The Behavior of Molecular Measures of Natural Selection after a Change in Population Size

ABSTRACTA common model to describe natural selection at the molecular level is the nearly neutral theory, which emphasizes the importance of mutations with slightly deleterious fitness effects as they have a chance to get fixed due to genetic drift. Since genetic drift is stronger in smaller than in larger populations, a negative relationship between molecular measures of selection and population size is expected within the nearly neutral theory. Originally, this hypothesis was formulated under equilibrium conditions. A change in population size, however, pushes the selection-drift balance off equilibrium leading to alterations in the efficacy of selection. To investigate the nonequilibrium behavior, we relate measures of natural selection and genetic drift to each other, considering both, measures of micro- and macroevolution. Specifically, we use a Poisson random field framework to model πN/πS and ω as time-dependent measures of selection and assess genetic drift by an effective population size. This analysis reveals a clear deviation from the expected equilibrium selection-drift balance during nonequilibrium periods. Moreover, we find that microevolutionary measures quickly react to a change in population size and reflect a recent change well, at the same time as they quickly lose the knowledge about it. Macroevolutionary measures, on the other hand, react more slowly to a change in population size but instead capture the influence of ancient changes longer. We therefore conclude that it is important to be aware of the different behaviors of micro- and macroevo- lutionary measures when making inference in empirical studies, in particular when comparing results between studies.

Cortical gyrification and its relationships with molecular measures and cognition in children with the FMR1 premutation

Neurobiological basis for cognitive development and psychiatric conditions remains unexplored in children with the FMR1 premutation (PM). Knock-in mouse models of PM revealed defects in embryonic cortical development that may affect cortical folding. Cortical-folding complexity quantified using local gyrification index (LGI) was examined in 61 children (age 8–12 years, 19/14 male/female PM carriers, 15/13 male/female controls). Whole-brain vertex-wise analysis of LGI was performed for group comparisons and correlations with IQ. Individuals with aberrant gyrification in 68 cortical areas were identified using Z-scores of LGI (hyper: Z ≥ 2.58, hypo: Z ≤ − 2.58). Significant group-by-sex-by-age interaction in LGI was detected in right inferior temporal and fusiform cortices, which correlated negatively with CGG repeat length in the PM carriers. Sixteen PM boys (hyper/hypo: 7/9) and 10 PM girls (hyper/hypo: 2/5, 3 both) displayed aberrant LGI in 1–17 regions/person while 2 control boys (hyper/hypo: 0/2) and 2 control girls (hyper/hypo: 1/1) met the same criteria in only 1 region/person. LGI in the precuneus and cingulate cortices correlated positively with IQ scores in PM and control boys while negatively in PM girls and no significant correlation in control girls. These findings reveal aberrant gyrification, which may underlie cognitive performance in children with the PM.

EpiMetal: an open-source graphical web browser tool for easy statistical analyses in epidemiology and metabolomics

Motivation An intuitive graphical interface that allows statistical analyses and visualizations of extensive data without any knowledge of dedicated statistical software or programming. Implementation EpiMetal is a single-page web application written in JavaScript, to be used via a modern desktop web browser. General features Standard epidemiological analyses and self-organizing maps for data-driven metabolic profiling are included. Multiple extensive datasets with an arbitrary number of continuous and category variables can be integrated with the software. Any snapshot of the analyses can be saved and shared with others via a www-link. We demonstrate the usage of EpiMetal using pilot data with over 500 quantitative molecular measures for each sample as well as in two large-scale epidemiological cohorts (N >10 000). Availability The software usage exemplar and the pilot data are open access online at [http://EpiMetal.computationalmedicine.fi]. MIT licensed source code is available at the Github repository at [https://github.com/amergin/epimetal].

Overexpression of GmCAMTA12 enhanced drought tolerance in Arabidopsis and Soybean

Background: Earlier, fifteen transcription factors in the CAMTA family of soybean were reported to differentially express in multiple stresses however, their functional analyses had not yet been attempted. To characterize their role in drought stress, we comprehensively analyzed GmCAMTA family in silico and determined their expression pattern after which we cloned and overexpressed the 2769 bp CDS of GmCAMTA12 in Arabidopsis and soybean and carried out drought assays. Results: The bioinformatics analysis revealed multiple stress-related cis motifs including ABRE, SARE, G-box and W-box, 10 unique miRNA targets in GmCAMTA transcripts and 48 proteins in GmCAMTAs’ interaction network. The stably transformed homozygous lines of Arabidopsis overexpressing GmCAMTA12 exhibited enhanced tolerance to drought in soil as well as on MS media containing mannitol. In their drought assay, the average survival rate of transgenic Arabidopsis lines OE5 and OE12 (Overexpression Line 5 and Line 12) was 83.66% and 87.87% respectively, which was ~30% higher than that of wt. In addition, the germination and root length assays as well as physiological indexes such as proline and MDA contents, CAT activity and leakage of electrolytes affirmed the better performance of OE lines. Likewise, GmCAMTA12 overexpression in soybean promoted more developed hairy roots in OE chimeric plants as compare to that of VC (Vector control). In parallel, the improved growth performance of OE in Hoagland-PEG and on MS-mannitol was revealed by their phenotypic, physiological and molecular measures. Furthermore, with the overexpression of GmCAMTA12, the downstream genes including AtAnnexin5, AtCaMHSP, At2G433110 and AtWRKY14 were upregulated in Arabidopsis. Likewise, in soybean hairy roots, GmELO, GmNAB and GmPLA1-IId were significantly upregulated as a result of GmCAMTA12 overexpression and majority of these upregulated genes in both plants possess CAMTA binding CGCG/CGTG motif in their promoters. Conclusions: Taken together, we report that GmCAMTA12 plays substantial role in tolerance of soybean against drought stress and could prove as a novel candidate for engineering of soybean and other plants against drought stress. Some research gaps were also identified for future studies to extend our comprehension of Ca-CaM-CAMTA-mediated stress regulatory mechanisms.

Developments in molecular epidemiology of aging

The field of molecular epidemiology of aging involves the application of molecular methods to measure aging processes and their genetic determinants in human cohorts. Over the last decade, the field has undergone rapid progress with a dramatic increase in the number of papers published. The aim of this review is to give an overview of the research field, with a specific focus on new developments, opportunities, and challenges. Aging occurs at multiple hierarchical levels. There is increasing consensus that aging-related changes at the molecular level cause declines in physiological integrity, functional capacity, and ultimately lifespan. Molecular epidemiology studies seek to quantify this process. Telomere length, composite scores integrating clinical biomarkers, and omics clocks are among the most well-studied metrics in molecular epidemiology studies. New developments in the field include bigger data and hypothesis-free analysis together with new modes of collaborations in interdisciplinary teams and open access norms around data sharing. Key challenges facing the field are the lack of a gold standard by which to evaluate molecular measures of aging, inconsistency in which metrics of aging are measured and analyzed across studies, and a need for more longitudinal data necessary to observe change over time.