickens are shown in Table 1 ; Fig. 2 . According to the body weights at 87, 95, 104 and 113 days, Wenchang chickens exhibited slow growth, with an ADFI of 82.52 g/d and an ADG of 8.66 g/d. The RFI ranged from − 39.14 to 36.25 g/d.
Phenotypic data and correlation analysis of Wenchang chicken
Comparison of density markers before and after the imputation in Fig. 3 revealed a significant increase in loci after imputation. The estimated genetic parameters of residual feed intake and body weight are shown in Table 2 . The heritability of the RFI, BW, ADFI and ADG ranged from moderate and low level (0.05–0.44). RFI had the highest genetic association with ADFI and ADG, at 0.92 and 0.86, respectively ( P < 0.001). Regarding phenotypic correlation, the genetic correlation between RFI and ADFI was 0.63 and decreased with body weight to a minimum of 0.20. Based on the result, the heritability of RFI in Wenchang chicken is low, while the heritability of body weight is not significantly different from that of other breeds.
Comparison of density markers before and after the imputation
The GWAS results showed significant SNPs on GGA 2, 6 and 26 were associated with RFI (expansion coefficient λ of 0.987) (Fig. 4 a-b). One of significant SNPs 6_21123592 on GGA6 was located on the intron of the PLCE1 gene, with this SNP explaining 2.46% of the genetic variation. Additionally, significant SNPs 2_45795056 and 26_2851843 were located in the lncRNA introns of the ENSGALG00000052614 and ENSGALG00000001264 genes, respectively (Table 3 ). The GWAS results showed significant SNPs on GGA 2, 4, 6, 19, and 28 were associated with ADFI (expansion coefficient λ of 0.994) (Fig. 4 c-d). Specifically, the significant SNPs 4_75971941, 4_85488222, 19_2763444, and 28_388715 were found on the genes LAP3, IMMT, GATSL2 , and FBN3 , respectively (Table 3 ). The SNP 6_21123592 for RFI in the CT genotype exhibited significantly higher values than those in the CC genotype (Table 4 ). Another SNP 2_45795056 showed a significant additive effect on ADFI but a nonsignificant effect on the two feed efficiency traits assessed in this study. The estimated additive effect of candidate SNP 6_21123592 on RFI and ADFI were − 0.81 and 2.67 respectively, while the dominance effect was − 2.99 and 0.93 respectively. Significant dominance effects were observed on both RFI and ADFI, while the additive effect was not significant (Table 5 ).
Manhattan and Q-Q plots of GWAS for feed efficiency traits. The horizontal red lines indicate the thresholds for genome-wide significance ( P = 3.47E-08), and the horizontal blue lines indicate the thresholds for suggestive significance ( P = 6.93E-07)
The GWAS analysis results for growth traits at different ages are presented in Fig. 5 ; Table 6 . Significant QTLs affecting BW, ADG, and ADFI were observed in the 73.15–76.55 Mb interval of GGA4. Specifically, BW87 (Fig. 5 a, Table S1 ) had 661 SNPs in the significant interval, BW95 had 933 SNPs (Fig. 5 c, Table S2 ), BW104 had 1018 SNPs (Fig. 5 e, Table S3 ), BW113 had 1150 SNPs (Fig. 5 g, Table S4 ), and ADFI and ADG had 3 and 7 SNPs (Table 3 ; Fig. 4 c and e). The most prominent SNP among these traits was 4_75971941, located in the intron of the LAP3 gene. There were 13 important candidate genes in the colocalization interval of different day-ages, including LAP3, KCNIP4, NCAPG, LDB2, FAM184B, SEL1L3, ZCCHC4, PPARGC1A, PACRGL, SLIT2, LCORL, MED28 and QDPR (Table 6 ). Through gene function enrichment analysis (Figure S1 ), we identified significant enrichment primarily in functions such as RNA biosynthetic process, regulation of RNA metabolic process, and regulation of cellular macromolecule biosynthetic process.
Manhattan and Q-Q plots of GWAS for growth traits. The horizontal red lines indicate the thresholds for genome-wide significance ( P = 3.47E-08), and the horizontal blue lines indicate the thresholds for suggestive significance ( P = 6.93E-07)
The four LD blocks were detected within the common location of growth traits on GGA4:75971.28-75974.42 kb (Fig. 6 ), each containing 3–10 SNPs. The most significant SNP 4_75971941 was located on block1, which is located in the intron of the LAP3 gene. Genotype analysis showed that the body weight of the CC genotype at different ages was significantly higher than that of the CT genotype. Specifically, the weight of the CC genotype for BW87 was 54.89 g heavier than that of the CT genotype, and the weight of the CC genotype for BW113 was 82.80 g heavier than that of the CT genotype (Fig. 6 b-g). The estimated additive effects of candidate SNP 4_75971941 on body weight at different ages were 37.97, 46.67, 53.38 and 60.53, while the dominance effects were − 16.92, -16.19, -17.98 and − 22.26. SNP 4_75971941 had significant additive effects on BW and ADG ranging from 37.97 to 60.53. The additive effects increased with age, while the locus had significant dominance effects on BW and nonsignificant dominance effects on ADG and ADFI (Table 7 ).
