Changes of diurnal rhythm and levels of total and free testosterone secretion from pre to late puberty in boys: testis size of 3 ml is a transition stage to puberty

OBJECTIVE: To establish levels for comparison for 24-h total and free serum testosterone in prepubertal boys and throughout pubertal development. DESIGN: The study subjects were 55 healthy boys, aged 5.0-18.6 years, who underwent serial sampling one or more times during their pubertal development. METHODS: Testicular volumes were determined by orchidometer. Serum testosterone was measured by a modified RIA (detection limit, 0.03 nmol/l). Free testosterone was calculated (calc-FT) using a formula derived from the law of mass action. RESULTS: Significant increases in testosterone and calc-FT concentrations in boys were found between testis volumes of 1 ml to 2 ml, 2 ml to 3 ml, 6 ml to 8 ml, and 10 ml to 15 ml. No differences were found between testis volumes of 3, 4, 5 and 6 ml neither were there differences between 8 and 10 ml, or between 15, 20 and 25 ml. Boys who had reached their final height had higher calc-FT values than boys who had the same pubertal development but had not reached their final height. Based on the results, puberty was classified into six stages: pre1 (testis, 1 ml), pre2 (testis, 2 ml), early (testis, 3-6 ml), mid (testis, 8-12 ml), late1 (testis,15-25 ml, not reached final height) and late2 (testis, 15-25 ml, reached final height). Serum testosterone was secreted with a diurnal variation in prepuberty and during puberty. The increase of testosterone in the morning hours started earlier in pubertal than in pre-pubertal boys. The most pronounced diurnal rhythm was found in early and in mid puberty. CONCLUSION: Using a sensitive method, and a pubertal reclassification, we have established levels for comparison of testosterone and calc-FT in prepubertal and pubertal boys. The existence of data for comparison forms the basis for future studies on pubertal disorders.     

Bacterial genome reduction using the progressive clustering of deletions via yeast sexual cycling

The availability of genetically tractable organisms with simple genomes is critical for the rapid, systems-level understanding of basic biological processes. Mycoplasma bacteria, with the smallest known genomes among free-living cellular organisms, are ideal models for this purpose, but the natural versions of these cells have genome complexities still too great to offer a comprehensive view of a fundamental life form. Here we describe an efficient method for reducing genomes from these organisms by identifying individually deletable regions using transposon mutagenesis and progressively clustering deleted genomic segments using meiotic recombination between the bacterial genomes harbored in yeast. Mycoplasmal genomes subjected to this process and transplanted into recipient cells yielded two mycoplasma strains. The first simultaneously lacked eight singly deletable regions of the genome, representing a total of 91 genes and ∼10% of the original genome. The second strain lacked seven of the eight regions, representing 84 genes. Growth assay data revealed an absence of genetic interactions among the 91 genes under tested conditions. Despite predicted effects of the deletions on sugar metabolism and the proteome, growth rates were unaffected by the gene deletions in the seven-deletion strain. These results support the feasibility of using single-gene disruption data to design and construct viable genomes lacking multiple genes, paving the way toward genome minimization. The progressive clustering method is expected to be effective for the reorganization of any mega-sized DNA molecules cloned in yeast, facilitating the construction of designer genomes in microbes as well as genomic fragments for genetic engineering of higher eukaryotes.Complexities of natural biological systems make it difficult to understand and define precisely the roles of individual genes and their integrated functions. The use of model organisms with a relatively small number of genes enables