Set Configuration in Resistance Exercise: Muscle Fatigue and Cardiovascular Effects

Purpose

Cardiovascular responses of traditional resistance (TS) training have been extensively explored. However, the fatigue mechanisms associated with an intra-set rest configuration (ISR) have not been investigated. This study compares two modalities of set configurations for resistance exercise that equates work to rest ratios and measures the central and peripheral fatigue in combination with cortical, hemodynamic and cardiovascular measures.

Methods

11 subjects performed two isometric knee extension training sessions using TS and ISR configurations. Voluntary activation (VA), single twitch amplitude, low frequency fatigue (LFF), Mwave, motor evoked potential (MEP), short intracortical inhibition (SICI), intracortical facilitation (ICF) and heart rate variability were evaluated before and after each training session. During each session beat to beat heart rate, blood pressure and rate pressure product (RPP) were also evaluated.

Results

After exercise VA decreased significantly for TS but not for ISR (P < 0.001), single twitch amplitude and LFF values were lower for TS than ISR (P < 0.004), and SICI was reduced only for the TS configuration (P = 0.049). During exercise RPP values were significantly higher for the TS than for ISR (P = 0.001). RPP correlated with VA for TS (r = -.85 P < 0.001) suggesting a relationship between central fatigue and cardiovascular stress.

Conclusions

We conclude that ISR induced lower central and peripheral fatigue as well as lower cardiovascular stress in comparison with TS configuration. Our study suggests that set configuration is a key factor in the regulation of the neuromuscular and cardiovascular responses of resistance training.     