Association results of the candidate SNPs on GGA4. ( a ), LD analysis of the 25 significant SNPs on GGA4. ( b , c , d , e , f , g ), The phenotypic differences of individuals with different genotypes at rs80610898 on GGA4. *, P < 0.05; **, P < 0.01; ***, P < 0.001; ns, no significance
The candidate sites for body weight were determined to be located in distinct LD blocks. In this study, haplotype association analysis was conducted using the glm model with sex as a fixed effect (Table 8 ). The analysis indicated that block1 had a highly significant effect ( P < 0.01) on BW at various ages, while block2 exhibited a significant effect ( P < 0.05) on BW across different age groups. The results revealed that block2, block3, and block4 all had a significant impact on ADFI ( P < 0.05), while none of the four LD blocks showed a significant impact on RFI ( P > 0.05).
We conducted gene differential expression analysis on all genes mapped to the feed efficiency and growth traits, respectively. Significant differences in phenotypes were observed between the high and low RFI groups (Fig. 7 a) and between the high and low BW groups (Fig. 7 c). Between the high and low RFI groups, the PLCE1 and IMMT genes showed significantly ( P < 0.01) higher expression levels in the high RFI group than in the low RFI group (Fig. 7 b). Similarly, expression level of LAP3, MED28 , and QDPR genes was significantly higher in the high BW group than that in the low BW group ( P < 0.01), while expression of LDB2 and SEL1L3 genes was significantly ( P < 0.05) higher in the low BW group than the high BW group (Fig. 7 d).
The expressions of candidate genes for feed efficiency and growth traits. ( a ), Analysis of RFI Differences between high and low RFI groups. ( b ), Differential expression of related candidate genes between high and low RFI groups. ( c ), Analysis of BW Differences between high and low BW groups. ( d ), Differential expression of related candidate genes between high and low weight groups. Data were expressed as the mean ± SEM ( n = 15), *** P < 0.001, ** P < 0.01, * P < 0.05
Body weight traits (BW87-BW113) of Wenchang chickens were selected for the GWAS analysis because Wenchang chickens are listing age within that age range. Through different trait measurements at this stage, our study revealed that the BW87 was 1,409.48 g, that of BW113 was 1,894.36 g, the ADG was 18.66 g/d, and the relative growth rate was 25.60%. These findings are consistent with previous studies on yellow-feathered chickens in China [ 35 , 36 ]. The low heritability of RFI may be caused by the lack of systematic breeding efforts and the chickens are in the latter stage of growth, similar to the results of previous studies [ 37 , 38 ]. Heritability of BW, ADFI and ADG revealed in the current study ranged from medium to low (0.21–0.44), which was similar to that of other breeds [ 39 , 40 , 41 ]. The phenotypic and genetic correlations between RFI and ADFI were 0.92 and 0.63, similar to the research of Shirali et al [ 42 ]. To optimize breeding outcomes, it is recommended to integrate additional metrics, such as ADFI, and implement a multi-trait selection approach throughout the breeding process, ultimately enhancing breeding results in Wenchang chickens.
To investigate the genetic architecture of feed efficiency and growth traits in Wenchang chickens, a mixed linear model was used for GWAS analysis of related traits. Through GWAS analysis, we identified significant SNP 6_21123592 on GGA6 was located within the intron of the PLCE1 gene associated with RFI. Previous studies have shown that PLCE1 gene is highly expressed in the nervous system and belongs to the phosphoinositide-specific phospholipase C family. The production of second messenger molecules such as diacylglycerol is regulated by activated phosphatidylinositol-specific phospholipase C enzymes, which mediate small GTPases of the Ras superfamily through the activity of its Ras guanine exchange factor. As the effector of heterotrimer and small G protein, PLCE1 is involved in regulating cell growth, T-cell activation, actin organization and cell survival. Mapping the PLCE1 gene function were mostly related to nervous system activity, which regulates the function of the brain in different ways, and the brain was key to regulating diet behavior and body energy homeostasis [ 43 , 44 , 45 , 46 ].