the isolation of core biological processes from their complex regulatory networks for extensive characterization. However, even the simplest natural microbes contain many genes of unknown function, as well as genes that can be singly or simultaneously deleted without any noticeable effect on growth rate in a laboratory setting (Hutchison et al. 1999; Glass et al. 2006; Posfai et al. 2006). Ill-defined genes and those mediating functional redundancies both compound the challenge of understanding even the simplest life forms.Toward generating a minimal cell where every gene is essential for the axenic viability of the organism, we are pursuing strategies to reduce the 1-Mb genome of Mycoplasma mycoides JCVI-syn1.0 (Gibson et al. 2010). Because we can (1) introduce this genome into yeast and maintain it as a plasmid (Benders et al. 2010; Karas et al. 2013a); and (2) “transplant” the genome from yeast into mycoplasma recipient cells (Lartigue et al. 2009), genetic tools in yeast are available for reducing this bacterial genome. Several systems offer advanced tools for bacterial genome engineering. Here we further exploit distinctive features of yeast for this purpose.Methods for serially replacing genomic regions with selectable markers are limited by the number of available markers. One effective approach is to reuse the same marker after precise and scarless marker excision (Storici et al. 2001). We have previously used a self-excising marker (Noskov et al. 2010) six times in yeast to generate a JCVI-syn1.0 genome lacking all six restriction systems (JCVI-syn1.0 ∆1-6) (Karas et al. 2013a). Despite the advantages of scarless engineering, sequential procedures are time-consuming. When applied to poorly characterized genes with the potential to interact with other genes, some paths for multigene knockout may lead to dead ends that result from synergistic mutant phenotypes. When a dead end is reached, sequentially returning to a previous genome in an effort to find a detour to a viable higher-order multimutant may be prohibitively time-consuming.An alternative approach to multigene engineering, available in yeast, is to prepare a set of single mutants and combine the deletions into a single strain via cycles of mating and meiotic recombination (Fig. 1A; Pinel et al. 2011; Suzuki et al. 2011, 2012). With a green fluorescent protein (GFP) reporter gene inserted in each deletion locus, the enrichment of higher-order yeast deletion strains in the meiotic population can be accomplished using flow cytometry. Here we apply this method to the JCVI-syn1.0 ∆1-6 exogenous, bacterial genome harbored in yeast to nonsequentially assemble deletions for genes predicted to be individually deletable based on biological knowledge or transposon-mediated disruption data. The functional identification of simultaneously deletable regions is expected to accelerate the effort to construct a minimal genome.Open in a separate windowFigure 1.Progressive clustering of deleted genomic segments. (A) Scheme of the method. Light blue oval represents a bacterial cell. Black ring or horizontal line denotes a bacterial genome, with the orange box indicating the yeast vector used as a site for linearization and recircularization. Gray shape denotes a yeast cell. Green dot in the genome indicates a deletion replaced with a GFP marker. (B) Map of deleted regions. Orange box indicates the yeast vector sequence used for genome linearization and recircularization. Green boxes indicate regions deleted in multimutant mycoplasma strains. Blue boxes denote restriction modification (RM) systems that are also deleted in the strains. (C) Pulsed-gel electrophoresis result for deleted genomes. The starting strain was the JCVI-syn1.0 ∆1–6 strain (1062 kb). Two strains were analyzed for each design of simultaneous deletion (962 kb for eight-deletion or 974 kb for seven-deletion genome). Ladder is a set of yeast chromosomes (New England BioLabs). (D) GFP-RFP ratio sorting result. Standard sorting was compared with sorting based on a GFP-RFP ratio (Methods).     

