Emerging applications of machine learning in genomic medicine and healthcare.

The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined.

Monitoring the Spatial and Interannual Dynamic of Zostera noltei.

Seagrass is a vital structural and functional element of the marine environment worldwide and is highly valued for its ecological benefits. Monitoring the evolution of the seagrass habitat is essential to understand how this coastal ecosystem changes, and to develop good environmental management practices. For the present study, two remote sensing methods were used to map and monitor Zostera noltei Hornemann, 1832 (Z. noltei), in the Merja Zerga lagoon from 2010 to 2020. These methods which are the random forest algorithm and the object-oriented classification, were convenient to provide significant results. The first approach employed Sentinel-2 images from 2018 to 2020, which were used to extract information on changes in Z. noltei (commonly called dwarf eelgrass) distribution and aboveground biomass estimation. The second involved three orthophotography (orthophoto) mosaics from the years 2010, 2016, and 2018, which were analyzed to map the distribution of the species. It was revealed that Z. noltei coverage has increased by 212 ha since 2010, with most of the growth occurring in the center and upstream part of the lagoon. The mean aboveground biomass of dwarf eelgrass in the lagoon was 78.5 DW/m² in 2018, 92.6 DW/m² in 2019, and 115.2 g DW/m² in 2020. The approach used in this study has provided important insights into the dynamic and mean biomass of Z. noltei in the Merja Zerga lagoon. It is therefore a valuable, non-destructive method that uses freely-available Sentinel-2 satellite data.

Prevalence of specific and recurrent/founder pathogenic variants in BRCA genes in breast and ovarian cancer in North Africa.

BACKGROUND: Elucidation of specific and recurrent/founder pathogenic variants (PVs) in BRCA (BRCA1 and BRCA2) genes can make the genetic testing, for breast cancer (BC) and/or ovarian cancer (OC), affordable for developing nations. METHODS: To establish the knowledge about BRCA PVs and to determine the prevalence of the specific and recurrent/founder variants in BRCA genes in BC and/or OC women in North Africa, a systematic review was conducted in Morocco, Algeria, and Tunisia. RESULTS: Search of the databases yielded 25 relevant references, including eleven studies in Morocco, five in Algeria, and nine in Tunisia. Overall, 15 studies investigated both BRCA1 and BRCA2 genes, four studies examined the entire coding region of the BRCA1 gene, and six studies in which the analysis was limited to a few BRCA1 and/or BRCA2 exons. Overall, 76 PVs (44 in BRCA1 and32 in BRCA2) were identified in 196 BC and/or OC patients (129 BRCA1 and 67 BRCA2 carriers). Eighteen of the 76 (23.7%) PVs [10/44 (22.7%) in BRCA1 and 8/32 (25%) in BRCA2] were reported for the first time and considered to be novel PVs. Among those identified as unlikely to be of North African origin, the BRCA1 c.68_69del and BRCA1 c.5266dupC Jewish founder alleles and PVs that have been reported as recurrent/founder variants in European populations (ex: BRCA1 c.181T>G, BRCA1 c1016dupA). The most well characterized PVs are four in BRCA1 gene [c.211dupA (14.7%), c.798_799detTT (14%), c.5266dup (8.5%), c.5309G>T (7.8%), c.3279delC (4.7%)] and one in BRCA2 [c.1310_1313detAAGA (38.9%)]. The c.211dupA and c.5309G>T PVs were identified as specific founder variants in Tunisia and Morocco, accounting for 35.2% (19/54) and 20.4% (10/49) of total established BRCA1 PVs, respectively. c.798_799delTT variant was identified in 14% (18/129) of all BRCA1 North African carriers, suggesting a founder allele. A broad spectrum of recurrent variants including BRCA1 3279delC, BRCA1 c.5266dup and BRCA2 c.1310_1313detAAGA was detected in 42 patients. BRCA1 founder variants explain around 36.4% (47/129) of BC and outnumber BRCA2 founder variants by a ratio of ≈3:1. CONCLUSIONS: Testing BC and/or OC patients for the panel of specific and recurrent/founder PVs might be the most cost-effective molecular diagnosis strategy.

