Protein hydroxylation is an emerging posttranslational modification involved in both normal cellular processes and a growing number of pathological states, including several cancers. Protein hydroxylation is mediated by members of the hydroxylase family of enzymes, which catalyze the conversion of an alkyne group at select lysine or proline residues on their target substrates to a hydroxyl. Traditionally, hydroxylation has been identified using expensive and time-consuming experimental methods, such as tandem mass spectrometry. Therefore, to facilitate identification of putative hydroxylation sites and to complement existing experimental approaches, computational methods designed to predict the hydroxylation sites in protein sequences have recently been developed. Building on these efforts, we have developed a new method, termed RF-hydroxysite, that uses random forest to identify putative hydroxylysine and hydroxyproline residues in proteins using only the primary amino acid sequence as input. RF-Hydroxysite integrates features previously shown to contribute to hydroxylation site prediction with several new features that we found to augment the performance remarkably. These include features that capture physicochemical, structural, sequence-order and evolutionary information from the protein sequences. The features used in the final model were selected based on their contribution to the prediction. Physicochemical information was found to contribute the most to the model. The present study also sheds light on the contribution of evolutionary, sequence order, and protein disordered region information to hydroxylation site prediction.

The Nuclear Receptor (NR) superfamily plays an important role in key biological, developmental, and physiological processes. Developing a method for the classification of NR proteins is an important step towards understanding the structure and functions of the newly discovered NR protein. The recent studies on NR classification are either unable to achieve optimum accuracy or are not designed for all the known NR subfamilies. In this study, we developed RF-NR, which is a Random Forest based approach for improved classification of nuclear receptors. The RF-NR can predict whether a query protein sequence belongs to one of the eight NR subfamilies or it is a non-NR sequence. The RF-NR uses spectrum-like features namely: Amino Acid Composition, Di-peptide Composition, and Tripeptide Composition. Benchmarking on two independent datasets with varying sequence redundancy reduction criteria, the RF-NR achieves better (or comparable) accuracy than other existing methods. The added advantage of our approach is that we can also obtain biological insights about the important features that are required to classify NR subfamilies.

Protein phosphorylation is one of the most widespread regulatory mechanisms in eukaryotes. Over the past decade, phosphorylation site prediction has emerged as an important problem in the field of bioinformatics. Here, we report a new method, termed Random Forest-based Phosphosite predictor 2.0 (RF-Phos 2.0), to predict phosphorylation sites given only the primary amino acid sequence of a protein as input. RF-Phos 2.0, which uses random forest with sequence and structural features, is able to identify putative sites of phosphorylation across many protein families. In side-by-side comparisons based on 10-fold cross validation and an independent dataset, RF-Phos 2.0 compares favorably to other popular mammalian phosphosite prediction methods, such as PhosphoSVM, GPS2.1, and Musite.

Background: The beta-Lactamase (BL) enzyme family is an important class of enzymes that plays a key role in bacterial resistance to antibiotics. As the newly identified number of BL enzymes is increasing daily, it is imperative to develop a computational tool to classify the newly identified BL enzymes into one of its classes. There are two types of classification of BL enzymes: Molecular Classification and Functional Classification. Existing computational methods only address Molecular Classification and the performance of these existing methods is unsatisfactory. Results: We addressed the unsatisfactory performance of the existing methods by implementing a Deep Learning approach called Convolutional Neural Network (CNN). We developed CNN-BLPred, an approach for the classification of BL proteins. The CNN-BLPred uses Gradient Boosted Feature Selection (GBFS) in order to select the ideal feature set for each BL classification. Based on the rigorous benchmarking of CCN-BLPred using both leave-one-out cross-validation and independent test sets, CCN-BLPred performed better than the other existing algorithms. Compared with other architectures of CNN, Recurrent Neural Network, and Random Forest, the simple CNN architecture with only one convolutional layer performs the best. After feature extraction, we were able to remove ~95% of the 10,912 features using Gradient Boosted Trees. During 10-fold cross validation, we increased the accuracy of the classic BL predictions by 7%. We also increased the accuracy of Class A, Class B, Class C, and Class D performance by an average of 25.64%. The independent test results followed a similar trend. Conclusions: We implemented a deep learning algorithm known as Convolutional Neural Network (CNN) to develop a classifier for BL classification. Combined with feature selection on an exhaustive feature set and using balancing method such as Random Oversampling (ROS), Random Undersampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE), CNN-BLPred performs significantly better than existing algorithms for BL classification.

Phosphorylation, which is mediated by protein kinases and opposed by protein phosphatases, is an important post-translational modification that regulates many cellular processes, including cellular metabolism, cell migration, and cell division. Due to its essential role in cellular physiology, a great deal of attention has been devoted to identifying sites of phosphorylation on cellular proteins and understanding how modification of these sites affects their cellular functions. This has led to the development of several computational methods designed to predict sites of phosphorylation based on a protein’s primary amino acid sequence. In contrast, much less attention has been paid to dephosphorylation and its role in regulating the phosphorylation status of proteins inside cells. Indeed, to date, dephosphorylation site prediction tools have been restricted to a few tyrosine phosphatases. To fill this knowledge gap, we have employed a transfer learning strategy to develop a deep learning-based model to predict sites that are likely to be dephosphorylated. Based on independent test results, our model, which we termed DTL-DephosSite, achieved efficiency scores for phosphoserine/phosphothreonine residues of 84%, 84% and 0.68 with respect to sensitivity (SN), specificity (SP) and Matthew’s correlation coefficient (MCC). Similarly, DTL-DephosSite exhibited efficiency scores of 75%, 88% and 0.64 for phosphotyrosine residues with respect to SN, SP, and MCC.