Linked selection and recombination rate variation drive the evolution of the genomic landscape of differentiation across the speciation continuum of Ficedula flycatchers

Speciation is a continuous process during which genetic changes gradually accumulate in the genomes of diverging species. Recent studies have documented highly heterogeneous differentiation landscapes, with distinct regions of elevated differentiation (“differentiation islands”) widespread across genomes. However, it remains unclear which processes drive the evolution of differentiation islands; how the differentiation landscape evolves as speciation advances; and ultimately, how differentiation islands are related to speciation. Here, we addressed these questions based on population genetic analyses of 200 resequenced genomes from 10 populations of four Ficedula flycatcher sister species. We show that a heterogeneous differentiation landscape starts emerging among populations within species, and differentiation islands evolve recurrently in the very same genomic regions among independent lineages. Contrary to expectations from models that interpret differentiation islands as genomic regions involved in reproductive isolation that are shielded from gene flow, patterns of sequence divergence (d_xy and relative node depth) do not support a major role of gene flow in the evolution of the differentiation landscape in these species. Instead, as predicted by models of linked selection, genome-wide variation in diversity and differentiation can be explained by variation in recombination rate and the density of targets for selection. We thus conclude that the heterogeneous landscape of differentiation in Ficedula flycatchers evolves mainly as the result of background selection and selective sweeps in genomic regions of low recombination. Our results emphasize the necessity of incorporating linked selection as a null model to identify genome regions involved in adaptation and speciation.Uncovering the genetic architecture of reproductive isolation and its evolutionary history are central tasks in evolutionary biology. The identification of genome regions that are highly differentiated between closely related species, and thereby constitute candidate regions involved in reproductive isolation, has recently been a major focus of speciation genetic research. Studies from a broad taxonomic range, involving organisms as diverse as plants (Renaut et al. 2013), insects (Turner et al. 2005; Lawniczak et al. 2010; Nadeau et al. 2012; Soria-Carrasco et al. 2014), fishes (Jones et al. 2012), mammals (Harr 2006), and birds (Ellegren et al. 2012) contribute to the emerging picture of a genomic landscape of differentiation that is usually highly heterogeneous, with regions of locally elevated differentiation (“differentiation islands”) widely spread over the genome. However, the evolutionary processes driving the evolution of the differentiation landscape and the role of differentiation islands in speciation are subject to controversy (Turner and Hahn 2010; Cruickshank and Hahn 2014; Pennisi 2014).Differentiation islands were originally interpreted as “speciation islands,” regions that harbor genetic variants involved in reproductive isolation and are shielded from gene flow by selection (Turner et al. 2005; Soria-Carrasco et al. 2014). During speciation-with-gene-flow, speciation islands were suggested to evolve through selective sweeps of locally adapted variants and by hitchhiking of physically linked neutral variation (“divergence hitchhiking”) (Via and West 2008); gene flow would keep differentiation in the remainder of the genome at bay (Nosil 2008; Nosil et al. 2008). In a similar way, speciation islands can arise by allopatric speciation followed by secondary contact. In this case, genome-wide differentiation increases during periods of geographic isolation, but upon secondary contact, it is reduced by gene flow in genome regions not involved in reproductive isolation. In the absence of gene flow in allopatry, speciation islands need not (but can) evolve by local adaptation, but may consist of intrinsic incompatibilities sensu Bateson-Dobzhansky-Muller (Bateson 1909; Dobzhansky 1937; Muller 1940) that accumulated in spatially isolated populations.However, whether differentiation islands represent speciation islands has been questioned. Rather than being a cause of speciation, differentiation islands might evolve only after the onset of reproductive isolation as a consequence of locally accelerated lineage sorting (Noor and Bennett 2009; Turner and Hahn 2010; White et al. 2010; Cruickshank and Hahn 2014; Renaut et al. 2014), such as in regions of low recombination (Nachman 2002; Sella et al. 2009; Cutter and Payseur 2013). In these regions, the diversity-reducing effects of both positive selection and purifying selection (background selection [BGS]) at linked sites (“linked selection”) impact physically larger regions due to the stronger linkage among sites. The thereby locally reduced effective population size (N_e) will enhance genetic drift and hence inevitably lead to increased differentiation among populations and species.These alternative models for the evolution of a heterogeneous genomic landscape of differentiation are not mutually exclusive, and their population genetic footprints can be difficult to discern. In the cases of (primary) speciation-with-gene-flow and gene flow at secondary contact, shared variation outside differentiation islands partly stems from gene flow. In contrast, under linked selection, ancestral variation is reduced and differentiation elevated in regions of low recombination, while the remainder of the genome may still share considerable amounts of ancestral genetic variation and show limited differentiation. Many commonly used population genetic statistics do not capture these different origins of shared genetic variation and have the same qualitative expectations under both models, such as reduced diversity (π) and skews toward an excess of rare variants (e.g., lower Tajima''s D) in differentiation islands relative to the remainder of the genome. However, since speciation islands