The significant QTL affecting BW, ADG, and ADFI was located in the 73.15–76.55 Mb interval of GGA4.There were 13 important candidate genes in the colocalization interval related to BW, including LAP3, KCNIP4, NCAPG, LDB2, FAM184B, SEL1L3, ZCCHC4, PPARGC1A, PACRGL, SLIT2, LCORL, MED28 and QDPR . Among these candidates LAP3, MED28, QDPR, LDB2 and SEL1L3 demonstrated differential expression between high and low groups. Comparison with the Animal QTL database (Chicken QTL Database at (animalgenome.org)) reveals a total of 305 QTLs related to BW and 221 QTLs associated with ADG within 73.15–76.55 Mb interval of GGA4. LAP3 has been shown to catalyze the hydrolysis of the amino-terminal leucine residues of protein or peptide substrates, with diverse functions in mammals, invertebrates, microbes, and plants [ 47 ]. The primary function of LAP3 lies in protein maturation and degradation, processes crucial for metabolism, development, adaptation, and repair [ 48 ]. LAP3 gene variation may underlie variations in growth rates among species and significant genetic polymorphism of traits of interest in breeding, potentially leading to applications in animal breeding. Furthermore, studies have been conducted on its SNP and its association with growth traits in mammals, such as bovine [ 49 ]. Another study found that the LAP3 gene may have a potential function affecting muscle development in sheep [ 50 ]. Prenatal development stages are directly related to the growth and development of individual skeletal muscle, which determines the number of muscle fibers and postnatal muscle mass and further exerts long-term effects on the postnatal growth of animals [ 51 , 52 , 53 ]. Related research that tracked LAP3 mutations in Hu sheep populations reportedly linked, the mutations with body weight at different growth stages [ 54 ]. In poultry LAP3 gene was found to be associated with chicken growth traits [ 22 ].
Related study linked the MED28 gene with live weight in sheep [ 55 ].We found that the MED28 gene was related to muscle development in pig [ 56 ]. QDPR for an enzyme that regulates tetrahydrobiopterin (BH4), a cofactor for enzymes involved in neurotransmitter synthesis and blood pressure regulation. Therefore, QDPR gene are also important genes in the regulation of growth [ 57 ]. Previous studies have shown that the LDB2 gene located at GGA4 is important for chicken growth traits, and a 31-bp indel was significantly correlated with multiple growth and carcass traits in the F2 population and affected the expression of the LDB2 gene in muscle tissue [ 26 , 58 , 59 ]. It was also identified as an important candidate gene for rapid growth in chickens and had the strongest association with late body weight in Jinghai yellow chicken hens [ 22 , 60 , 61 ]. According to relevant studies, the KCNIP4 gene is located on GGA4, and the candidate gene belongs to the potassium channel interaction protein family and has a wide range of physiological functions, including heart rate regulation, insulin secretion, neurotransmitter release, and smooth muscle contraction. It was considered to be an important candidate gene for growth traits of chickens. In addition, it was reported in different breeds and different growth stages, which also verified that the KCNIP4 and FAM184B genes can affect the growth and development of chickens [ 58 , 62 , 63 ]. Studies have shown that the NCAPG gene is an important candidate gene for mammalian body size growth traits in growth trait association analysis of horses, sheep, and domestic donkeys and is involved in chromosome condensation and methylation [ 64 , 65 , 66 , 67 ]. The FAM184B gene has been found in previous studies to be associated with cattle carcass weight [ 68 ].
Transcriptomic data enable the quantification of DNA or RNA abundance and expression levels [ 69 ]. Differential analysis between different groups is conducted by measuring the expression levels of gene RNA, thereby identifying distinct patterns and variations in gene expression among different groups. The results of related studies also confirm gene expression data of important candidate genes in different groups can demonstrate the reliability of the GWAS results [ 70 , 71 ]. We showed that expression of PLCE1 in the high and low RFI groups of broilers, validatied it a key candidate gene for RFI. We found that the candidate gene LAP3 was related to the BW, ADFI and ADG traits, as demonstrated by the significant difference in the expression of LAP3 in high and low body recombination in broilers, and relevant research reports also indicated that this might be an important candidate gene affecting growth traits. This study also found that the candidate genes LDB2 , KCNIP4 , FAM184B , and NCAPG were located on GGA4 and were related to growth traits, which provides an important reference value for subsequent research on the growth traits of Wenchang chickens.
The validity of the SNPs and candidate genes obtained in this study is worth further extensive verification. The significant SNPs and candidate genes identified in this study can be incorporated into the chip markers in future research. By utilizing genomic selection breeding techniques, these markers can be used to breed and improve the target traits of WenChang chickens, ultimately enhancing breeding efficiency.
In this study, we identified that the significant SNP 6_21123592 was located in candidate gene PLCE1 for feed efficiency traits of Wenchang chickens, and the significant SNP 4_75971941 was located in candidate gene LAP3. Other candidate genes including MED28, QDPR, LDB2 , and SEL1L3 were identified for growth traits of Wenchang chickens. This provides a good theoretical basis for developing methods of Wenchang chickens breeding, and by further studying the functions and regulatory mechanisms of these genes, we could provide more efficient solutions for breeding of these chickens.
The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences that are publicly accessible at https://bigd.big.ac.cn/gsa/browse/CRA016976.
Yang XT, Sun JH, Zhao GP, Li W, Tan XD, Zheng MQ, Feng FR, Liu DW, Wen J, Liu RR. Identification of major loci and candidate genes for meat production-related traits in broilers. Front Genet 2021, 12.