Protein phosphorylation is among the most widely used regulatory mechanisms in eukaryotes. In recent years, several phosphorylation site prediction tools have been developed to identify phosphorylation sites in silico. However, there are still ways to improve the performance of these methods. Here, we report the development of a new predictor, termed Canonical Correlation Forest-based Phosphosite (CCF-Phos) predictor, to predict putative phosphorylation sites on a given protein. The CCF-Phos was evaluated using both 10-fold cross-validation and an independent dataset. During these analyses, CCF-Phos compared favorably to other popular mammalian phosphosite prediction methods.

Sericea Lespedeza (SL), is a high-quality, low input forage that suppresses gastro-intestinal parasites in goats. The effect of dietary SL on the expression of genes involved in innate immunity in goats has not been established. The objective of this study was to evaluate the impact of a diet containing SL on the expression of genes involved in innate immunity in goat blood. Blood was collected by jugular venipuncture from goats fed a diet of 75% SL (n = 9) and a control group (n = 7), fed a SL free diet. Blood was used to evaluate expression of (CD-14, TLR-2, TLR-4, IL-10, IL-8, IL-2, INF-r, and TNF-a). Serum was extracted and used for evaluation of the secretion of pro-inflammatory cytokines (TNF-a, IFNr, granulocyte colony stimulating factor (GCSF), granulocyte-macrophage colony-stimulating factor (GMCSF), IL-1a, IL-8, IP-10 and RANTES) using a commercial ELISA kit. The level of gene expression of CD-14, TLR-2, TLR-4, IL-10, IL-8, IL-2, INF-r, and TNF-a was higher in treated animals compared to control. The Sericea Lespedeza diet affected the secretion of pro-inflammatory cytokines by increasing the serum levels of TNF-a, IFNr, GCSF, GMCSF, IL-1a, IP-10 (P < 0.0002), and by decreasing (P < 0.0001) IL-8 and RANTES in blood from goats fed SL. This suggests that dietary tannins modulate gene expression and may affect the goat's innate immune response in blood. Further research is needed to understand and harness the effect of dietary condensed tannins to modulate innate immunity in goats.

The objective of this study was to assess the molecular impact of probiotic administration on genes involved in homeostasis and immunity in goat blood. Following initial screening for infection, one-week post weaning, female SpanishXBoer goats were drenched daily with the recommended doses of FASTtrak microbial pack (Conklin Company Inc., Shakopee, MN) in 10 mL sterile water over an 8-week period. The control group were given sterile water. Blood samples were collected weekly. Total RNA was isolated from blood collected at the beginning of the study (week 0) and at the end of the study (week 8) using Tri-reagent and then reverse-transcribed to cDNA using the Ambion-Retroscript kit. Quantification of genes was performed in the CFX96TM Biorad Real-Time PCR detection system with the addition of the dye SYBR green. The cow Wingless (Wnt) signaling pathway, Human Innate & Adaptive Immune Responses and the Cow Inflammatory Cytokine & Receptors RT² Profiler™ PCR Arrays (QIAGEN, Valencia, CA) were used to profile the expression of 84 genes involved in each pathway. Probiotic treatment had no effect on body weight, body condition, fecal egg count and RNA concentration (p>0.05). Packed cell volume and FAMACHA scores were significantly improved by probiotic administration. Results from RT-PCR showed increased expression of genes in innate and adaptive immune response, cytokine and Wnt pathways in response to probiotics. Probiotics induced the expression of 32 genes involved in innate and adaptive immune response inflammatory cytokines, and 48 genes involved in the Wnt signaling pathway. This study provides evidence for a systematic effect of oral probiotic administration on expression of genes involved in immunity and homeostasis in goat blood.

Probiotic supplements are beneficial for animal health and rumen function; and lipopolysaccharides (LPS) from gram negative bacteria have been associated with inflammatory diseases. In this study the transcriptional profile in whole blood collected from probiotics-treated cows was investigated in response to stimulation with lipopolysaccharides (LPS) in vitro. Microarray experiment was performed between LPS-treated and control samples using the Agilent one-color bovine v2 bovine (v2) 4x44K array slides. Global gene expression analysis identified 13,658 differentially expressed genes (fold change cutoff ≥ 2, P < 0.05), 3816 upregulated genes and 9842 downregulated genes in blood in response to LPS. Treatment with LPS resulted in increased expression of TLR4 (Fold change (FC) = 3.16) and transcription factor NFkB (FC = 5.4) and decreased the expression of genes including TLR1 (FC = − 2.54), TLR3 (FC = − 2.43), TLR10 (FC = − 3.88), NOD2 (FC = − 2.4), NOD1 (FC = − 2.45) and pro-inflammatory cytokine IL1B (− 3.27). The regulation of the genes involved in inflammation signaling pathway suggests that probiotics may stimulate the innate immune response of animal against parasitic and bacterial infections. We have provided a detailed description of the experimental design, microarray experiment and normalization and analysis of data which have been deposited into NCBI Gene Expression Omnibus (GEO): GSE75240.

