Crossover Patterning by the Beam-Film Model: Analysis and Implications

Crossing-over is a central feature of meiosis. Meiotic crossover (CO) sites are spatially patterned along chromosomes. CO-designation at one position disfavors subsequent CO-designation(s) nearby, as described by the classical phenomenon of CO interference. If multiple designations occur, COs tend to be evenly spaced. We have previously proposed a mechanical model by which CO patterning could occur. The central feature of a mechanical mechanism is that communication along the chromosomes, as required for CO interference, can occur by redistribution of mechanical stress. Here we further explore the nature of the beam-film model, its ability to quantitatively explain CO patterns in detail in several organisms, and its implications for three important patterning-related phenomena: CO homeostasis, the fact that the level of zero-CO bivalents can be low (the “obligatory CO”), and the occurrence of non-interfering COs. Relationships to other models are discussed

Prevalence of Common Respiratory Viral Infections and Identification of Adenovirus in Hospitalized Adults in Harbin, China 2014 to 2017

Background: Respiratory infections pose a great challenge in global health, and the prevalence of viral infection in adult patients has been poorly understood in northeast China. Harbin is one of the major cities in northeast China, and more than half of any given year in Harbin is occupied by winter. To reveal the viral etiology and seasonality in adult patients from Harbin, a 4-year consecutive survey was conducted in Harbin, China.Methods: From January 2014 to December 2017, specimens were obtained from adult patients admitted to the Second Affiliated Hospital of Harbin Medical University with lower respiratory tract infections. Sputum samples were examined by direct immunofluorescence assays to detect seven common respiratory viruses, including influenza virus (type A and B), parainfluenza virus (type 1 to 3), respiratory syncytial virus and adenovirus. Adenovirus positive samples were seeded onto A549 cells to isolate viral strains. Phylogenetic analysis was conducted on the highly variable region of adenoviral hexon gene.Results: A total of 1,300 hospitalized adult patients with lower respiratory tract infections were enrolled, in which 189 patients (14.5%) were detected as having at least one viral infection. The co-infection rate in this study was 25.9% (49/189). The dominant viral pathogen from 2014 to 2017 was parainfluenza virus, with a detection rate of 7.2%, followed by influenza virus, respiratory syncytial virus and adenovirus. Based on the climate seasons determined by daily average temperature, the highest overall viral detection rate was detected in spring (22.0%, 52/236), followed by winter (13.4%, 109/813), autumn (11.4%, 13/114) and summer (10.9%, 15/137). Adenovirus type 3 strains with slight variations were isolated from positive cases, which were closely related to the GB strain from the United States, as well as the Harbin04B strain isolated locally.Conclusion: This study demonstrated that common respiratory viruses were partially responsible for hospitalized lower respiratory tract infections in adult patients from Harbin, China, with parainfluenza virus as the dominant viral pathogen. Climate seasons could be rational indicators for the seasonality analysis of airborne viral infections. Future surveillance on viral mutations would be necessary to reveal the evolutionary history of respiratory viruses

α1A-Adrenergic Receptor Induces Activation of Extracellular Signal-Regulated Kinase 1/2 through Endocytic Pathway

G protein-coupled receptors (GPCRs) activate mitogen-activated protein kinases through a number of distinct pathways in cells. Increasing evidence has suggested that endosomal signaling has an important role in receptor signal transduction. Here we investigated the involvement of endocytosis in α1A-adrenergic receptor (α1A-AR)-induced activation of extracellular signal-regulated kinase 1/2 (ERK1/2). Agonist-mediated endocytic traffic of α1A-AR was assessed by real-time imaging of living, stably transfected human embryonic kidney 293A cells (HEK-293A). α1A-AR was internalized dynamically in cells with agonist stimulation, and actin filaments regulated the initial trafficking of α1A-AR. α1A-AR-induced activation of ERK1/2 but not p38 MAPK was sensitive to disruption of endocytosis, as demonstrated by 4°C chilling, dynamin mutation and treatment with cytochalasin D (actin depolymerizing agent). Activation of protein kinase C (PKC) and C-Raf by α1A-AR was not affected by 4°C chilling or cytochalasin D treatment. U73122 (a phospholipase C [PLC] inhibitor) and Ro 31–8220 (a PKC inhibitor) inhibited α1B-AR- but not α1A-AR-induced ERK1/2 activation. These data suggest that the endocytic pathway is involved in α1A-AR-induced ERK1/2 activation, which is independent of Gq/PLC/PKC signaling

The beam-film model.