Kong F, Zhao G, He Z, Sun J, Wang X, Liu D, Zhu D, Liu R, Wen J. Serum creatine kinase as a Biomarker to predict wooden breast. Front Physiol. 2021;12:711711.
Article PubMed PubMed Central Google Scholar
Liu Y, Li HJ, Wang M, Zhang XH, Yang L, Zhao CJ, Wu CX. Genetic architectures and selection signatures of body height in Chinese indigenous donkeys revealed by next-generation sequencing. Anim Genet. 2022;53(4):487–97.
Article CAS PubMed Google Scholar
Tan X, Liu R, Zhao D, He Z, Li W, Zheng M, Li Q, Wang Q, Liu D, Feng F et al. Large-scale genomic and transcriptomic analyses elucidate the genetic basis of high meat yield in chickens. J Adv Res 2023.
Havenstein GB. Poultry breeding and Genetics. Poult Sci. 1991;70(3):662–3.
Article Google Scholar
Tan Z, Luo LL, Wang XZ, Wen Q, Zhou L, Wu KB. Characterization of the cecal microbiome composition of Wenchang chickens before and after fattening. PLoS ONE 2019, 14(12).
Gu LH, Jiang QC, Chen YY, Zheng XL, Zhou HL, Xu TS. Transcriptome-wide study revealed m6A and miRNA regulation of embryonic breast muscle development in Wenchang chickens. Front Vet Sci 2022, 9.
Tian S, Tang W, Zhong Z, Wang Z, Xie X, Liu H, Chen F, Liu J, Han Y, Qin Y et al. Identification of Runs of Homozygosity Islands and Functional Variants in Wenchang Chicken. Animals (Basel) 2023, 13(10).
Aggrey SE, Karnuah AB, Sebastian B, Anthony NB. Genetic properties of feed efficiency parameters in meat-type chickens. Genet Sel Evol 2010, 42.
Zampiga M, Calini F, Sirri F. Importance of feed efficiency for sustainable intensification of chicken meat production: implications and role for amino acids, feed enzymes and organic trace minerals. World Poult Sci J. 2021;77(3):639–59.
Titus HW, Mehring AL, Brumbaugh JH. Variation of feed Conversion. Poult Sci. 1953;32(6):1074–7.
Li W, Zheng MQ, Zhao GP, Wang J, Liu J, Wang SL, Feng FR, Liu DW, Zhu D, Li QH et al. Identification of QTL regions and candidate genes for growth and feed efficiency in broilers. Genet Sel Evol 2021, 53(1).
Sell-Kubiak E, Wimmers K, Reyer H, Szwaczkowski T. Genetic aspects of feed efficiency and reduction of environmental footprint in broilers: a review. J Appl Genet. 2017;58(4):487–98.
He Z, Liu R, Wang M, Wang Q, Zheng J, Ding J, Wen J, Fahey AG, Zhao G. Combined effect of microbially derived cecal SCFA and host genetics on feed efficiency in broiler chickens. Microbiome. 2023;11(1):198.
Article CAS PubMed PubMed Central Google Scholar
Wen CL, Yan W, Zheng JX, Ji CL, Zhang DX, Sun CJ, Yang N. Feed efficiency measures and their relationships with production and meat quality traits in slower growing broilers. Poult Sci. 2018;97(7):2356–64.
Koch RM, Swiger LA, Chambers D, Gregory KE. Efficiency of feed use in beef cattle. J Anim Sci. 1963;22(2):486–94.
Luiting P, Urff EM. Optimization of a model to estimate residual feed consumption in the laying hen. Livest Prod Sci. 1991;27(4):321–38.
Case LA, Wood BJ, Miller SP. The genetic parameters of feed efficiency and its component traits in the Turkey (Meleagris gallopavo). Genet Sel Evol. 2012;44(1):2.
Bai H, Guo Q, Yang B, Dong Z, Li X, Song Q, Jiang Y, Wang Z, Chang G, Chen G. Effects of residual feed intake divergence on growth performance, carcass traits, meat quality, and blood biochemical parameters in small-sized meat ducks. Poult Sci. 2022;101(9):101990.
Tan X, He Z, Fahey AG, Zhao G, Liu R, Wen J. Research progress and applications of genome-wide association study in farm animals. Anim Res One Health. 2023;1(1):56–77.
Hu ZL, Park CA, Wu XL, Reecy JM. Animal QTLdb: an improved database tool for livestock animal QTL/association data dissemination in the post-genome era. Nucleic Acids Res. 2013;41(D1):D871–9.
Liu R, Sun Y, Zhao G, Wang F, Wu D, Zheng M, Chen J, Zhang L, Hu Y, Wen J. Genome-wide association study identifies loci and candidate genes for body composition and meat quality traits in Beijing-You chickens. PLoS ONE. 2013;8(4):e61172.