Predicting microbial traits with phylogenies

Phylogeny reflects genetic and phenotypic traits in Bacteria and Archaea. The phylogenetic conservatism of microbial traits has prompted the application of phylogeny-based algorithms to predict unknown trait values of extant taxa based on the traits of their evolutionary relatives to estimate, for instance, rRNA gene copy numbers, gene contents or tolerance to abiotic conditions. Unlike the ‘macrobial'' world, microbial ecologists face scenarios potentially compromising the accuracy of trait reconstruction methods, as, for example, extremely large phylogenies and limited information on the traits of interest. We review 990 bacterial and archaeal traits from the literature and support that phylogenetic trait conservatism is widespread through the tree of life, while revealing that it is generally weak for ecologically relevant phenotypic traits and high for genetically complex traits. We then perform a simulation exercise to assess the accuracy of phylogeny-based trait predictions in common scenarios faced by microbial ecologists. Our simulations show that ca. 60% of the variation in phylogeny-based trait predictions depends on the magnitude of the trait conservatism, the number of species in the tree, the proportion of species with unknown trait values and the mean distance in the tree to the nearest neighbour with a known trait value. Results are similar for both binary and continuous traits. We discuss these results under the light of the reviewed traits and provide recommendations for the use of phylogeny-based trait predictions for microbial ecologists.Trait-based approaches in community ecology studies are becoming increasingly appealing for microbial ecologists partly because metagenomic sequencing allows surveying molecular functions (Green et al., 2008; Lauro et al., 2009; Burke et al., 2011; Raes et al., 2011; Brown et al., 2014; Fierer et al., 2014). Although genetic data can provide precise information on cellular processes or metabolic pathways, they are generally blind to other ecologically relevant phenotypic traits such as the tolerance to certain abiotic conditions or the specific growth rate (but see Vieira-Silva and Rocha, 2010). Unlike ‘macrobial'' ecologists, who can directly observe phenotypic characters of plants and animals, microbial ecologists usually face situations where most of the phenotypes of their study organisms are unknown. This difficulty relies on the fact that gathering phenotypic (physiological, morphological, biochemical) data requires culturing microbial species. The unbalanced growth of genotypic vs phenotypic information is currently challenging microbial ecologists to work with phylogenetic trees of increasing size (hundreds to thousands of species) in which the percentage of species with unknown traits becomes larger and larger.Recent evidence indicate that phylogeny reflects molecular functions and phenotypes in Bacteria and Archaea (Langille et al., 2013; Martiny et al., 2013). This is due to the phylogenetic conservatism of microbial traits (Martiny et al., 2013), which likely arises from microbial evolution mostly proceeding by vertical gene inheritance rather than horizontal gene transfer (Kurland et al., 2003, see Fraser et al., 2007 for theoretical models on the role of horizontal gene transfer in bacterial speciation). At present, the massive sequencing of microbes in the environment is providing a huge amount of genetic information that is extremely useful to reconstruct the phylogenetic relationships among microbial lineages. This fact has triggered the interest of microbial ecologists to apply the methods developed to predict unobserved trait values of extant taxa based on the traits observed in their evolutionary relatives (Kembel et al., 2012; Langille et al., 2013; Angly et al., 2014, see review in Zaneveld and Thurber, 2014). All these methods are based on the existence of a significant phylogenetic signal or, in other words, in the fact that close relatives have more similar traits than expected by chance. Phylogeny-based trait prediction procedures (PTP hereafter) in microbes have been mainly performed under the phylogenetic generalized least squares framework (Martins and Hansen, 1997; Garland and Ives, 2000). Specifically, the trait value (for continuous traits) or state (for binary traits) of the focal species have been reconstructed through ancestral state reconstructions after rerooting the phylogeny at the most recent common ancestor of the taxon with unobserved trait and the rest of the tree (Kembel et al., 2012). The accuracy of PTP methods has been typically assessed under ‘macrobial'' scenarios containing phylogenies of moderate size, with low-to-medium proportion of species with unknown traits and significant phylogenetic signals. For example, Fagan et al. (2013) predicted population growth rates of mammals in phylogenies of 42–65 species containing 54–64% of unknowns and a significant phylogenetic signal (Blomberg et al., 2003; Blomberg''s K) ranging from 0.68 to 1.42. However, the current microbial scenarios derived from high-throughput sequencing projects face large-sized phylogenies (hundreds to thousands tips) with a high number of species with unknown traits and varying phylogenetic signals jeopardizing the applicability of PTP methods (Zaneveld and Thurber, 2014).The extent to which phylogeny reflects phenotype is strongly dependent on the degree of conservatism with which the focal trait has evolved. For instance, complex traits that involve many genes (for example, photosynthesis or methanogenesis) show higher conservatism than simpler traits, such as the consumption of a specific carbon source (Martiny et al., 2013). Furthermore, certain traits such as those related to genes encoding antibiotic or metal resistance are particularly prone to be horizontally transferred (Bruins et al., 2000), a process that can blur their phylogenetic signal. Therefore, if phylogenetic relatedness is to be used to infer the phenotype, the phylogenetic conservatism of the target trait needs to be quantified in every case.Altogether, the abovementioned observations indicate that the possibility to estimate phenotypes from phylogenies depends on the amount of phylogenetic and phenotypic information available to predict the unobserved trait values. Here we provide a simulation exercise to test the accuracy of the most widely used PTP method in microbial ecology to predict continuous trait values and binary trait states of extant taxa with different amount of phenotypic and phylogenetic information. We simulated several situations faced by microbial ecologists, including phylogenies of different sizes in which a small (P=0.3), medium (P=0.6) or large (P=0.9) proportion of species have unknown trait values. The correlations between the actual and the predicted trait values were obtained for characters evolved under different degree of conservatism. Finally, we put these values in the context of the phylogenetic signals described in the literature for different continuous and binary microbial traits and provide some recommendations for future analyses aimed to predict microbial traits with the help of the phylogenetic information.     

Separable Roles for a Caenorhabditis elegans RMI1 Homolog in Promoting and Antagonizing Meiotic Crossovers Ensure Faithful Chromosome Inheritance

During the first meiotic division, crossovers (COs) between homologous chromosomes ensure their correct segregation. COs are produced by homologous recombination (HR)-mediated repair of programmed DNA double strand breaks (DSBs). As more DSBs are induced than COs, mechanisms are required to establish a regulated number of COs and to repair remaining intermediates as non-crossovers (NCOs). We show that the Caenorhabditis elegans RMI1 homolog-1 (RMH-1) functions during meiosis to promote both CO and NCO HR at appropriate chromosomal sites. RMH-1 accumulates at CO sites, dependent on known pro-CO factors, and acts to promote CO designation and enforce the CO outcome of HR-intermediate resolution. RMH-1 also localizes at NCO sites and functions in parallel with SMC-5 to antagonize excess HR-based connections between chromosomes. Moreover, RMH-1 also has a major role in channeling DSBs into an NCO HR outcome near the centers of chromosomes, thereby ensuring that COs form predominantly at off-center positions.     

