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Transcriptomic Changes Throughout Post-Hatch Development in Gallus Gallus Pituitary

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58:1 E M PRITCHETT and others Chicken pituitary transcriptome 58: 1 43–55 Research Open Access Transcriptomic changes throughout post-hatch development in Gallus gallus pituitary Correspondence 1 2 1 Elizabeth M Pritchett , Susan J Lamont and Carl J Schmidt should be addressed 1Animal and Food Science, University of Delaware, Newark, Delaware, USA to E M Pritchett 2Animal Science, Iowa State University, Ames, Iowa, USA Email [email protected] Abstract The pituitary gland is a neuroendocrine organ that works closely with the hypothalamus to affect multiple processes within the body including the stress response, metabolism, Key Words growth and immune function. Relative tissue expression (rEx) is a transcriptome analysis f chicken method that compares the genes expressed in a particular tissue to the genes expressed f pituitary in all other tissues with available data. Using rEx, the aim of this study was to identify f gene expression genes that are uniquely or more abundantly expressed in the pituitary when compared f development to all other collected chicken tissues. We applied rEx to define genes enriched in the chicken pituitaries at days 21, 22 and 42 post-hatch. rEx analysis identified 25 genes shared between all time points, 295 genes shared between days 21 and 22 and 407 genes unique to day 42. The 25 genes shared by all time points are involved in morphogenesis and general nervous tissue development. The 295 shared genes between days 21 and 22 are involved in neurogenesis and nervous system development and differentiation. Journal of Molecular Endocrinology The 407 unique day 42 genes are involved in pituitary development, endocrine system development and other hormonally related gene ontology terms. Overall, rEx analysis indicates a focus on nervous system/tissue development at days 21 and 22. By day 42, in addition to nervous tissue development, there is expression of genes involved in the endocrine system, possibly for maturation and preparation for reproduction. This study defines the transcriptome of the chicken pituitary gland and aids in understanding the Journal of Molecular expressed genes critical to its function and maturation. Endocrinology (2017) 58, 43–55 Introduction The avian pituitary gland is a neuroendocrine organ that (i.e. adrenocorticotropic hormone (ACTH)). The posterior works closely with the hypothalamus to regulate multiple pituitary gland, or the neurohypophysis, is composed processes within the body including the stress response, of nervous tissue and is connected to the hypothalamus reproduction, metabolism, growth and immune function. via the median eminence. Neurosecretory terminals The hypothalamus receives neuronal and endocrine within the posterior pituitary store and release arginine, signals from the brain and body and synthesizes releasing vasotocin (AVT) and mesotocin that are synthesized in hormones that are sent to the pituitary gland to either the supraoptic nucleus and paraventricular nucleus of inhibit or stimulate the production of several hormones the hypothalamus, respectively. Releasing hormones that will directly affect other tissues (i.e. growth hormone synthesized by the hypothalamus are transported to the (GH)) or stimulate the release of further protein products anterior pituitary gland through the hypophyseal blood http://jme.endocrinology-journals.org © 2017 The authors This work is licensed under a Creative Commons DOI: 10.1530/JME-16-0186 Published by Bioscientifica Ltd. Attribution 3.0 Unported License. Printed in Great Britain Downloaded from Bioscientifica.com at 09/24/2021 12:20:26PM via free access 10.1530/JME-16-0186 Research E M PRITCHETT and others Chicken pituitary transcriptome 58:1 44 portal system and regulate the production and release of Total RNA quantity was measured using a Qubit various endocrine hormones (ACTH, GH, LH, FSH and Fluorometer, and quality was assessed by fragment TSH). Relative tissue expression analysis (rEx) was used analysis. A total of 15 pituitary glands from day 21, 10 to compare the chicken pituitary gland transcriptome to from day 22 and 10 from day 42 were used for individual the transcriptome of all other tissues our laboratory has Rnaseq library preparation using the Illumina Stranded analyzed in the broiler chicken across three time