The effect of tomato on diseases of economic importance continues to be of broad interest. To evaluate these relationships, the effect of crude extracts of Cherokee purple tomato on pro-inflammatory genes and cytokines were evaluated. Neutrophils from three Holstein Fresian cows were treated with Phosphate Buffered Saline (PBS) or E. coli O111-B4 Lipopolysaccharide (LPS). Treated cells were exposed to different concentrations of polyphenol extracts of fresh and heated Cherokee purple. Transcription of Cyclooxygenase-2 (Cox-2) and Tumor Necrosis Factor-alpha (TNF-α) was detected using real time PCR. Secretion of cytokines was evaluated using specific Enzyme-Linked Immunosorbent Assay (ELISA)’s for three pro-inflammatory genes. Transcription of Cox-2 and TNF-α in bovine neutrophils were found. No significant treatment effect of tomato polyphenols was found in the LPS treated neutrophils. TNF-α and Cox-2 expression in bovine neutrophils were modulated by polyphenol treatment. Variable concentration of polyphenol extracts had no effect on Cox-2 transcription. Gene expression analysis of TNF-α and Cox-2 mRNA showed significantly decreased transcription of Cox-2 in neutrophils exposed to polyphenol extracts (p<0.0326). Polyphenol exposure did not have an influence on induction of Cox-2 by LPS. Treatment with polyphenols decreased transcription of TNF-α at the level of 200 ng mL-1 (p<0.05). Fresh or heated polyphenol did not influence transcription of TNF-α. Significant variation was observed among cows in transcription of TNF-α (p<0.05). No significant treatment effect was observed for translation in Cox-2, TNF-α and GCSF exposed to fresh or heated polyphenol extracts. Relative secretion of the antiviral pro-inflammatory cytokine IFNr was increased compared to the control in samples exposed to heated tomato polyphenol extracts (p<0.05). Results showed that tomato polyphenols modulate expression of pro-inflammatory genes in bovine neutrophils and may provide avenues to boost innate immunity. To our knowledge, this is the first study to discuss the role Cherokee purple polyphenol extracts plays in innate immunity.

In this study, galectin gene expression pre- and postpartum was evaluated in blood. St. Croix sheep are a breed of hair sheep that are parasite resistant and prolific and so are of interest for production. Immunosuppression during the periparturient period occurs in every organ in the sheep's body to enable the ewe support fetal growth and prevent abortion. During this time, sheep become susceptible to diseases and parasite infection. They rely on the effectiveness of their immune system to fight off invading parasites and diseases. Galectins are a family of lectins that have been proposed to promote intra- and intercellular communication impacting immunity. Blood was aseptically collected from 6 pregnant ewes into Paxgene tubes 1 wk before and 1 wk after lambing. Body weight, FAMACHA score, and BCS were collected to ensure they were in good health. Total RNA was isolated from the whole blood and converted into cDNA. Real-time PCR was performed using pooled cDNA samples with 8 different galectin primers (Gal 1, 2, 3, 4, 8, 12, 15 and 16) and B-actin primer served as control. All the galectins were expressed pre- and postpartum except for Gal 2, which was absent prepartum but expressed postpartum. Although all were expressed after lambing, Gal 3, 4, and 16 had a significant fold change increase, with Gal 16 being the highest with a fold change of 4. Periparturient changes in galectin expression may be useful indicator of animal health and welfare and should be characterized further.

The effect of exposure of cow blood to non pathogen associated (probiotics) molecular patterns on the subsequent response to pathogen associated molecular patterns (PAMPS) was evaluated using transcriptional profiling. Probiotic supplements are beneficial for animal health and rumen function and represent non pathogen associated molecular patterns. Lipopolysacharides form gram negative bacteria are associated with inflammatory diseases and represent PAMPS. A global gene expression profile in whole blood collected from probiotics-supplemented cow was investigated in response to stimulation with lipopolysaccharide (LPS) in vitro. The recommended dose of FASTtrak microbial pack (Conklin Company, Kansas City, MO, USA) was administered orally in 50 ml of sterile water to Holstein-Friesian cows (n = 10) in mid lactation, for 60 d. Whole blood was collectedly aseptically and treated with 100 ng/ml of LPS and untreated samples served as control. Total RNA was extracted, and samples (0.5ug, RIN > 7) pooled together, were used for the microarray experiment on a bovine (v2) 4 × 44 arrays with 44,000 gene transcripts. A Real-time quantitative PCR was performed to validate the expression of Wnt signaling pathway and innate and adaptive immune response genes using RT-PCR profilers arrays (Qiagen) with 84 test genes each. Global gene expression analysis identified 13,658 differentially expressed genes (fold change cutoff ≥ 2, P < 0.05), 3816 upregulated genes and 9842 downregulated genes. Treatment with LPS resulted in increased expression of TLR4 (Fold change (FC) = 3.16), TLR2 (FC = 2.4), TLR7 (FC = 2.13), WNT5A (FC = 2.68), and transcription factor NF-Kb (FC = 5.4). Genes downregulated in expression included WNT 11 (FC = -2.60), TLR1 (FC = -2.54), TLR3 (FC = -2.43), TLR10 (FC = -3.88), NOD2 (FC = -2.4), NOD1 (FC = -2.45) and pro-inflammatory cytokine IL1B (FC = -3.27). Thus, probiotic supplementation had an effect on the response to LPS exposure with specific effects on Toll-like receptor transcription. Exposure of bovine blood to pathogen associated (LPS) and non pathogen associated (probiotics) patterns resulted in transcriptional activation. Thus, probiotic supplementation may modulate the response to gram negative bacteria.

The objective of this study was to assess the impact of probiotic administration on growth and global gene expression profile in dairy cow. Use of probiotic supplements is a nonchemical approach to promote animal health. Understanding the mechanism of action of probiotics in cows may aid in sustainable dairy production. Lactating Holstein-Friesian cows (n = 10) received daily oral doses (50ml) of a commercial probiotic FASTtrak microbial pack (Conklin Company, Kansas City, MO) (containing Lactobacillus acidophilus, Saccharomyces cerevisiae, Enterococcus faecium, Aspergillus oryza, and Bacillus subtilis) over a 60-d period. Body weight was recorded weekly. Whole blood was collected at the beginning (d 0) and end of the study (d 60). Blood samples were analyzed for total and viable cell count, packed cell volume (PCV), white blood cell differential counts (WBC), and total protein concentration in plasma. Daily supplementation of probiotics had no effect on BW, PCV, and total protein concentration in plasma at the end of the study (P > 0.05). Percentage lymphocyte count increased (P < 0.05), and percentage neutrophil count (P < 0.05) decreased in probiotic-treated animals. Gene expression analysis identified 10,859 differentially expressed genes, 1168 up-regulated and 9691 down-regulated genes respectively following probiotic administration. Pathway analysis identified 87 bovine pathways impacted by probiotic treatment. These pathways included the Toll-like receptor signaling pathway, inflammation response and Wnt signaling pathways. Oral administration of probiotic to dairy cows has a systemic effect on global gene expression, including genes involved in immunity and homeostasis (Wnt). The results of this study show that the utilization of probiotics in animal agriculture impacts genes important to dairy cow health and production. Further definition of the interaction between the pathways involved may aid in the design of the most effective probiotics for optimum dairy production and health.