Comparative Investigation of Chemical Constituents of Kernels, Leaves, Husk, and Bark of Juglans regia L., Using HPLC-DAD-ESI-MS/MS Analysis and Evaluation of Their Antioxidant, Antidiabetic, and Anti-Inflammatory Activities.

Leaves, husk, kernels, and bark methanolic extracts of Juglans regia L. were tested for their in vitro antidiabetic, anti-inflammatory, and antioxidant activities. For these purposes, α-amylase and α-glucosidase were used as the main enzymes to evaluate antidiabetic activities. Moreover, lipoxidase and tyrosinase activities were tested to estimate anti-inflammatory properties. Antioxidant properties of Juglans regia L., extracts were determined using three different assays. Leaves extract has an important radical scavenging activity and a-amylase inhibition. Similarly, husk extracts showed high total phenolic content (306.36 ± 4.74 mg gallic acid equivalent/g dry extract) with an important α-amylase inhibition (IC50 = 75.42 ± 0.99 µg/mL). Kernels exhibit significant tyrosinase (IC50 = 51.38 ± 0.81 µg/mL) correlated with antioxidant activities (p < 0.05). Husk and bark extracts also showed strong anti-lipoxidase activities with IC50 equal to 29.48 ± 0.28 and 28.58 ± 0.35 µg/mL, respectively. HPLC-DAD-ESI-MS/MS analysis highlights the phenolic profile of methanolic extracts of Juglans regia L. plant parts. The identified polyphenols were known for their antioxidant, antidiabetic (dicaffeoyl-quinic acid glycoside in kernels), and anti-inflammatory (3,4-dihydroxybenzoic acid in leaves) activities. Further investigations are needed to determine molecular mechanisms involved in these effects as well as to study the properties of the main identified compounds.

How have sheep breeds differentiated from each other in Morocco? Genetic structure and geographical distribution patterns.

BACKGROUND: Based on the relatively homogeneous origin of the sheep breeds in Morocco that originate mainly from Iberia, it is highly relevant to address the question of how these very diverse sheep populations differentiated from each other. The Mountains of the High Atlas and Middle Atlas are expected to constitute North-South and West-East geographical barriers, respectively, which could have shaped the history of the differentiation of sheep breeds. The aim of this study was to test this hypothesis by considering the genetic structure and the spatial distribution of five major breeds (Sardi, Timahdite, Beni Guil, Boujaad and D'man) and one minor breed (Blanche de Montagne), by analysing the mtDNA control region, using 30 individuals per breed. RESULTS: Phylogenetic and network analyses did not indicate any clear separation among the studied breeds and discriminant component principal analysis showed some overlap between them, which indicates a common genetic background. The calculated pairwise FST values and Nei's genetic distances revealed that most breeds showed a moderate genetic differentiation. The lowest and highest degrees of differentiation were retrieved in the Beni Guil and Boujaad breeds, respectively. Analysis of molecular variance (AMOVA) indicated that more than 95% of the genetic diversity occurs within individuals, while between- and within-population variabilities represent only 1.332% and 2.881%, respectively. Isolation-by-distance, spatial Principal Component Analysis (sPCA), and spatial AMOVA analyses evidenced clear examples of geographical structuration among the breeds, both between and within breeds. However, several enigmatic relationships remain, which suggest the occurrence of complex events leading to breed differentiation. CONCLUSIONS: The approaches used here resulted in a convergent view on the hypothetic events that could have led to the progressive differentiation between the Moroccan breeds. The major split seems to be linked to the West-East barrier of the Middle Atlas, whereas the influence of the High Atlas is less obvious and incompletely resolved. The study of additional breeds that have settled near the High Atlas should clarify the relationships between the breeds of the West part of the country, in spite of their small population size.