points: RNAseq kit (San Diego, CA, USA). All 35 samples were days 21, 22 and 42 post-hatch. Previous work focusing on sequenced at the Delaware Biotechnology Institute (DBI) the morphometric changes of broiler chickens post-hatch Sequencing and Genotyping center using the Illumina identified two distinct growth periods: one from day of HiSeq 2500 sequencer. All libraries were sequenced to a hatch through day 14, and one from day 14 to day 42 or depth of 20–30 million reads per library. The reads were typical market age (Schmidt et al. 2009). The days discussed aligned to the Gallus gallus, ver 4 genome sequence, fall into the second growth phase. These days were also identified and counted using software packages Bowtie, chosen as part of a larger heat stress study conducted in Tophat and Cufflinks, and expression levels were reported our laboratory. The pituitary gland is a complex tissue in as fragments per kilobase of gene per million mapped reads that it contains many different cell types, and signals are (FPKM) values for further analysis (Trapnell et al. 2009, sent through neural endocrine, paracrine and autocrine Langmead & Salzberg 2012). Three biological replicates networks. The messages received by the pituitary gland for each day (day 21, 22 and 42) were used for cDNA lead to many changes within the tissue and results in synthesis using the SuperScript First-Strand Synthesis tissue plasticity. The results of this analysis identify the System for RT-PCR (Invitrogen). cDNA concentration expression levels of genes in the pituitary gland that are was determined using the Qubit Fluorometer and diluted either more abundant or are unique to this particular to 30 ng/µL for PCR. qRT-PCR was performed using Fast tissue at specific points in development. These data confer SYBR green master mix (Applied Biosystems) on the a better understanding of the genes and pathways that are Applied Biosystems 7500 Fast Real-Time PCR system critical to the function of the pituitary gland. for the following genes: glycoprotein alpha subunit (CGA), follicle-stimulating hormone beta (FSHB), growth hormone (GH), proopiomelanocortin (POMC), prolactin Materials and methods (PRL) and thyroid-stimulating hormone beta (TSHB). Each Animal housing and tissue collection qRT-PCR (Table 1) was performed in triplicate, and analysis was completed using the delta Ct method. All sequencing Journal of Molecular Endocrinology Male Ross 708 broiler chickens (Gallus gallus) were data generated in the current project have been uploaded obtained on the day of hatch from Mountaire Hatchery in the NCBI Gene Expression Omnibus application and (Millsboro, DE, USA) and placed in large-colony houses are accessible through GEO series, accession number on the University of Delaware farm (Newark, DE, USA). GSE89297. Husbandry and management followed all standards and procedures as approved by the Animal Care and Use Committee (AACUC #(27) 03-12-14R). Standard broiler Relative tissue expression analysis feed (corn-soy), which met all NRC requirements (National Research Council 1994), and water were supplied, and The gene expression of the pituitary gland was compared animals had ad libitum access throughout the trial. Birds to the expression of other tissues (abdominal fat pad, were humanely killed and the pituitaries were collected heart fat pad, breast muscle, cerebellum, heart, liver, on 21, 22 and 42 days post-hatch as part of a larger study. duodenum, jejunum, ileum, spleen, retina, pineal and Whole pituitaries were placed into liquid nitrogen and hypothalamus) analyzed by our laboratory following the stored at −80°C until further analysis. relative tissue expression (rEx) protocol outlined by Bailey and coworkers (Bailey et al. 2009). Each day the expression was analyzed independently, and maximum FPKM values RNA isolation, cDNA synthesis, Rnaseq library for each gene in the pituitary gland were compared to the preparation and qRT-PCR validation median FPKM value of each gene in all other collected tissues with the following calculation: Total RNA was extracted from whole pituitary glands (8–20 mg depending on age) from individual birds using Maximum FPKMfor each genein pituitary gland log (1) the Qiagen RNeasy Mini Kit (Germantown, MD, USA). 