Moreira GCM, Salvian M, Boschiero C, Cesar ASM, Reecy JM, Godoy TF, Ledur MC, Garrick D, Mourão GB, Coutinho LL. Genome-wide association scan for QTL and their positional candidate genes associated with internal organ traits in chickens. BMC Genomics. 2019;20(1):669.
Li J, Akanno EC, Valente TS, Abo-Ismail M, Karisa BK, Wang Z, Plastow GS. Genomic heritability and Genome-Wide Association Studies of Plasma Metabolites in crossbred beef cattle. Front Genet. 2020;11:538600.
Dou D, Shen L, Zhou J, Cao Z, Luan P, Li Y, Xiao F, Guo H, Li H, Zhang H. Genome-wide association studies for growth traits in broilers. BMC Genomic Data. 2022;23(1):1.
Li Y-d, Bai X, Liu X, Wang W-j, Li Z-w, Wang N, Xiao F, Gao H-h, Guo H-s, Li H, et al. Integration of genome-wide association study and selection signatures reveals genetic determinants for skeletal muscle production traits in an F2 chicken population. J Integr Agric. 2022;21(7):2065–75.
Article CAS Google Scholar
National Research Council (NRC). In. The grants Register 2023: the complete guide to Postgraduate Funding Worldwide. London: Palgrave Macmillan UK; 2022. pp. 783–4.
Google Scholar
Liu L, Liu XJ, Cui HX, Liu RR, Zhao GP, Wen J. Transcriptional insights into key genes and pathways controlling muscle lipid metabolism in broiler chickens. In: Bmc Genomics vol. 20; 2019.
Liu R, Xing S, Wang J, Zheng M, Cui H, Crooijmans R, Li Q, Zhao G, Wen J. A new chicken 55K SNP genotyping array. BMC Genomics. 2019;20(1):410.
Browning BL, Zhou Y, Browning SR. A one-penny Imputed Genome from Next-Generation reference panels. Am J Hum Genet. 2018;103(3):338–48.
Kumar SK, Feldman MW, Rehkopf DH, Tuljapurkar S. Limitations of GCTA as a solution to the missing heritability problem (113, pg E61, 2015). P Natl Acad Sci USA. 2016;113(6):E813–813.
Kumar SK, Feldman MW, Rehkopf DH, Tuljapurkar S. Limitations of GCTA as a solution to the missing heritability problem. P Natl Acad Sci USA. 2016;113(1):E61–70.
CAS Google Scholar
Yang JA, Lee SH, Goddard ME, Visscher PM. GCTA: A Tool for Genome-wide Complex Trait Analysis. Am J Hum Genet. 2011;88(1):76–82.
Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21(2):263–5.
Tang H, Gong YZ, Wu CX, Jiang J, Wang Y, Li K. Variation of meat quality traits among five genotypes of chicken. Poult Sci. 2009;88(10):2212–8.
Huang Q, Wen C, Yan W, Sun C, Gu S, Zheng J, Yang N. Comparative analysis of the characteristics of digestive organs in broiler chickens with different feed efficiencies. Poult Sci. 2022;101(12):102184.
Yuan J, Dou T, Ma M, Yi G, Chen S, Qu L, Shen M, Qu L, Wang K, Yang N. Genetic parameters of feed efficiency traits in laying period of chickens. Poult Sci. 2015;94(7):1470–5.
Marchesi JAP, Ono RK, Cantao ME, Ibelli AMG, Peixoto JD, Moreira GCM, Godoy TF, Coutinho LL, Munari DP, Ledur MC. Exploring the genetic architecture of feed efficiency traits in chickens. Sci Rep-Uk 2021, 11(1).
Venturini GC, Stafuzza NB, Cardoso DF, Baldi F, Ledur MC, Peixoto JO, El Faro L, Munari DP. Association between ACTA1 candidate gene and performance, organs and carcass traits in broilers. Poult Sci. 2015;94(12):2863–9.
Mebratie W, Shirali M, Madsen P, Sapp RL, Hawken R, Jensen J. The effect of selection and sex on genetic parameters of body weight at different ages in a commercial broiler chicken population. Livest Sci. 2017;204:78–87.
Mebratie W, Madsen P, Hawken R, Romé H, Marois D, Henshall J, Bovenhuis H, Jensen J. Genetic parameters for body weight and different definitions of residual feed intake in broiler chickens. Genet Sel Evol. 2019;51(1):53.
Shirali M, Varley PF, Jensen J. Bayesian estimation of direct and correlated responses to selection on linear or ratio expressions of feed efficiency in pigs. Genet Sel Evol. 2018;50(1):33.
Rao J, Ashraf S, Tan W, van der Ven AT, Gee HY, Braun DA, Feher K, George SP, Esmaeilniakooshkghazi A, Choi WI, et al. Advillin acts upstream of phospholipase C ϵ1 in steroid-resistant nephrotic syndrome. J Clin Invest. 2017;127(12):4257–69.