Comparison of three statistical approaches for feature selection for fine-scale genetic population assignment in four pig breeds.

BACKGROUND: Assigning animals to their corresponding breeds through breed informative single-nucleotide polymorphisms (SNPs) is required in many fields. For instance, it is used in the traceability and the authentication of meat and other livestock products. SNPs' information for several pork breeds are now accessible thanks to the availability of dense SNP chips. These SNP chips cover a large number of molecular markers distributed across the entire genome. To identify the pork breed from a sample of industrial meat, one must analyze a large panel of genetic markers depending on the SNP chip used. The analysis of such large datasets requires intensive work. This leads to the idea of creating less dense chips of breed informative markers based on a reduced number of SNPs. Therefore, the analysis of the data emanating from the genotyping of these reduced chips will require less time and effort. AIM: The objective of this study is to find the most informative SNPs for the discrimination between four pig breeds, namely Duroc, Landrace, Large White, and Pietrain. METHOD: The Illumina Porcine 60 k SNP chip was used to genotype SNPs distributed all over the individuals' genomes. Firstly, we used three different statistical approaches for feature selection: (i) principal component analysis (PCA), (ii) least absolute shrinkage and selection operator (LASSO), and (iii) random forest (RF). These three approaches identified three sets of SNPs; each set corresponds to one approach. Then, we combined the results of the three methods by setting up a final panel containing the SNPs which appear on the three sets altogether. RESULTS: Separately, each method resulted in a panel with the corresponding most discriminating SNPs. The PCA, the LASSO, and the random forest with Boruta algorithm highlighted 28,816, 50, and 286 SNPs, respectively. The number of SNPs selected by PCA is high compared to Boruta and LASSO because PCA chooses the variables while preserving as much information about the data as possible. The only downside of LASSO regression is that among a group of correlated variables, LASSO tends to select only one variable and ignore the others regardless of their importance. Contrarily to LASSO, the Boruta algorithm considers the interdependence between SNPs and selects informative variables even if they are correlated and have the same effect. The three panels shared 23 SNPs; the distribution of the individuals according to these SNPs showed a grouping of individuals of each breed in well-defined clusters without any overlapping. CONCLUSIONS: The biological pathways represented by 23 breed informative SNPs resulted by the combination of PCA, LASSO, and Boruta should be explored in further analysis. The results provided by our study are promising for further applications of this method in other livestock animals.

Morphological diversity of female camel (Camelus dromedarius) populations in Morocco.

Fourteen body measurements of 132 adult female camels belonging to three populations Guerzni (60), Khouari (28), and Marmouri (44) reared in 38 herds of 8 provinces of Southern Morocco were studied to identify homogeneous groups according to their conformation. The measurements were chest girth (CG), hump girth (HG), height at withers (HW), body length (BL), fore limb length (FLL), chest width (CW), chest depth (CD), fore hoof circumference (FHC), head length (HL), distance between eyes (DE), ear length (EL), neck length (NL), neck circumference (NC), and tail length (TL). The three populations were compared according to their mean body measurements and through multivariate analyses. The results revealed that only HG, HW, BL, and FLL were significantly influenced by the population. Moreover, the MANOVA showed that Guerzni and Marmouri populations were significantly different, whereas Khouari was not significantly different either from Guerzni or Marmouri populations. Discriminant analysis showed that out of 14 variables, BL and FLL were the most discriminant and resulted into two significant canonical variables (CAN1 and CNA2). Khouari population could be best discriminated from Guerzni and Marmouri by CAN1, and Guerzni could be best distinguished from Marmouri by CAN2. The discriminant analysis revealed that 46.7%, 60.7%, and 40.9% of Guerzni, Khouari, and Marmouri animals, respectively, were correctly classified in their original population. The clustering of the three populations highlighted two Moroccan camel groups: Guerzni and Marmouri in the first group and Khouari in the second one.

Analyses of pig genomes provide insight into porcine demography and evolution.