Infectious and metabolic diseases are the significant hindrance to animal production. Galectins (Gal) are β-galactosidase binding proteins that can modulate inflammation following external and internal secretion from the cell. Galectin 9 is a tandem repeat member of Gal involved in cell activation, pathogen destruction and immunosuppression. Binding of Gal 9 to its receptor T cell immunoglobulin and mucin domain 3 (TIM-3) on immune cells can modulate the inflammatory response. Migration of neutrophils from blood contributes to increased Somatic cell count (SCC) in Mastitis. The possible role of TIM -3 and its ligand Gal 9 in animal health and disease needs to be defined. The objective of this study was to determine the transcription of TIM-3, Gal 9 and secretion of Gal 9 in cow milk. Milk was collected from the udder of 10 clinically healthy Holstein cows. Dairy herd index reports were used to assign 5 cows to high or low SCC groups. Total RNA was isolated from milk somatic cells, pooled and converted to cDNA, for Real-time PCR. Specific primers for cow TIM-3 and LGALS 9 were designed using the NCBI Primer 3 tool. Housekeeping gene β-actin served as an internal control. Whey was extracted from whole milk. Cow Gal 9 specific ELISA kits were used for assessing secretion. Data were analyzed using Proc ANOVA procedure in SAS 9.4. Transcription of TIM-3 was not detected in milk. However, Gal 9 was transcribed and secreted. The level of secretion was 33% higher in LSCC. The observed increase may be important in homeostasis and health thus warrants further study with more cows. Future studies should look at cell surface expression of TIM-3 using more animals.

The objective of this study was to investigate the effect of crude cowpea polyphenol extract (CPE) on the expression of genes involved in the inflammatory response in bovine blood in vitro. Plant-derived polyphenols in animal feeds are being used as alternatives to antibiotics to treat and prevent invading microbes. These compounds have antioxidant and anti-inflammatory properties and are able to modulate immune and inflammation responses. Whole blood collected from lactating Holstein-Friesian cows (n = 10) were incubated with 10 µg of CPE for 60 min at 37°C and 5% CO2.Total RNA was extracted from whole blood after incubation and reverse transcribed to cDNA, and quantitative PCR (qPCR) was performed using the cow inflammatory cytokines and receptor array (Qiagen) with 84 genes. The qPCR data were analyzed using Livak's method to calculate fold change in gene expression between CPE-treated and control cows. Normalization of data was performed with GAPDH as an internal control. Out of the 84 genes tested, 81 were expressed, 13 were upregulated and 68 were downregulated, in response to CPE. Treatment with CPE downregulated the expression of proinflammatory cytokine TNFα (fold change [FC; treatment/control] = −43.39), IL1α (FC = −6.19), ILβ (FC = -3.62), and IL8 (FC = −1.25). Expression of chemokines such as CXCL10, CXCL12, and CXCR2 was not altered by treatment with CPE. Interestingly, expression of IL10RA (FC = 3.39), a receptor for IL10, a well-known anti-inflammatory cytokine, was upregulated in blood incubated with CPE. IL15, a cytokine that regulates T and natural killer cell activation and proliferation, was upregulated (FC = 2.08) by CPE treatment. The study results demonstrate that polyphenols derived from cowpea have an anti-inflammatory effect in cow blood, and target genes modulated by CPE have been identified for further characterization.

The objective of this study was to compare galectin gene (LGALS) expression and secretion in cow, sheep and goat blood. Ruminants succumb to infectious and metabolic diseases. Galectins (Gals) are a family of β –galactoside binding proteins that have a conserved carbohydrate recognition domain increasingly recognized for their role in inflammation. At least 15 Gals are secreted intracellularly and extracellularly. They are involved in cell adhesion, migration, activation, proliferation, apoptosis and modulate pathological processes such as inflammation. Little is currently known about the expression and physiological role of circulating galectins in animals. A comparative study was done to determine the expression and secretion of galectins in blood from adult non-pregnant female sheep, cows and goats (N=3 each). Total RNA was isolated using Tri-reagent (Sigma) and reverse-transcribed to cDNA. Commercially sequenced species specific primers for Galectins were used for real-time PCR. GAPDH and beta-actin served as internal controls. Enzyme-linked Immunosorbent assay was used to detect and determine the concentration of structurally different Galectins, Gal 1(Prototype), Gal 3 (chimera type) and Gal 9 (tandem-repeat type) in plasma. Galectin secretion was analyzed with Proc GLM in SAS 9.4. All Galectins tested (1, 2, 3, 4, 7, 8, 9, 11 and 12) were expressed in cows, goats and sheep blood. Galectins 1, 3 and 9 were secreted in plasma at varying concentrations ranging from pg/mL to ng/mL. The hierarchy was Gal1>Gal 3>Gal 9. Diversity was observed in level of expression and secretion among the three species. Extracellular secretion of Gal was related to structure. This may have implications for Gal function and possible use as preventives diagnostic and therapeutic in ruminants. Studies are underway to further analyze the role of circulating Gal in homeostasis and health.