should evolve by the prevention or breakdown of differentiation by gene flow in regions not involved in reproductive isolation, substantial gene flow should be detectable in these regions (Cruickshank and Hahn 2014) and manifested in the form of reduced sequence divergence (d_xy) or as an excess of shared derived alleles in cases of asymmetrical gene flow (Patterson et al. 2012). Under linked selection, predictions are opposite for d_xy (Cruickshank and Hahn 2014), owing to reduced ancestral diversity in low-recombination regions. Further predictions for linked selection include positive and negative relationships of recombination rate with genetic diversity (π) and differentiation (F_ST), respectively, and inverse correlations of the latter two with the density of targets for selection. Finally, important insights into the nature of differentiation islands may be gained by studying the evolution of differentiation landscapes across the speciation continuum. Theoretical models and simulations of speciation-with-gene-flow predict that after an initial phase during which differentiation establishes in regions involved in adaptation, differentiation should start spreading from these regions across the entire genome (Feder et al. 2012, 2014; Flaxman et al. 2013).Unravelling the processes driving the evolution of the genomic landscape of differentiation, and hence understanding how genome differentiation unfolds as speciation advances, requires genome-wide data at multiple stages of the speciation continuum and in a range of geographical settings from allopatry to sympatry (Seehausen et al. 2014). Although studies of the speciation continuum are emerging (Hendry et al. 2009; Kronforst et al. 2013; Shaw and Mullen 2014, and references therein), empirical examples of genome differentiation at multiple levels of species divergence remain scarce (Andrew and Rieseberg 2013; Kronforst et al. 2013; Martin et al. 2013), and to our knowledge, have so far not jointly addressed the predictions of alternative models for the evolution of the genomic landscape of differentiation. In the present study, we implemented such a study design encompassing multiple populations of four black-and-white flycatcher sister species of the genus Ficedula (Fig. 1A,B; Supplemental Fig. S1; for a comprehensive reconstruction of the species tree, see Nater et al. 2015). Previous analyses in collared flycatcher (F. albicollis) and pied flycatcher (F. hypoleuca) revealed a highly heterogeneous differentiation landscape across the genome (Ellegren et al. 2012). An involvement of gene flow in its evolution would be plausible, as hybrids between these species occur at low frequencies in sympatric populations in eastern Central Europe and on the Baltic Islands of Gotland and Öland (Alatalo et al. 1990; Sætre et al. 1999), although a recent study based on genome-wide markers identified no hybrids beyond the F₁ generation (Kawakami et al. 2014a). Still, gene flow from pied into collared flycatcher appears to have occurred (Borge et al. 2005; Backström et al. 2013; Nadachowska-Brzyska et al. 2013) despite premating isolation (for review, see Sætre and Sæther 2010), hybrid female sterility (Alatalo et al. 1990; Tegelström and Gelter 1990), and strongly reduced long-term fitness of hybrid males (Wiley et al. 2009). Atlas flycatcher (F. speculigera) and semicollared flycatcher (F. semitorquata) are two closely related species, which have been less studied, but may provide interesting insights into how genome differentiation evolves over time. Here, we take advantage of this system to identify the processes underlying the evolution of differentiation islands based on the population genetic analysis of whole-genome resequencing data of 200 flycatchers.Open in a separate windowFigure 1.A recurrently evolving genomic landscape of differentiation across the speciation continuum in Ficedula flycatchers. (A) Species’ neighbor-joining tree based on mean genome-wide net sequence divergence (d_A). The same species tree topology was inferred with 100% bootstrap support from the distribution of gene trees under the multispecies coalescent (Supplemental Fig. S1). (B) Map showing the locations of population sampling and approximate species ranges. (C) Population genomic parameters along an example chromosome (Chromosome 4A) (see Supplemental Figs. S2, S4 for all chromosomes). Color codes for specific–specific parameters: (blue) collared; (green) pied; (orange) Atlas; (red) semicollared. Color codes for d_xy: (green) collared-pied; (light blue) collared-Atlas; (blue) collared-semicollared; (orange) pied-Atlas; (red) pied-semicollared; (black) Atlas-semicollared. For differentiation within species, comparisons with the Italian (collared) and Spanish (pied) populations are shown. Color codes for F_ST within collared flycatchers: (cyan) Italy–Hungary; (light blue) Italy–Czech Republic; (dark blue) Italy–Baltic. Color codes for F_ST within pied flycatchers: (light green) Spain–Sweden; (green) Spain–Czech Republic; (dark green) Spain–Baltic. (D) Distributions of differentiation (F_ST) from collared flycatcher along the speciation continuum. Distributions are given separately for three autosomal recombination percentiles (33%; 33%–66%; 66%–100%) corresponding to high (>3.4 cM/Mb, blue), intermediate (1.3–3.4 cM/Mb, orange), and low recombination rate (0–1.3 cM/Mb, red), and the Z Chromosome (green). Geographically close within-species comparison: Italy–Hungary. Comparisons within species include the geographically close Italian and Hungarian populations (within [close]), and the geographically distant Italian and Baltic populations (within [far]). Geographically far within-species comparison: Italy–Baltic. (E) Differentiation from collared flycatcher along an example chromosome (Chromosome 11) (see Supplemental Fig. S3 for all chromosomes). Color codes for between-species comparisons: (green) pied; (orange) Atlas; (red) semicollared; (dark red) red-breasted; (black) snowy-browed flycatcher. Color codes for within-species comparisons: (cyan) Italy–Hungary; (blue) Italy–Baltic. Flycatcher artwork in panel A courtesy of Dan Zetterström.     