For 10,000 years pigs and humans have shared a close and complex relationship. From domestication to modern breeding practices, humans have shaped the genomes of domestic pigs. Here we present the assembly and analysis of the genome sequence of a female domestic Duroc pig (Sus scrofa) and a comparison with the genomes of wild and domestic pigs from Europe and Asia. Wild pigs emerged in South East Asia and subsequently spread across Eurasia. Our results reveal a deep phylogenetic split between European and Asian wild boars ∼1 million years ago, and a selective sweep analysis indicates selection on genes involved in RNA processing and regulation. Genes associated with immune response and olfaction exhibit fast evolution. Pigs have the largest repertoire of functional olfactory receptor genes, reflecting the importance of smell in this scavenging animal. The pig genome sequence provides an important resource for further improvements of this important livestock species, and our identification of many putative disease-causing variants extends the potential of the pig as a biomedical model.

Isolation of Endoplasmic Reticulum Fractions from Mammary Epithelial Tissue.

In the mammary glands of lactating animals, the mammary epithelial cells that surround the lumen of the acini produce and secrete copious amounts of milk. Functional differentiation of these mammary epithelial cells depends on the development of high-efficiency secretory pathways, notably for protein and lipid secretion. Protein secretion is a fundamental process common to all animal cells that involves a subset of cellular organelles, including the endoplasmic reticulum and the Golgi apparatus. In contrast, en masse secretion of triglycerides and cholesterol esters in the form of milk fat globules is a unique feature of the mammary epithelial cell. Cytoplasmic lipid droplets, the intracellular precursors of milk fat globules, originate from the endoplasmic reticulum, as do most milk-specific proteins. This organelle is therefore pivotal in the biogenesis of milk components. Fractionation of the cell into its subcellular parts is an approach that has proven very powerful for understanding organelle function and for studying the specific role of an organelle in a given cell activity. Here we describe a method for the purification of both smooth and rough microsomes, the membrane-bound endoplasmic reticulum fragments that form from endoplasmic reticulum domains when cells are broken up, from mammary gland tissue at lactation.

Temporal ß-diversity decomposition: Insights into fish biodiversity dynamics in the Moroccan South Atlantic.

Understanding the various aspects of temporal ß-diversity and their relationships can profoundly enhance the knowledge of the intricate dynamics of biodiversity over temporal scales. In this study, we examined extensive data on fish in the Moroccan South Atlantic, to quantify taxonomic and functional temporal ß-diversity over three five-year periods, determine the relative contributions of turnover and nestedness to each facet, and elucidate the relationship between taxonomic and functional temporal ß-diversity including their components using temporal and spatial comparisons. Our findings revealed a complex relationship between taxonomic and functional temporal ß-diversity, with decoupled variation often observed. Furthermore, the predominant component of functional temporal ß-diversity was functional nestedness, while species turnover had a greater impact on taxonomic temporal ß-diversity. A noteworthy observation was the significant fluctuation in the turnover and nestedness components, despite consistent temporal ß-diversity. These insights underscore the pivotal role of temporal ß-diversity decomposition and advocate for the integration of functional aspects in temporal biodiversity research to provide additional key indicators for biodiversity sustainable management.

Effect of C-phycocyanin purified from Spirulina platensis on cooled ram semen quality and in vivo fertility.