The aim of this study was to evaluate the expression of galectins in cow blood and to evaluate their modulation in periparturient cows at different stages of lactation. Galectins are multipotent, evolutionarily conserved, carbohydrate- binding proteins that, by crosslinking cell surface glycoconjugates, trigger a cascade of transmembrane signaling events such as cell activation, cytokine secretion, migration, and apoptosis There are 15 galectin protein subtypes that all share the shared characteristic of AA sequences and affinity for β-galactoside sugars. Galectins are known to have an impact on immunomodulation and are involved in uterine immunoregulation during pregnancy. The cows were grouped into 3 lactation periods (first, second, and third lactations). Blood was taken 2 wk close to parturition (close up) and 7 d after parturition(c+7) at Michigan State University dairy farm and shipped in Paxgene tubes for analysis. Total RNA was isolated, reverse transcribed to cDNA, and then used in real-time PCR experiments. With the use of Primer-Blast from the National Center for Biotechnology Information, specific primers for galectins 1, 2, 3, 4, 7, 8, 9, and 12 and β-actin (forward and reverse) were sequenced and used for this project. β-actin was used as internal control. Fold change in transcript abundance was calculated using the Livak method. In first-lactation cows, Galectin 1 was turned off after parturition. Galectin 2 was absent in both close up and c+7 cows. Galectins 3 and 7 were present in both close up and c+7 cows but levels did not change after parturition (fold change < 2). Galectin 4 was present before parturition but absent a week after parturition, Galectin 9 expression increased after parturition (fold change = 2). Galectin 12 was turned off after parturition. In the second-lactation cows, Galectin 1 was turned off after parturition, and Galectins 2, 3, 7, and 9 increased in transcription after parturition (fold change > 2). Galectins 1, 4, 8, and 12 were present before and after parturition but their fold changes were not significant. In third-lactation cows, all 8 galectins except for Galectin 8 were detected in both close ups and c+7 cows. Expression levels for these galectin genes did not change (fold change < 2). All genes tested were expressed in cow blood at varying levels. It is clear from this study that galectin gene expression is affected by stage of lactation and parturition. Further studies are needed to determine the factors that contribute to the different galectin expressions in cow blood during the periparturient period.

Statistical modeling is a branch of advanced statistics and a critical component of many applications in science and business. This book is an attempt to satisfy the need of mathematical statisticians and computational students in linear modeling and ANOVA. This book addresses linear modeling from a computational perspective with an emphasis on the mathematical details and step-by-step calculations using SAS® PROC IML. This book covers correlation analysis, simple and multiple linear regression, polynomial regression, regression with correlated data, model selection, analysis of covariance (ANCOVA), and analysis of variance (ANOVA). The level is suitable for upper level undergraduate and graduate students with knowledge of linear algebra and some programming skills.

Inflammation is a complex but defensive biological response of living tissue to infection that can be triggered by exogenous injury and infectious agents such as bacteria and viruses. Inflammation is part of the innate immune response acting as the first line of defense against pathogens and disease. The inflammatory response acts as a doubleedged sword because if inflammation persists or augments, it may switch from a defensive mechanism to a detrimental process, therefore, modulating the inflammatory response using therapeutic interventions is important for animal health and well being and for longterm food security. Recent studies have reported that plant-derived bioactive compounds are precursors for numerous anti-microbial and anti-parasitic medicines. The antibiotic activities may be attributed to some bioactive compounds such as polyphenols, flavonoids, terpenes, tannins, terpenoids, vitamin C, essential oils, and carotenoids. The ban on the use of synthetic antibiotics and inconsistent efficacy of non-steroidal anti-inflammatory drugs as NSAID have elevated the need for alternative drugs, particularly, those which are derived from plant-based bioactive compounds. Such substances can be used as supplements in animal food to boost the immune system particularly, for domestic ruminants, which are the main source for milk and meat for humans. During the past recent years, plant-based bioactive substances were studied intensively along with their anti-inflammatory and other therapeutic effects, both in-vivo and in-vitro. Modulating the inflammatory response using plant-based bioactive compounds is important for understanding the anti-inflammatory pathways that stimulate the innate immune response to pathogens or injury. The present study is a literature review that focuses on the applications of plant-derived bioactive substances and their effects on the immune responses of goats, sheep, and cows.

The objective of this study was to evaluate the effect of probiotics (FASTtrack microbial pack) on the expression of Toll-like receptors (TLR) in goats. Probiotics are viable microorganisms that have positive effects on growth performance, nutrient synthesis, the microbial ecosystem, absorption, and the reduction in the incidence of intestinal infection and restoration of gut microflora. Following initial screening for infection, 1 wk after weaning, female Spanish × Boer goats were drenched daily with the recommended doses of probiotic (containing Lactobacillus acidophilus, Saccharomyces cerevisae, Enterococcus faecium, Aspergillus oryza, and fructooligosacharide) in 10 mL sterile water over an 8-wk period. The control group was given sterile water. Blood samples were collected weekly. Total RNA was isolated from blood collected at the beginning of the study (wk 0) and at the end of the study (wk 8) using Tri-reagent. Ribonucleic acid integrity number (RIN) was determined with a Bioanalyzer, and samples with RIN > 7 for each treatment were reverse transcribed to cDNA using the Ambion RETROscript kit. The RT2 profiler array was used to determine the expression of 84 genes involved in innate and adaptive immunity. Fold change was calculated using the Livak method with GAPDH as the reference gene for normalization. At wk 0, out of the 84 genes assayed, animals in the control group expressed 25 genes whereas 22 genes were expressed at wk 8. In the treatment group, out of the 84 genes assayed in the treatment group, 41 genes were expressed at wk 0, whereas 67 genes were expressed at wk 8. Probiotics induced the expression of TLR3 and TLR8 and increased the expression of TLR4 (fold change [FC] = 27), TLR6 (FC = 2), TLR7 (FC = 7), and TLR9 (FC = 3). Treatment with probiotics resulted in differential expression of genes related to the TLR signaling pathway. The results from this study will help in the definition of the role of TLR expression in goat blood and the design of therapeutic probiotics and help define the mechanism of action of probiotics.