2 Median FPKMfor eachhgeneinall othercollected tissues http://jme.endocrinology-journals.org © 2017 Society for Endocrinology Published by Bioscientifica Ltd. DOI: 10.1530/JME-16-0186 Printed in Great Britain Downloaded from Bioscientifica.com at 09/24/2021 12:20:26PM via free access Research E M PRITCHETT and others Chicken pituitary transcriptome 58:1 45 Table 1 Primer sequences used for qRT-PCR transcriptome validation. Gene Forward primer Reverse primer CGA 5′ GCAACGTGCTGTGTAGCAAAG 3′ 5′ TGGTTCTCTATCTTCACATTGCCTT 3′ FSHB 5′ CGTACAGGGTAGAGCCAACGA 3′ 5′ GGCCCTCAAAAGGCTGAAC 3′ GH 5′ GCTTCAAGAAGGATCTGCACAA 3′ 5′ GCGCCGGCACTTCATC 3′ POMC 5′ GCTACGGCGGCTTCATGA 3′ 5′ CGATGACGTTTTTGAACAGA 3′ PRL 5′ TTGGGCGGGTTCATTCTG 3′ 5′ GGCCGTCCCAGTGAGAGTAA 3′ TSHB 5′ TGGCCATCAACACCACCAT 3′ 5′ CGTTGCTGTCCCGTGTCA 3′ Primer sequences used in qRT-PCR validation of transcriptomic data comparing expression levels between days 21, 22 and 42. Alpha subunit of glycoproteins (CGA), follicle-stimulating hormone,
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    Published online: 2021-02-12 THIEME Review Article 101 Genetic Factors in Nonsyndromic Orofacial Clefts Mahamad Irfanulla Khan1 Prashanth C.S.2 Narasimha Murthy Srinath3 1 Department of Orthodontics & Dentofacial Orthopedics, The Oxford Address for correspondence Mahamad Irfanulla Khan, BDS, MDS, Dental College, Bangalore, Karnataka, India Department of Orthodontics & Dentofacial Orthopedics, The Oxford 2 Department of Orthodontics & Dentofacial Orthopedics, DAPM R.V. Dental College, Bangalore, Karnataka 560068, India Dental College, Bangalore, Karnataka, India (e-mail: [email protected]). 3 Department of Oral & Maxillofacial Surgery, Krishnadevaraya College of Dental Sciences, Bangalore, Karnataka, India Global Med Genet 2020;7:101–108. Abstract Orofacial clefts (OFCs) are the most common congenital birth defects in humans and immediately recognized at birth. The etiology remains complex and poorly understood and seems to result from multiple genetic and environmental factors along with gene– environment interactions. It can be classified into syndromic (30%) and nonsyndromic (70%) clefts. Nonsyndromic OFCs include clefts without any additional physical or Keywords cognitive deficits. Recently, various genetic approaches, such as genome-wide associ- ► orofacial clefts ation studies (GWAS), candidate gene association studies, and linkage analysis, have ► nonsyndromic identified multiple genes involved in the etiology of OFCs. ► genetics This article provides an insight into the multiple genes involved in the etiology of OFCs. ► gene mutation Identification of specific genetic causes of clefts helps in a better understanding of the ► genome-wide molecular pathogenesis of OFC. In the near future, it helps to provide a more accurate association study diagnosis, genetic counseling, personalized medicine for better clinical care, and ► linkage analysis prevention of OFCs.
  • Genome-Wide Investigation of Gene-Cancer Associations to Predict Novel Therapeutic Targets in Oncology

    Genome-Wide Investigation of Gene-Cancer Associations to Predict Novel Therapeutic Targets in Oncology

    Supplementary Material for: Genome-wide investigation of gene-cancer associations to predict novel therapeutic targets in oncology by Adrian´ Bazaga, Dan Leggate and Hendrik Weisser 1 Supplementary Table S1: Details of the artificial neural network architecture used in this work. Layer Type Number of neurons (output) Activation function 1 Fully-connected 64 ReLU 2 Fully-connected 128 ReLU 3 Fully-connected 16 ReLU 4 Fully-connected 2 Sigmoid 2 Supplementary Table S2: Performance in terms of test set AUC achieved by each of the five different machine learning methods across cancer types. Cancer type Bladder Breast Colon Kidney Leukemia Liver Lung Ovarian Pancreatic Method Logistic Regression 0.78 0.77 0.69 0.86 0.75 0.84 0.81 0.79 0.75 Support Vector Machine 0.77 0.78 0.72 0.88 0.72 0.84 0.87 0.8 0.73 Gradient Boosting Machine 0.75 0.7 0.74 0.75 0.71 0.81 0.73 0.74 0.73 Neural Network 0.67 0.72 0.71 0.71 0.7 0.86 0.75 0.75 0.72 Random Forests 0.76 0.75 0.76 0.79 0.74 0.85 0.83 0.77 0.76 3 Distributions of predicted probabilities Distributions of standard scores 3.0 Cancer type: Cancer type: bladder 0.6 bladder breast breast colon colon 2.5 kidney kidney leukemia 0.5 leukemia liver liver lung lung 2.0 ovarian ovarian 0.4 pancreatic pancreatic 1.5 0.3 Density Density 1.0 0.2 0.5 0.1 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 −2 −1 0 1 2 3 P(target) z−score Supplementary Figure S1: Distributions (kernel density estimates) of genome-wide predicted probabilities for different cancer types, before (left) and after (right) scaling per cancer type.