This research sought to purify C-phycocyanin (C-PC) from Spirulina platensis and investigate its potential in enhancing the quality parameters and in vivo fertility of ram semen subjected to cooled storage at 5 °C, when using a skim milk (SM) based semen extender. The purification process of C-PC involved cold maceration, pre-purification using chitosan and activated charcoal, followed by purification through aqueous two-phase extraction (ATPE) and ion-exchange chromatography. Afterward, fifty ejaculates were collected from 4 fertile Boujaâd rams and extended using the SM extender at 37 °C, enriched with 0 µg/mL (control), 1.2 µg/mL, 2.4 µg/mL, 3.6 µg/mL, or 4.8 µg/mL of C-PC. The diluted semen was subsequently cooled to 5 °C using a controlled cooling process, with a gradual cooling rate of approximately 0.5 °C per minute, and its quality parameters were evaluated after 0, 4, 8, and 24 h of cooling storage. Then, its fertilization ability after 4 h of cooling storage was evaluated using artificial insemination. The adopted purification process yielded a grade analytical purity of 4.06. Additionally, semen extended in SM with a 2.4 µg/mL C-PC supplement displayed significant (P < 0.0001) enhancement in total motility, progressive motility, curvilinear velocity, straight-line velocity, average path velocity, viability and lipid peroxidation of ram semen at 0, 4, 8, and 24 h of cooling storage. These improvements were observed in direct comparison to both the control group and the other C-PC concentrations. Regarding fertility rates, semen extended in SM with a 2.4 µg/mL C-PC recorded a 76 % rate, a notable increment from the 63 % observed in ewes inseminated by semen extended in SM alone, although the difference was not statistically significant (p > 0.05). These findings underscore the promising potential of C-PC as a natural supplement for enhancing semen quality, warranting further investigations.

Genetic diversity in Leishmania infantum and Leishmania tropica isolates from human and canine hosts in northern Morocco.

This study investigated nine provinces in northern Morocco and collected 275 skin scraping, 22 bone marrow aspirates, and 89 fine needle aspirations from suspected cutaneous leishmaniasis (CL) and visceral leishmaniasis (VL) patients and potentially infected dogs. Molecular analysis using ITS1 RFLP PCR and RT-PCR revealed a higher prevalence of L. infantum (66.18 %; χ2 = 28.804; df = 1; P-value = 8.01e-08) than L. tropica in skin scraping, with L. infantum being the sole causative agent for both VL and canine leishmaniasis. L. infantum was predominantly found in most provinces, while L. tropica was relatively more dominant in Taza Province. Discriminant Analysis of Principal Components (DAPC) revealed distinct clustering between L. tropica and the other three species. However, no small subset of SNPs could clearly differentiate between Infantum_CL, Infantum_VL, and CanL, as they likely share a significant genetic background. The high rate of L. infantum could be attributed to the abundance of sand fly species transmitting VL. In Taza Province, Phlebotomus sergenti, responsible for anthroponotic CL, is the most abundant species. DNA sequencing demonstrated sequence heterogeneity in L. infantum (variants 1-9) and L. tropica (variants 1-7). Phylogenetic analysis showed a distinct separation between L. tropica and L. infantum strains, with an overlap among L. infantum strains isolated from cutaneous, visceral, and canine cases, and dogs serving as the central population for L. infantum.

Pig immune response to general stimulus and to porcine reproductive and respiratory syndrome virus infection: a meta-analysis approach.

BACKGROUND: The availability of gene expression data that corresponds to pig immune response challenges provides compelling material for the understanding of the host immune system. Meta-analysis offers the opportunity to confirm and expand our knowledge by combining and studying at one time a vast set of independent studies creating large datasets with increased statistical power. In this study, we performed two meta-analyses of porcine transcriptomic data: i) scrutinized the global immune response to different challenges, and ii) determined the specific response to Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) infection. To gain an in-depth knowledge of the pig response to PRRSV infection, we used an original approach comparing and eliminating the common genes from both meta-analyses in order to identify genes and pathways specifically involved in the PRRSV immune response. The software Pointillist was used to cope with the highly disparate data, circumventing the biases generated by the specific responses linked to single studies. Next, we used the Ingenuity Pathways Analysis (IPA) software to survey the canonical pathways, biological functions and transcription factors found to be significantly involved in the pig immune response. We used 779 chips corresponding to 29 datasets for the pig global immune response and 279 chips obtained from 6 datasets for the pig response to PRRSV infection, respectively. RESULTS: The pig global immune response analysis showed interconnected canonical pathways involved in the regulation of translation and mitochondrial energy metabolism. Biological functions revealed in this meta-analysis were centred around translation regulation, which included protein synthesis, RNA-post transcriptional gene expression and cellular growth and proliferation. Furthermore, the oxidative phosphorylation and mitochondria dysfunctions, associated with stress signalling, were highly regulated. Transcription factors such as MYCN, MYC and NFE2L2 were found in this analysis to be potentially involved in the regulation of the immune response. The host specific response to PRRSV infection engendered the activation of well-defined canonical pathways in response to pathogen challenge such as TREM1, toll-like receptor and hyper-cytokinemia/ hyper-chemokinemia signalling. Furthermore, this analysis brought forth the central role of the crosstalk between innate and adaptive immune response and the regulation of anti-inflammatory response. The most significant transcription factor potentially involved in this analysis was HMGB1, which is required for the innate recognition of viral nucleic acids. Other transcription factors like interferon regulatory factors IRF1, IRF3, IRF5 and IRF8 were also involved in the pig specific response to PRRSV infection. CONCLUSIONS: This work reveals key genes, canonical pathways and biological functions involved in the pig global immune response to diverse challenges, including PRRSV infection. The powerful statistical approach led us to consolidate previous findings as well as to gain new insights into the pig immune response either to common stimuli or specifically to PRRSV infection.