Circulating neutrophils act against invading bacteria by migrating to infected tissue. They are an essential component of goat immunity to mastitis causing pathogens in milk. Pathogen associated molecular patterns are recognized by expression of Toll like receptors (TLR) on the cell surface. Toll-like receptors 1 to 10 have been identified in goat peripheral blood mononuclear cells and other tissues but not in neutrophils. In light of the significant role of the neutrophil in innate immunity to microbial pathogens and the inflammatory response, the objective of this study was to determine expression of TLR by caprine blood neutrophils and to evaluate their modulation by the probiotic (CorPet from the mushroom Coriolus versicolor Mycology Research Laboratory, San Francisco, CA). Fifteen (15) female Spanish × Boer goats were drenched daily with 10 mL of either a hot or a cold extract of CorPet and a control group of goats received sterile water for 4 wk. Blood was collected at wk 1 and 4. Neutrophils were isolated using differential centrifugation and hypotonic lysis of red blood cells. Isolated neutrophils were used for RNA isolation using Trizol (Sigma-Aldrich, St Louis, MO), RNA with integrity number > 7 determined using a Bioanalyzer (Agilent) was converted to cDNA using the RETROscript kit (Ambion, Grand Island, NY). The human Toll-like Receptor signaling pathway RT2 PCR Array (Qiagen, Valencia, CA) was used to profile the expression of 84 genes involved in TLR-mediated signal transduction and innate immunity. The Livak method was used to calculate the fold change in transcription compared to the control. The house keeping gene GAPDH was used to normalize the data. At the beginning of the experiment 15 to 48 genes were detected across treatment groups. At wk 4, all 84 genes were expressed in the hot treatment group, 8 genes in the control and 9 genes in the cold treatment group. Administration of the hot extract increased the number of TLR expressed from 3 to 10 and decreased from 2 to 1 and 6 to 0 in the cold extract and control groups respectively over the 4-wk period. Goat neutrophils can express all ten TLR. This mushroom based probiotic modulates expression of genes in the TLR signaling pathway. Extract specific effects need further study.

Machine learning algorithms have been widely used in bioinformatics to develop computational tools and the usage is still growing due to the growth of the volume of data and availability of computational resources, and invention of newer machine learning algorithms. The important task in this implementation is to fit models to experimentally pre-classified data and then to use these models to make a prediction about an unclassified instance. Since the advent of whole genome sequencing, protein sequences have been increasingly deposited and classified in databases. The objectives of this dissertation are to develop a computational tool for protein feature extraction and to implement random forest based algorithm to solve various bioinformatics problems. The project is motivated by the gap existing in feature extraction tools and the need for improvement to some current prediction methods. Four different tools are developed; the first one is the Feature Extraction from Protein Sequence tool (FEPS), which is an easy-to-use web-based tool that computes the most common protein features and provides features in different output file formats. The other three tools are RF-NR, RF-Phos, and RF-Hydroxysite. RF-NR predicts the subfamilies of nuclear receptor proteins, which represent a large protein superfamily, while RF-Phos and RF-Hydroxysite predicts the sites of post-translational phosphorylation and hydroxylation respectively in protein sequences. These methods were validated and tested rigorously with both cross validation and independent samples. In comparison with the existing ones, our new bioinformatics tools perform equally well or better compared to the existing tools. These tools are available online at Bioinformatics and Computational Biology Lab’s website at bcb.ncat.edu.

Protein sequence-driven features are numeric vectors extracted from amino acid residues of protein sequences for their ability to capture some information that can be used for knowledge discovery in both supervised and unsupervised machine learning. Extracting features from protein sequences is always a challenge for many researchers, who need features to develop a learning model or for statistical purposes, without dealing with the hassle of mathematical and programming details. We developed FEPS, a web application for protein feature extraction that computes most common sequence-driven features of proteins from a single or multiple fasta-formatted files with multiple protein sequences and outputs user-friendly and ready-to-use feature files. The application uses 48 published feature extraction methods, of which 6 can use any one of the 544 physicochemical properties and 4 can accept user-defined amino acid indices. The total number of features calculated by FEPS is 2765, which is far more than the number of features that can be computed by any other peer application. A simple tutorial and guidelines were provided to walk the user through the different steps without difficulties. The FEPS is available online at http://bcb.ncat.edu/Features/. Index Terms – protein feature extraction, protein descriptors, machine learning.

Gastrointestinal nematodes and other pathogens pose a major problem for goat production by reducing animal performance and welfare. Plants such as Acacia Senegal are useful as dietary sources for natural prophylaxis. Gum Arabica (GA) from A. Sengal has antimicrobial, anti-inflammatory properties that need to be explored in goats. The objective of this study was to investigate the possible immunomodulatory effect of a water extract of GA in goat blood. Clinically healthy Boer and Spanish goats from the NCA&T Small ruminant unit were used. Goats were assigned randomly to two groups of ten (n = 20). Goats of one group were drenched daily with 10 mL of GA (treatment I) extract for 6 weeks. The second (control) group of goats received sterile water (treatment II). Blood was collected from the jugular vein in tubes containing acid-citrate-dextrose anticoagulant. Plasma was separated and the concentration of total protein was determined using Pierce BCA kit (Thermo Scientific Pierce, Rockford, IL). The white blood cell differential count was assessed on Wrights smeared stains. Data were analyzed using PROC GLM in SAS 9.4 (P < 0.05). Treatment with GA modulated total plasma protein concentration and the differential white blood cell counts. Treatment increased total plasma protein concentration and % lymphocytes, it decreased % neutrophils. Immunomodulation by GA may be advantageous in promoting health and wellness in goats. Further studies on the mechanism of action are warranted.