Strazza M, Adam K, Smrcka AV, Lerrer S, Mor A. PLCepsilon1 suppresses tumor growth by regulating murine T cell mobilization. Clin Exp Immunol. 2020;200(1):53–60.
Yu S, Choi WI, Choi YJ, Kim HY, Hildebrandt F, Gee HY. PLCE1 regulates the migration, proliferation, and differentiation of podocytes. Exp Mol Med. 2020;52(4):594–603.
Huang LH, Liao CD, Wu HH, Huang PW. PLCE1 is a poor prognostic marker and may promote immune escape from osteosarcoma by the CD70-CD27 signaling pathway. Bosnian J Basic Med. 2022;22(6):992–1004.
Yao HH, Ren FZ, Bao YB, Dong YH, Lin ZH. Molecular characterization and expression of the LAP3 gene and its Association with Growth traits in the blood clam Tegillarca Granosa. Fishes-Basel 2021, 6(4).
de Seny D, Baiwir D, Bianchi E, Cobraiville G, Deroyer C, Poulet C, Malaise O, Paulissen G, Kaiser MJ, Hauzeur JP et al. New proteins contributing to Immune Cell infiltration and pannus formation of synovial membrane from Arthritis diseases. Int J Mol Sci 2021, 23(1).
Zheng X, Ju ZH, Wang J, Li QL, Huang JM, Zhang AW, Zhong JF, Wang CF. Single nucleotide polymorphisms, haplotypes and combined genotypes of LAP3 gene in bovine and their association with milk production traits. Mol Biol Rep. 2011;38(6):4053–61.
Ge L, Su PW, Wang S, Gu YF, Cao XK, Lv XY, Wang SH, Getachew T, Mwacharo JM, Haile A et al. New Insight into the Role of the Leucine Aminopeptidase 3 (LAP3) in Cell Proliferation and Myogenic Differentiation in Sheep Embryonic Myoblasts. Genes-Basel 2022, 13(8).
Rehfeldt C, Kuhn G. Consequences of birth weight for postnatal performance and carcass quality in pigs as related to myogenesis. J Anim Sci. 2006;84(Supplsuppl):E113–123.
Article PubMed Google Scholar
Du M, Tong J, Zhao J, Underwood KR, Zhu M, Ford SP, Nathanielsz PW. Fetal programming of skeletal muscle development in ruminant animals. J Anim Sci. 2010;88(13 Suppl):E51–60.
Du M, Zhao JX, Yan X, Huang Y, Nicodemus LV, Yue W, McCormick RJ, Zhu MJ. Fetal muscle development, mesenchymal multipotent cell differentiation, and associated signaling pathways. J Anim Sci. 2011;89(2):583–90.
La YF, Zhang XX, Li FD, Zhang DY, Li C, Mo FT, Wang WM. Molecular characterization and expression of SPP1, LAP3 and LCORL and their Association with Growth traits in Sheep. Genes-Basel 2019, 10(8).
Ramos Z, Garrick DJ, Blair HT, Vera B, Ciappesoni G, Kenyon PR. Genomic regions Associated with wool, Growth and Reproduction traits in Uruguayan Merino Sheep. Genes-Basel. 2023;14(1):167.
Zhuang X, Xie F, Lin Z, Luo J, Chen T, Xi Q, Zhang Y, Sun J. Effect of mir-493-5p on proliferation and differentiation of myoblast by targeting ANKRD17. Cell Tissue Res. 2023;393(1):119–32.
Girish A, Sutar S, Murthy TPK, Premanand SA, Garg V, Patil L, Shreyas S, Shukla R, Yadav AK, Singh TR. Comprehensive bioinformatics analysis of structural and functional consequences of deleterious missense mutations in the human QDPR gene. J Biomol Struct Dynamics 2023:1–17.
Wang S, Wang Y, Li Y, Xiao F, Guo H, Gao H, Wang N, Zhang H, Li H. Genome-Wide Association Study and Selective Sweep Analysis Reveal the Genetic Architecture of body weights in a Chicken F2 Resource Population. Front Vet Sci. 2022;9:875454.
Zhang Y, Wang Y, Li Y, Wu J, Wang X, Bian C, Tian Y, Sun G, Han R, Liu X, et al. Genome-wide association study reveals the genetic determinism of growth traits in a Gushi-Anka F2 chicken population. Heredity. 2021;126(2):293–307.
Wang WH, Wang JY, Zhang T, Wang Y, Zhang Y, Han K. Genome-wide association study of growth traits in Jinghai Yellow chicken hens using SLAF-seq technology. Anim Genet. 2019;50(2):175–6.
Wei C, Hou D, Feng Y, Li T, Jing Z, Li W, Han R, Li G, Sun G, Tian Y, et al. Molecular characterization and a duplicated 31-bp indel within the LDB2 gene and its associations with production performance in chickens. Gene. 2020;761:145046.