Microhabitat selection in the common lizard: implications of biotic interactions,age, sex,local processes,and model transferability among populations

Modeling species' habitat requirements are crucial to assess impacts of global change, for conservation efforts and to test mechanisms driving species presence. While the influence of abiotic factors has been widely examined, the importance of biotic factors and biotic interactions, and the potential implications of local processes are not well understood. Testing their importance requires additional knowledge and analyses at local habitat scale. Here, we recorded the locations of species presence at the microhabitat scale and measured abiotic and biotic parameters in three different common lizard (Zootoca vivipara) populations using a standardized sampling protocol. Thereafter, space use models and cross‐evaluations among populations were run to infer local processes and estimate the importance of biotic parameters, biotic interactions, sex, and age. Biotic parameters explained more variation than abiotic parameters, and intraspecific interactions significantly predicted the spatial distribution. Significant differences among populations in the relationship between abiotic parameters and lizard distribution, and the greater model transferability within populations than between populations are in line with effects predicted by local adaptation and/or phenotypic plasticity. These results underline the importance of including biotic parameters and biotic interactions in space use models at the population level. There were significant differences in space use between sexes, and between adults and yearlings, the latter showing no association with the measured parameters. Consequently, predictive habitat models at the population level taking into account different sexes and age classes are required to understand a specie's ecological requirements and to allow for precise conservation strategies. Our study therefore stresses that future predictive habitat models at the population level and their transferability should take these parameters into account.     

Host‐plant associated genetic divergence of two Diatraea spp. (Lepidoptera: Crambidae) stemborers on novel crop plants

Diatraea lineolata and Diatraea saccharalis (Lepidoptera: Crambidae) are moths with stemboring larvae that feed and develop on economically important grasses. This study investigated whether these moths have diverged from a native host plant, corn, onto introduced crop plants including sorghum, sugarcane, and rice. Diatraea larvae were collected from these four host plants throughout the year in El Salvador and were reared on artificial diet until moths or parasitoids emerged. Adult moths were subsequently identified to species. Amplified fragment length polymorphisms (AFLPs) and mitochondrial DNA cytochrome oxidase I (COI) were used to examine whether or not there was genetic divergence of D. lineolata or D. saccharalis populations on the four host plants. Percent parasitism was also determined for each moth on its host plants. D. lineolata was collected from corn in the rainy season and sorghum in the dry season. D. saccharalis was most abundant on sugarcane in the rainy season and sorghum in the dry season. The AFLP analysis found two genetically divergent populations of both D. lineolata and D. saccharalis. Both moths had high levels of parasitism on their dominant host plant in the rainy season, yet had low levels of parasitism on sorghum in the dry season. The presence of two genotypes of both Diatraea spp. on sorghum suggest that host‐associated differentiation is occurring on this novel introduced crop plant.     

Evidence of Inbreeding in Hodgkin Lymphoma

Genome-wide association studies (GWASs) have identified several, mainly co-dominantly acting, single-nucleotide polymorphisms (SNPs) associated with Hodgkin lymphoma (HL). We searched for recessively acting disease loci by performing an analysis of runs of homozygosity (ROH) based on windows of homozygous SNP-blocks and by calculating genomic inbreeding coefficients on a SNP-wise basis. We used data from a previous GWAS with 906 cases and 1217 controls from a population with a long history of no matings between relatives. Ten recurrent ROHs were identified among 25 055 ROHs across all individuals but their association with HL was not genome-wide significant. All recurrent ROHs showed significant evidence for natural selection. As a novel finding genomic inbreeding among cases was significantly higher than among controls (P = 2.11*10⁻¹⁴) even after correcting for covariates. Higher inbreeding among the cases was mainly based on a group of individuals with a higher average length of ROHs per person. This result suggests a correlation of higher levels of inbreeding with higher cancer incidence and might reflect the existence of recessive alleles causing HL. Genomic inbreeding may result in a higher expression of deleterious recessive genes within a population.