Partners in Health: A Conceptual Framework for the Role of Community Health Workers in Facilitating Patients' Adoption of Healthy Behaviors

We formulated a conceptual framework that begins to answer the national call to improve health care access, delivery, and quality by explaining the processes through which community health workers (CHWs) facilitate patients’ adoption of healthy behaviors. In September 2011 to January 2012, we conducted a qualitative study that triangulated multiple data sources: 26 in-depth interviews, training documents, and patient charts. CHWs served as partners in health to immigrant Filipinos with hypertension, leveraging their cultural congruence with intervention participants, employing interpersonal communication techniques to build trust and rapport, providing social support, and assisting with health behavior change. To drive the field forward, this work can be expanded with framework testing that may influence future CHW training and interventions.Community health workers (CHWs) are laypeople from within the communities where they work, who share common characteristics with their patients (e.g., ethnicity, culture, race, and language).1–4 CHWs have demonstrated effectiveness in an array of conditions, ranging from maternal and child health to chronic disease management.4–12 A systematic review of randomized controlled trials on CHW effectiveness determined that CHWs address health issues among various ethnic and racial groups, help improve use of early intervention services for children at risk for developmental delay, improve screening for breast and cervical cancer, and aid in improving dietary behaviors and blood pressure control.10CHWs have specific training in providing basic nutrition and health promotion services; they aim to improve health care access through a set of core skills,13,14 advocacy, outreach, and education.2,15,16 They function in multiple roles: bridging communication between patients and providers, providing health education and counseling, and monitoring health status.7,12 Thus, CHWs have a tremendous potential to influence and improve health outcomes.Although CHWs play integral roles in supporting patients’ individual health behaviors,17–23 the processes through which they are influential are poorly understood.21 Increasing use of this model in the United States, and recommendations in the Patient Protection and Affordable Care Act to integrate CHWs as part of health care teams,24 underscore the need to advance the knowledge base surrounding CHWs and to better understand mechanisms of this role. We developed a conceptual framework to explicate the processes through which CHWs facilitate the adoption of healthy behaviors among their patients.     

Sjögren's syndrome with myalgia is associated with subnormal secretion of cytokines by peripheral blood mononuclear cells

OBJECTIVE: To measure in vitro cytokine release from peripheral blood mononuclear cells (PBMC) and serum cytokines in patients with primary Sj?gren's syndrome (SS) with and without myalgia, compared to patients with rheumatoid arthritis (RA) and healthy controls. METHODS: Sixteen women with SS (8 with myalgia, 8 without pain), 15 women with RA, and 14 healthy women were studied. PBMC were isolated and cultured. Secretion of interleukin 1 beta (IL-1 beta), IL-6, IL-10, and tumor necrosis factor-alpha (TNF-alpha) was measured in cell supernatants with or without stimulation with phytohemagglutinin, tetanus toxoid, or purified protein derivative (PPD). Enzyme-linked immunospot was used to enumerate interferon-gamma (IFN-gamma) and IL-4-secreting cells. Serum concentrations of IL-8 and IL-18 were analyzed by ELISA. RESULTS: PPD-stimulated PBMC from SS patients responded with less production of IL-10, TNF-alpha, and IFN-gamma compared to controls. Patients with SS and pain were hyporesponsive also with respect to IL-1 beta and IL-6. The generally subnormal cytokine release was statistically significant in myalgic patients with SS compared to healthy controls. Serum IL-18 was increased in both SS groups as well as in patients with RA, and the highest levels were found in myalgic patients with SS. Serum IL-8 was increased in RA but not in SS. CONCLUSION: Patients with SS, especially those with myalgia, had diminished PBMC cytokine release and increased serum IL-18. This finding suggests that impaired cytokine regulation may have pathogenetic importance for myalgia in SS.     

Genotyping and characterization of two polymorphic microsatellite markers located within introns 29 and 30 of the human thyroglobulin gene.

The purpose of the present work was to characterize two new polymorphic microsatellite markers in the thyroglobulin gene. TGrI29 and TGrI30 repeats are located within introns 29 and 30, respectively. Genetic studies were carried out by using polymerase chain reaction (PCR) followed by denaturing polyacrilamide gel electrophoresis. TGrI29 exhibited clearly 4 distinguishable alleles ranging from 197 to 203 base pair (bp) in length and TGrI30 showed 8 alleles ranging from 502 to 542 bp. We characterized the two markers by determinating allele frequencies and measures of variation. The heterozygosities (HET) observed of TGrI29 and TGrI30 were 0.859 and 0.522, respectively. The polymorphism information contents (PIC) were 0.471 and 0.434, respectively. No significant differences from Hardy-Weinberg values were found for these two systems. The PCR products of each allele were cloned using the pGEM-T Easy vector and directly sequenced by Taq polymerase-based chain terminator method. Sequencing analysis indicated that both loci are complex repeats, TGrI29 containing two types of variable motifs (tc)n and (tg)n, and TGrI30 a tetra-nucleotide tandem units (atcc)n. In two TGrI29 alleles and one TGrI30 allele were found two different subtypes in each one, with the same molecular weights but different distribution of the tandem repeats. In conclusion, both microsatellites analyzed are highly informative polymorphic markers and can be used in linkage studies in families with congenital hypothyroidism or autoimmunity thyroid diseases.