<p>(A) Beam-film model <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004042#pgen.1004042-Kleckner1" target="_blank">[3]</a>. CO-designation is promoted by stress. Each stress-promoted CO-designation reduces the stress level to zero at the designation point. That effect redistributes in the vicinity, decreasing exponentially with distance, correspondingly reducing the probability of subsequent designation(s) in the immediate vicinity. Subsequent CO-designations will tend to occur in regions with higher remaining stress levels, thus giving an even distribution. More specifically: with a film attached to a beam, if the beam expands relative to the film, stress arises along the film, causing it to crack at the positions of flaws. A crack at one position will release the stress nearby (with a distance L that is characteristic of the materials) thus reducing the probability that another crack occurs nearby. Assuming a maximal possible stress level of σ₀, if a crack occurs at an isolated position that is unaffected by any prior cracks, then the stress level at any distance “x” to either side is σ = σ₀ (1−e^−x/L). If two cracks occur near one another, the stress levels at positions between them is the sum of their individual effects, with additional considerations also coming into play at the ends of the beam as described <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004042#pgen.1004042-Kleckner1" target="_blank">[3]</a>. (B) A generalized version of the beam-film model involving sequential rounds of event designation and spreading interference as described by the mathematical expressions of the BF model.</p

Variations in A, c or M can alter CO patterns.

<p>All panels: BF simulations as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004042#pgen-1004042-g003" target="_blank">Figure 3</a>. (A) Effects of variations in precursor reactivity relationships. (B, C) Effects of variations in end clamping status on the distribution of COs along the chromosome (Panel B) and on CoC and ED relationships (Panel C). (D) Variations in maturation efficiency (M) do not affect CoC relationships but do affect the ED.</p

Variations in the L and/or Smax can alter CO patterns.

<p>All panels: BF simulations as in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004042#pgen-1004042-g003" target="_blank">Figure 3</a>. Panels (A) and (B) illustrate the effects of variations in L or Smax, respectively. Panels (C) and (D) illustrate the fact that very similar CoC and ED relationships can be achieved by appropriate different combinations of L and Smax, but with a differential response of CoC relationships to changes in L (C) and of ED relationships to changes in Smax (D). These differential responses are further documented in Panels (E) and (F).</p

Experimental and BF analysis of CO patterns in grasshopper (<i>Chorthippus bruneus</i>).

<p>(A, B) Experimental data. (A) CoC and ED relationships; (B) Distribution of COs along the bivalent for total COs (left) and for bivalents with either two or three COs with different colors for first, second (and third) COs from the left end of the bivalent. The centromeric region is labeled by a red bar. (C–H) BF simulation analyses. (C) Precursor density (frequency of precursors pre bivalent per interval specified for simulations, where the number of intervals = 17 as for CoC analysis) for BF simulations that used either an even distribution along the chromosome (blue; − Black hole) or a distribution where precursor levels decrease to zero over a region corresponding the paucity of COs in the centromeric region (red; + Black hole; centromeric region defined in Panel B). (D) BF simulations with best-fit parameter values (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004042#pgen-1004042-t002" target="_blank">Table 2</a>) using the two precursor distributions defined in (C), i.e. with or without the centromere region black hole. CoC and ED relationships are the same in both cases (D and E); the distribution of COs along the chromosomes are well-fit when the black hole is included (F) as compared to when it is not (G). (H) BF simulations were used to estimate the likely value of N. CoC relationships seen experimentally (black) were compared by those given by BF simulations that use all best-fit parameter values except that the value of N (precursors per bivalent), which was varied from N = 7 to N = 40 (colors). Left panel: CoC relationships match the experimental curve for any N≥14 (i.e. all curves except gold and green which are N = 7 and N = 10). Right panel: ED relationships are best fit by N = 14 (compare red and black), with less good fits at lower and higher values (right side).</p

Experimental and BF analysis of CO patterns in budding yeast.