Structural and functional annotation of the porcine immunome.

BACKGROUND: The domestic pig is known as an excellent model for human immunology and the two species share many pathogens. Susceptibility to infectious disease is one of the major constraints on swine performance, yet the structure and function of genes comprising the pig immunome are not well-characterized. The completion of the pig genome provides the opportunity to annotate the pig immunome, and compare and contrast pig and human immune systems. RESULTS: The Immune Response Annotation Group (IRAG) used computational curation and manual annotation of the swine genome assembly 10.2 (Sscrofa10.2) to refine the currently available automated annotation of 1,369 immunity-related genes through sequence-based comparison to genes in other species. Within these genes, we annotated 3,472 transcripts. Annotation provided evidence for gene expansions in several immune response families, and identified artiodactyl-specific expansions in the cathelicidin and type 1 Interferon families. We found gene duplications for 18 genes, including 13 immune response genes and five non-immune response genes discovered in the annotation process. Manual annotation provided evidence for many new alternative splice variants and 8 gene duplications. Over 1,100 transcripts without porcine sequence evidence were detected using cross-species annotation. We used a functional approach to discover and accurately annotate porcine immune response genes. A co-expression clustering analysis of transcriptomic data from selected experimental infections or immune stimulations of blood, macrophages or lymph nodes identified a large cluster of genes that exhibited a correlated positive response upon infection across multiple pathogens or immune stimuli. Interestingly, this gene cluster (cluster 4) is enriched for known general human immune response genes, yet contains many un-annotated porcine genes. A phylogenetic analysis of the encoded proteins of cluster 4 genes showed that 15% exhibited an accelerated evolution as compared to 4.1% across the entire genome. CONCLUSIONS: This extensive annotation dramatically extends the genome-based knowledge of the molecular genetics and structure of a major portion of the porcine immunome. Our complementary functional approach using co-expression during immune response has provided new putative immune response annotation for over 500 porcine genes. Our phylogenetic analysis of this core immunome cluster confirms rapid evolutionary change in this set of genes, and that, as in other species, such genes are important components of the pig's adaptation to pathogen challenge over evolutionary time. These comprehensive and integrated analyses increase the value of the porcine genome sequence and provide important tools for global analyses and data-mining of the porcine immune response.

Impact of genetic variation and geographic distribution of porcine reproductive and respiratory syndrome virus on infectivity and pig growth.