Dietary phytochemicals have both nutritional and health benefits for farm animals. Research on the immunomodulatory effects of phytochemicals may aid in developing novel therapeutic agents and provide insights into the regulation of gene expression. Eugenol (4-allyl-2-methoxyphenyl) is the active ingredient in clove oil that has been studied for its immunomodulatory/anti-inflammatory effects. The objective of this study was to evaluate the effect of eugenol on the expression of genes associated with the cow’s innate and adaptive immune responses. Blood was collected from (n = 3) clinically healthy Holstein-Friesian cows from the North Carolina A&T State University Dairy Unit. One milliliter of whole blood from three cows was treated individually with 10 ng/mL of Eugenol (Sigma-Aldrich St. Louis, MO), or maintained in PBS, incubated at 37ºC for 30 minutes. Total RNA was extracted, reverse transcribed, and real-time PCR was carried out using the RT2 Profiler™ human Innate & Adaptive Immune Responses PCR Array containing 84 genes, as recommended by the manufacturer (Qiagen). The Livak method was used to calculate fold change (FC >2 considered significant). The analysis showed that 25 genes out of 84 genes were affected by treatment with eugenol. Among 25 genes, 19 were upregulated, and 2 genes were downregulated. The highest up-regulated and down-regulated genes following exposure to eugenol was IL23A and Interferon Regulatory Factor 7 (IRF7), respectively. The upregulation of the IL-23A gene expression by exogenous eugenol may be important in the production of pro-inflammatory cytokines and warrants further studies to investigate the mechanism involved. Interferon Regulatory Factor 7 is a critical regulator of type I interferon production and plays an important role in innate immune responses. The observed transcriptional expression of IL23A and IRF7 by eugenol provides an insight into immune modulation in cow blood.

Gum arabica (GA) is a well-known traditional herbal medication from Acacia Senegal. Previous studies have shown that GA has physiological and therapeutic effects on animals. Anemia is a reduction below normal in the number of red blood cells. Infection by parasites, such as Haemonchus contortus, can result in anemia when there is a reduction below normal in the number of red blood cells. The objective of this study was to evaluate the effect of GA on anemia in goats. The packed cell volume (PCV) and FAMACHA© score were used as indicators of anemia. Clinically healthy Boer and Spanish goats (n = 20) from the NCA&T University Farm Small Ruminant Unit were used in the study. Following initial screening for infection, twenty goats (n = 20) were assigned randomly to two groups of ten (n = 20). Goats in the treatment group were drenched daily with 10 mL of GA extract for 6 weeks. A control group of ten age-matched goats received sterile water. The FAMACHA© score was evaluated and recorded weekly. Blood samples collected at the time of evaluation were assessed for packed cell volume weekly. Data were analyzed by using PROC GLM in SAS 9.4(PM < 0.05). Treatment with GA increased PCV and decreased FAMACHA© scores when compared to the control group. Thus, water extracts of GA may aid in alleviating anemia, caused by parasites or resulting from other causes, to enhance goat health and production. The effect on parasites and overall health is being evaluated.

Protein phosphorylation is among the most widely used regulatory mechanisms in eukaryotes. In recent years, several phosphorylation site prediction tools have been developed to identify phosphorylation sites in silico. However, there are still ways to improve the performance of these methods. Here, we report the development of a new predictor, termed Canonical Correlation Forest-based Phosphosite (CCF-Phos) predictor, to predict putative phosphorylation sites on a given protein. The CCF-Phos was evaluated using both 10-fold cross-validation and an independent dataset. During these analyses, CCF-Phos compared favorably to other popular mammalian phosphosite prediction methods.

Dietary supplements are being used to benefit animal production and health. Studies in twins offer an opportunity to control genetic variability to better assess the impact of supplements. The objectives of this study were to evaluate the effect of the dietary supplement OMEGA-3*6*9 Kid & Lamb Plus (Durvet, Blue Springs, MO) on body weight of twins in sheep and goats. Twins from three Spanish/Boer goat nannies and three St Croix Sheep ewes from the North Carolina Agricultural and Technical State University farm were used. All adults and fifty percent of the progeny received this high calorie liquid dietary supplement at the recommended dose for 8 wk. The supplement was administered via oral drench at a dose of 5cc/day. Body weight was determined bi-weekly for 8 wk. All data were analyzed using SAS proc glm repeated measure procedure (SAS, Cary, NC). Body weight increased significantly over time (P < 0.05). However no treatment effect (p > 0.05) was observed and this may be due to the small sample size used. Overall, an average increase in body weight was observed in kids as well as in the lambs. The effect on body weight differed between species. Further, studies are needed to evaluate the observed trend of increased body weight in supplement treated twins from the same mother compared to untreated group. This study provides preliminary evidence for species specific effects of Omega fatty acid enriched supplements on sheep and goat body weight. Further studies using more animals including the use of twin studies to control genetic variability offer an opportunity for better definition of this effect. Levels of infection, genetic variability in susceptibility of goats to parasites and other factors may also influence the efficacy of the supplement used.

The objective of this study was to use virtual screening with molecular docking to identify potential pan-PPAR ligands from the ZINC database. The 3D structural files of the receptor ligand binding domains (LBD), obtained from the Protein Data Bank (PDB), were energetically minimized and the binding pockets on each LBD were identified and measured. The screening was performed by docking each compound from the lead-like database to the LBD of the three receptors using the AutoDock software.