Jin CF, Chen YJ, Yang ZQ, Shi K, Chen CK. A genome-wide association study of growth trait-related single nucleotide polymorphisms in Chinese Yancheng chickens. Genet Mol Res. 2015;14(4):15783–92.
Cha J, Choo H, Srikanth K, Lee SH, Son JW, Park MR, Kim N, Jang GW, Park JE. Genome-Wide Association Study Identifies 12 Loci Associated with Body Weight at Age 8 weeks in Korean native chickens. Genes (Basel) 2021, 12(8).
Makvandi-Nejad S, Hoffman GE, Allen JJ, Chu E, Gu E, Chandler AM, Loredo AI, Bellone RR, Mezey JG, Brooks SA, et al. Four loci explain 83% of size variation in the horse. PLoS ONE. 2012;7(7):e39929.
Bouwman AC, Daetwyler HD, Chamberlain AJ, Ponce CH, Sargolzaei M, Schenkel FS, Sahana G, Govignon-Gion A, Boitard S, Dolezal M, et al. Meta-analysis of genome-wide association studies for cattle stature identifies common genes that regulate body size in mammals. Nat Genet. 2018;50(3):362–7.
Posbergh CJ, Huson HJ. All sheeps and sizes: a genetic investigation of mature body size across sheep breeds reveals a polygenic nature. Anim Genet. 2021;52(1):99–107.
Liu R, Kong F, Xing S, He Z, Bai L, Sun J, Tan X, Zhao D, Zhao G, Wen J. Dominant changes in the breast muscle lipid profiles of broiler chickens with wooden breast syndrome revealed by lipidomics analyses. J Anim Sci Biotechno. 2022;13(1):93.
Naserkheil M, Mehrban H, Lee D, Park MN. Genome-wide Association Study for Carcass Primal cut yields using single-step bayesian Approach in Hanwoo Cattle. Front Genet. 2021;12:752424.
Wu Z, Wang Z, Xie Y, Liu G, Shang X, Zhan N. Transcriptome and metabolome profiling provide insights into flavonoid synthesis in Acanthus Ilicifolius Linn. Genes (Basel) 2023, 14(3).
Shao M, Shi K, Zhao Q, Duan Y, Shen Y, Tian J, He K, Li D, Yu M, Lu Y et al. Transcriptome analysis reveals the differentially expressed genes Associated with Growth in Guangxi Partridge Chickens. Genes (Basel) 2022, 13(5).
Li X, Yang J, Shen M, Xie XL, Liu GJ, Xu YX, Lv FH, Yang H, Yang YL, Liu CB, et al. Whole-genome resequencing of wild and domestic sheep identifies genes associated with morphological and agronomic traits. Nat Commun. 2020;11(1):2815.
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We thanks the Institute of Animal Science of CAAS, Chinese Academy of Agricultural Sciences for the support of this experiment.
This research was supported by the STI 2030-Major Project (2023ZD04072), the Wenchang Chicken superiority characteristic industrial cluster project (WCSCICP20211106), the Project of Sanya Yazhou Bay Science and Technology City (SKJC-2022-PTDX-002), the Special Project for Southern Propagation of the Chinese Academy of Agricultural Sciences (YYLH04), the National chicken industry technology system project (CARS-41), the Study of the Key Genetic Resources [JBGS (2021) 107].
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Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124, P.R. China
Keqi Cai & Yuxiao Chang
State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry of Agriculture, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100193, P.R. China
Keqi Cai, Ranran Liu, Huanxian Cui, Na Luo, Jie Wen & Guiping Zhao
The Sanya Research Institute, Hainan Academy of Agricultural Sciences, Sanya, 572025, P.R. China
Limin Wei & Guiping Zhao
Hainan (Tan Niu) Wenchang Chicken Co., LTD, Haikou, 570100, P.R. China
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Cai, K., Liu, R., Wei, L. et al. Genome-wide association analysis identify candidate genes for feed efficiency and growth traits in Wenchang chickens. BMC Genomics 25 , 645 (2024). https://doi.org/10.1186/s12864-024-10559-w
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FILE - Thermometers are seen atop a small-scale pasteurizer in Plainfield, Vt., on March 13, 2012. On Friday, June 28, 2024, U.S. officials said a new study provides reassurance that pasteurization kills bird flu virus in cow’s milk. (AP Photo/Toby Talbot)
NEW YORK (AP) — A new study that recreated commercial pasteurization in a government lab provides reassurance that heat treatment kills bird flu virus in cow’s milk, U.S. officials said Friday.
When the bird flu known as H5N1 was first detected in U.S. dairy cows earlier this year, there were no studies of whether heat treatment killed the virus in cows milk. But officials were comforted by studies that showed the pasteurization of eggs — which involves heating at a lower temperature and for a shorter amount of time – worked, said the Food and Drug Administration’s Donald Prater.