<p>Panels (A, B): Experimental System. (A) Spread yeast pachytene chromosomes fluorescently labeled for SC component Zip1, CO-correlated foci of ZMM protein Zip3, and terminally labeled at the end of Chromosome XV by a <i>lacO</i>/LacI-GFP array. (B) Positions of Zip3 foci along a single Chromosome XV bivalent were defined as shown. Panels (C–H): Experimental CO patterns for Chromosome XV. (C) CoC and ED relationships for a single representative Chromosome XV data set reflecting CO positions defined along >300 bivalents (as in (B)). Average CoC curve (black line) shows L_COC = 0.3 µm. (D–G) CoC curves and EDs for four independent experiments like that in (C). (D) shows the four individual average CoC curves; data set from panel (C) in black. (E) shows the four curves from four independent data sets and their average (in red). (F) shows the average of the four average CoC curves with the standard error at each inter-interval distance. (G) Shows the EDs for four independent experiments in (D–F) and the average (in red) with standard error. (H) Compares the average CoC curve and ED for an <i>mlh1</i>Δ mutant (blue) with those for WT (black; average of averages from panels (F) and (G)). Both ED relationships and CoC relationships in the mutant are as WT since Mlh1 acts very late (text). Panels (I–K): BF simulations of CO patterns for Chromosome XV (data from average of averages in panels (F) and (G). (I) Best-fit simulation (red) versus experimental data (black). Best-fit simulation specifies relatively even spacing of precursors (E = 0.6) and a constant number of precursors along the chromosome in all nuclei (B = 1). Other parameter values are in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004042#pgen-1004042-t001" target="_blank">Table 1</a>. (J, K). Experimental data and best-fit simulation data (black and red, from panel (I)) are compared with simulation using the same parameter values as the best-fit simulation except that precursors are either randomly spaced (Panel J; E = 0; green) or Poisson distributed among chromosomes in different nuclei (Panel K; B = 0; blue). Best-fit simulations for Chromosome IV and III data and for chromosome XIV in BR are shown in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004042#pgen.1004042.s005" target="_blank">Figure S5 A–D</a>; parameter values in <a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004042#pgen-1004042-t001" target="_blank">Table 1</a>. Importantly, even spacing is important for the best fit in all cases. In contrast, constant and Poisson distributions give very similar matches to experimental data except for the case of Chromosome III, where constant distribution must be required to ensure a sufficiently low number of zero-CO chromosomes (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004042#pgen.1004042.s005" target="_blank">Figure S5 EF</a>).</p

Descriptors of CO patterns: Coefficient of Coincidence (CoC) and Event Distribution (ED).

<p>(A) Determination of CoC. Interval sizes can be identical or different (<a href="http://www.plosgenetics.org/article/info:doi/10.1371/journal.pgen.1004042#pgen.1004042.s001" target="_blank">Figure S1</a> for more details). (B,C) Data sets were generated by BF simulations at the indicated varying values of parameter (L), also called L_BF. Other specified parameter values for the simulations are: Smax = 3.5, A = 1, cL = cR = 0.85, N = 13, B = 1, E = 0, M = 1. (B) CoC curves. Inter-interval distances given as fractions of total physical chromosome length in µm. The inter-interval distance at which CoC = 0.5 (vertical arrows) is defined as L_CoC. L_CoC and L_BF are always quite similar in magnitude. (C) EDs. Fraction of bivalents exhibiting different numbers of COs and average values.</p

Experimental and BF analysis of CO patterns in Drosophila.

<p>(A, B). Experimental data (black) and BF simulations (red) for the <i>D. melanogaster</i> X chromosome.</p