This review provides insight into the importance of understanding NETosis in cows, sheep, and goats in light of the importance to their health, welfare and use as animal models. Neutrophils are essential to innate immunity, pathogen infection, and inflammatory diseases. The relevance of NETosis as a conserved innate immune response mechanism and the translational implications for public health are presented. Increased understanding of NETosis in ruminants will contribute to the prediction of pathologies and design of strategic interventions targeting NETs. This will help to control pathogens such as coronaviruses and inflammatory diseases such as mastitis that impact all mammals, including humans. Definition of unique attributes of NETosis in ruminants, in comparison to what has been observed in humans, has significant translational implications for one health and global food security, and thus warrants further study. View Full-Text

Machine learning has become one of the most popular choices for developing computational approaches in protein structural bioinformatics. The ability to extract features from protein sequence/structure often becomes one of the crucial steps for the development of machine learning-based approaches. Over the years, various sequence, structural, and physicochemical descriptors have been developed for proteins and these descriptors have been used to predict/solve various bioinformatics problems. Hence, several feature extraction tools have been developed over the years to help researchers to generate numeric features from protein sequences. Most of these tools have some limitations regarding the number of sequences they can handle and the subsequent preprocessing that is required for the generated features before they can be fed to machine learning methods. Here, we present Feature Extraction from Protein Sequences (FEPS), a toolkit for feature extraction. FEPS is a versatile software package for generating various descriptors from protein sequences and can handle several sequences: the number of which is limited only by the computational resources. In addition, the features extracted from FEPS do not require subsequent processing and are ready to be fed to the machine learning techniques as it provides various output formats as well as the ability to concatenate these generated features. FEPS is made freely available via an online web server as well as a stand-alone toolkit. FEPS, a comprehensive toolkit for feature extraction, will help spur the development of machine learning-based models for various bioinformatics problems

Phosphorylation is one of the most important post-translational modifications and plays a pivotal role in various cellular processes. Although there exist several computational tools to predict phosphorylation sites, existing tools have not yet harnessed the knowledge distilled by pretrained protein language models. Herein, we present a novel deep learning-based approach called LMPhosSite for the general phosphorylation site prediction that integrates embeddings from the local window sequence and the contextualized embedding obtained using global (overall) protein sequence from a pretrained protein language model to improve the prediction performance. Thus, the LMPhosSite consists of two base-models: one for capturing effective local representation and the other for capturing global per-residue contextualized embedding from a pretrained protein language model. The output of these base-models is integrated using a score-level fusion approach. LMPhosSite achieves a precision, recall, Matthew's correlation coefficient, and F1-score of 38.78%, 67.12%, 0.390, and 49.15%, for the combined serine and threonine independent test data set and 34.90%, 62.03%, 0.298, and 44.67%, respectively, for the tyrosine independent test data set, which is better than the compared approaches. These results demonstrate that LMPhosSite is a robust computational tool for the prediction of the general phosphorylation sites in proteins.

O-linked β-N-acetylglucosamine (O-GlcNAc) is a distinct monosaccharide modification of serine (S) or threonine (T) residues of nucleocytoplasmic and mitochondrial proteins. O-GlcNAc modification (i.e., O-GlcNAcylation) is involved in the regulation of diverse cellular processes, including transcription, epigenetic modifications, and cell signaling. Despite the great progress in experimentally mapping O-GlcNAc sites, there is an unmet need to develop robust prediction tools that can effectively locate the presence of O-GlcNAc sites in protein sequences of interest. In this work, we performed a comprehensive evaluation of a framework for prediction of protein O-GlcNAc sites using embeddings from pre-trained protein language models. In particular, we compared the performance of three protein sequence-based large protein language models (pLMs), Ankh, ESM-2, and ProtT5, for prediction of O-GlcNAc sites and also evaluated various ensemble strategies to integrate embeddings from these protein language models. Upon investigation, the decision-level fusion approach that integrates the decisions of the three embedding models, which we call LM-OGlcNAc-Site, outperformed the models trained on these individual language models as well as other fusion approaches and other existing predictors in almost all of the parameters evaluated. The precise prediction of O-GlcNAc sites will facilitate the probing of O-GlcNAc site-specific functions of proteins in physiology and diseases. Moreover, these findings also indicate the effectiveness of combined uses of multiple protein language models in post-translational modification prediction and open exciting avenues for further research and exploration in other protein downstream tasks. LM-OGlcNAc-Site’s web server and source code are publicly available to the community. Keywords: O-GlcNAc prediction; protein language models; post-translational modification prediction; ensemble learning; embeddings

Motivation Recent advancements in natural language processing have highlighted the effectiveness of global contextualized representations from Protein Language Models (pLMs) in numerous downstream tasks. Nonetheless, strategies to encode the site-of-interest leveraging pLMs for per-residue prediction tasks, such as crotonylation (Kcr) prediction, remain largely uncharted. Results Herein, we adopt a range of approaches for utilizing pLMs by experimenting with different input sequence types (full-length protein sequence versus window sequence), assessing the implications of utilizing per-residue embedding of the site-of-interest as well as embeddings of window residues centered around it. Building upon these insights, we developed a novel residual ConvBiLSTM network designed to process window-level embeddings of the site-of-interest generated by the ProtT5-XL-UniRef50 pLM using full-length sequences as input. This model, termed T5ResConvBiLSTM, surpasses existing state-of-the-art Kcr predictors in performance across three diverse datasets. To validate our approach of utilizing full sequence-based window-level embeddings, we also delved into the interpretability of ProtT5-derived embedding tensors in two ways: firstly, by scrutinizing the attention weights obtained from the transformer’s encoder block; and secondly, by computing SHAP values for these tensors, providing a model-agnostic interpretation of the prediction results. Additionally, we enhance the latent representation of ProtT5 by incorporating two additional local representations, one derived from amino acid properties and the other from supervised embedding layer, through an intermediate-fusion stacked generalization approach, using an n-mer window sequence (or, peptide fragment). The resultant stacked model, dubbed LMCrot, exhibits a more pronounced improvement in predictive performance across the tested datasets. Availability and implementation LMCrot is publicly available at https://github.com/KCLabMTU/LMCrot.