BACKGROUND: The porcine reproductive and respiratory syndrome (PRRS) is a devastating disease for the pig industry. In this study, we analysed the genetic variability of PRRS virus (PRRSV) as well as the relationship between the genetic variability, the geographical and temporal distribution of the PRRSV strains. Moreover, we investigated the association between the glycosylation patterns in PRRSV sequences and pigs growth. RESULTS: The data highlight that PRRSV strains evolve rapidly on individual farms, and temporal evolution of PRRSV is an important factor of genetic variability. Analysis of glycosylation sites in the glycoprotein 5 (GP5) ectodomain revealed that PRRSV isolates had seven combinations of putative N-linked glycosylation sites of which the N37/46/53 sites was found in 79% of the sequences. No significant relationship was found between the genetic variation of the PRRSV strains and the geographic distance. A significant relationship was found between the genetic variation and time of sampling when farm was considered as a factor in the analysis. Furthermore, the commercial semen from artificial insemination centres was not a source of PRRS transmission.The PRRSV having the glycosylation site at position N46 (N46+) were observed to have higher burden on pigs and accordingly the corresponding infected pigs had lower average daily gain (ADG) compared with those infected with PRRSV lacking the glycosylation at N46 (N46-) position site. This study showed that the number of piglets by litter infected by PRRSV was lower for the Landrace breed than for the other studied breeds (Large White, Duroc and Pietrain). CONCLUSIONS: The PRRSV genetic variability which is determined by a local and temporal evolution at the farm level could be considered in a perspective of prevention. Moreover, the association between the PRRSV glycosylation patterns and its virulence could be of interest for vaccine development. The differences of resistance to PRRSV infections among pig breeds might open new horizons for the genetic selection of robustness against PRRSV infection.

A review of machine learning models applied to genomic prediction in animal breeding.

The advent of modern genotyping technologies has revolutionized genomic selection in animal breeding. Large marker datasets have shown several drawbacks for traditional genomic prediction methods in terms of flexibility, accuracy, and computational power. Recently, the application of machine learning models in animal breeding has gained a lot of interest due to their tremendous flexibility and their ability to capture patterns in large noisy datasets. Here, we present a general overview of a handful of machine learning algorithms and their application in genomic prediction to provide a meta-picture of their performance in genomic estimated breeding values estimation, genotype imputation, and feature selection. Finally, we discuss a potential adoption of machine learning models in genomic prediction in developing countries. The results of the reviewed studies showed that machine learning models have indeed performed well in fitting large noisy data sets and modeling minor nonadditive effects in some of the studies. However, sometimes conventional methods outperformed machine learning models, which confirms that there's no universal method for genomic prediction. In summary, machine learning models have great potential for extracting patterns from single nucleotide polymorphism datasets. Nonetheless, the level of their adoption in animal breeding is still low due to data limitations, complex genetic interactions, a lack of standardization and reproducibility, and the lack of interpretability of machine learning models when trained with biological data. Consequently, there is no remarkable outperformance of machine learning methods compared to traditional methods in genomic prediction. Therefore, more research should be conducted to discover new insights that could enhance livestock breeding programs.

Genetic diversity and breed-informative SNPs identification in domestic pig populations using coding SNPs.

Background: The use of breed-informative genetic markers, specifically coding Single Nucleotide Polymorphisms (SNPs), is crucial for breed traceability, authentication of meat and dairy products, and the preservation and improvement of pig breeds. By identifying breed informative markers, we aimed to gain insights into the genetic mechanisms that influence production traits, enabling informed decisions in animal management and promoting sustainable pig production to meet the growing demand for animal products. Methods: Our dataset consists of 300 coding SNPs genotyped from three Italian commercial pig populations: Landrace, Yorkshire, and Duroc. Firstly, we analyzed the genetic diversity among the populations. Then, we applied a discriminant analysis of principal components to identify the most informative SNPs for discriminating between these populations. Lastly, we conducted a functional enrichment analysis to identify the most enriched pathways related to the genetic variation observed in the pig populations. Results: The alpha diversity indexes revealed a high genetic diversity within the three breeds. The higher proportion of observed heterozygosity than expected revealed an excess of heterozygotes in the populations that was supported by negative values of the fixation index (FIS) and deviations from the Hardy-Weinberg equilibrium. The Euclidean distance, the pairwise FST, and the pairwise Nei's GST genetic distances revealed that Yorkshire and Landrace breeds are genetically the closest, with distance values of 2.242, 0.029, and 0.033, respectively. Conversely, Landrace and Duroc breeds showed the highest genetic divergence, with distance values of 2.815, 0.048, and 0.052, respectively. We identified 28 significant SNPs that are related to phenotypic traits and these SNPs were able to differentiate between the pig breeds with high accuracy. The Functional Enrichment Analysis of the informative SNPs highlighted biological functions related to DNA packaging, chromatin integrity, and the preparation of DNA into higher-order structures. Conclusion: Our study sheds light on the genetic underpinnings of phenotypic variation among three Italian pig breeds, offering potential insights into the mechanisms driving breed differentiation. By prioritizing breed-specific coding SNPs, our approach enables a more focused analysis of specific genomic regions relevant to the research question compared to analyzing the entire genome.