A study in April found that there was no evidence of infectious, live virus in store-bought samples of pasteurized milk, though they did contain dead remnants of it. Some later small studies that attempted to simulate pasteurization showed mixed results.
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The study has been not yet been published in a peer-reviewed journal.
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The loss of social memories caused by sleep deprivation could potentially be reversed using currently available drugs, according to a study in mice presented today (Friday) at the Federation of European Neuroscience Societies (FENS) Forum 2024. Lack of sleep is known to affect the brain, including memory, in mice and in humans, but research is beginning to show that these memories are not lost, they are just 'hidden' in the brain and difficult to retrieve. The new research shows that access to these otherwise hidden social memories can be restored in mice with a drug currently used to treat asthma and chronic obstructive pulmonary disease. The team of researchers have also shown that another drug currently used to treat erectile dysfunction can restore access to spatial memories. Researchers say these spatial memories in mice are akin to humans remembering where they put their keys the night before, whereas the social memories could be compared with remembering a new person you met. The research was presented by Dr Robbert Havekes from the University of Groningen in the Netherlands. He said: "Ever since starting as a PhD student, many years ago, I have been intrigued by the observation that even a single period of sleep deprivation can have a major impact on memory processes and the brain as a whole. The early work published years ago helped us identify some of the molecular mechanisms that mediate amnesia.
By manipulating these pathways specifically in the hippocampus, we have been able to make memory processes resilient to the negative impact of sleep deprivation. In our new studies, we have examined whether we could reverse amnesia even days after the initial learning event and period of sleep deprivation." Dr Robbert Havekes, University of Groningen
The new studies, presented at the FENS Forum and funded by the Air Force Office of Scientific Research (AFOSR), were conducted by Dr Havekes' PhD students Adithya Sarma and Camilla Paraciani, who will also be presenting their work as poster presentations. To study social memories in the lab, the researchers gave mice the opportunity to choose between interacting with a mouse they have never encountered before or a sibling from their own cage. Under normal circumstances, the mice prefer interacting with the new mouse over their litter-mate that they already know. Given the same choice the next day, mice will interact to a similar extent with both their litter-mate and the mouse they met the day before as both mice are now considered familiar. However, if the mice are sleep-deprived after their first encounter then the next day they still prefer to interact with the new mouse as if they never met it before. These findings suggest that they simply cannot recall their previous encounter. The team found they were able to permanently restore these hidden social memories, first using a technique called optogenetic engram technology. This technique allows them to identify neurons in the brain that together form a memory (known as a memory engram) for a specific experience and alter those neurons so they can be reactivated by light. Researchers can then use light to reactivate this specific group of neurons resulting in the recall of the specific experience (in this case a social memory). They were also able to restore the mice's social memories by treating them with roflumilast, a type of anti-inflammatory drug, approved by the US Food and Drug Administration, that is used to treat chronic obstructive pulmonary disease. Dr Havekes says this finding is particularly interesting as it provides a stepping stone towards studies of sleep deprivation and memory in humans, and he is now collaborating with another research group that is embarking on human studies. In parallel, the same researchers have investigated the loss of spatial memory caused by sleep deprivation by studying mice's abilities to learn and remember the location of individual objects. A brief period of sleep deprivation following training meant the mice could not recall the original locations of the object and so they did not notice when an object was moved to a new location during a test. As with the social memories, access to these spatial memories could be restored by treating the mice with another drug, vardenafil, that is currently used to treat erectile dysfunction. This is a second drug that is approved by the US Food and Drug Administration that the researchers have successfully used to reverse amnesia in mice. Dr Havekes said: "We have been able to show that sleep deprivation leads to amnesia in the case of specific spatial and social recognition memories. This amnesia can be reversed days later after the initial learning experience and sleep deprivation episode using drugs already approved for human consumption. We now want to focus on understanding what processes are at the core of these accessible and inaccessible memories. In the long term, we hope that these fundamental studies will help pave the way for studies in humans aimed at reversing forgetfulness by restoring access to otherwise inaccessible information in the brain." Professor Richard Roche is chair of the FENS Forum communication committee and Deputy Head of the Department of Psychology at Maynooth University, Maynooth, County Kildare, Ireland, and was not involved in the research. He said: "This research shows that social and spatial memories seemingly lost through sleep-deprivation can be recovered. Although these studies were carried out in mice, they suggest that it may be possible to recover people's lost social and spatial memories using certain drug treatments that are already approved for human use. There are many situations where people cannot get the amount of sleep they need, so this area of research has obvious potential. However, it will take time and a lot more work to move this research from mice into humans."
Federation of European Neuroscience Societies
Posted in: Medical Science News | Medical Research News | Healthcare News
Tags: Amnesia , Anti-Inflammatory , Asthma , Brain , Chronic , Chronic Obstructive Pulmonary Disease , Drugs , Erectile Dysfunction , Food , Hippocampus , Neurons , Neuroscience , Psychology , Research , Sleep , students , Technology
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