Genetic Diversity and Maternal Phylogenetic Relationships among Populations and Strains of Arabian Show Horses.

Genetic diversity and phylogenetic relationships within the Arabian show horse populations are of particular interest to breeders worldwide. Using the complete mitochondrial DNA D-loop sequence (916 pb), this study aimed (i) to understand the genetic relationship between three populations, the Desert-Bred (DB), a subset of the Kingdom of Saudi Arabia (KSA), United Arab Emirates (UAE) and Bahrain (BAH), the Straight Egyptian (EG) and the Polish bloodline (PL), and (ii) to assess the accuracy of the traditional strain classification system based on maternal lines, as stated by the Bedouin culture. To that end, we collected 211 hair samples from stud farms renowned for breeding Arabian show horses from Nejd KSA, Bahrain, Egypt, Qatar, Morocco, UAE, and Poland. The phylogenetic and network analyses of the whole mitochondrial DNA D-loop sequence highlighted a great genetic diversity among the Arabian horse populations, in which about 75% of variance was assigned to populations and 25% to strains. The discriminant analysis of principal components illustrated a relative distinction between those populations. A clear subdivision between traditional strains was found in PL, in contrast to the situation of DB and EG populations. However, several Polish horse individuals could not be traced back to the Bedouin tribes by historical documentation and were shown to differ genetically from other studied Bedouin strains, hence motivating extended investigations.

Meta-analysis of Arabidopsis thaliana microarray data in relation to heat stress response.

Introduction: Increasing global warming has made heat stress a serious threat to crop productivity and global food security in recent years. One of the most promising solutions to address this issue is developing heat-stress-tolerant plants. Hence, a thorough understanding of heat stress response mechanisms, particularly molecular ones, is crucial. Methods: Although numerous studies have used microarray expression profiling technology to explore this area, these experiments often face limitations, leading to inconsistent results. To overcome these limitations, a random effects meta-analysis was employed using advanced statistical methods. A meta-analysis of 16 microarray datasets related to heat stress response in Arabidopsis thaliana was conducted. Results: The analysis revealed 1,972 significant differentially expressed genes between control and heat-stressed plants (826 over-expressed and 1,146 down-expressed), including 128 differentially expressed transcription factors from different families. The most significantly enriched biological processes, molecular functions, and KEGG pathways for over-expressed genes included heat response, mRNA splicing via spliceosome pathways, unfolded protein binding, and heat shock protein binding. Conversely, for down-expressed genes, the most significantly enriched categories included cell wall organization or biogenesis, protein phosphorylation, transmembrane transporter activity, ion transmembrane transporter, biosynthesis of secondary metabolites, and metabolic pathways. Discussion: Through our comprehensive meta-analysis of heat stress transcriptomics, we have identified pivotal genes integral to the heat stress response, offering profound insights into the molecular mechanisms by which plants counteract such stressors. Our findings elucidate that heat stress influences gene expression both at the transcriptional phase and post-transcriptionally, thereby substantially augmenting our comprehension of plant adaptive strategies to heat stress.