Genomewide patterns of variation in genetic diversity are shared among populations,species and higher‐order taxa

Genomewide screens of genetic variation within and between populations can reveal signatures of selection implicated in adaptation and speciation. Genomic regions with low genetic diversity and elevated differentiation reflective of locally reduced effective population sizes (N_e) are candidates for barrier loci contributing to population divergence. Yet, such candidate genomic regions need not arise as a result of selection promoting adaptation or advancing reproductive isolation. Linked selection unrelated to lineage‐specific adaptation or population divergence can generate comparable signatures. It is challenging to distinguish between these processes, particularly when diverging populations share ancestral genetic variation. In this study, we took a comparative approach using population assemblages from distant clades assessing genomic parallelism of variation in N_e. Utilizing population‐level polymorphism data from 444 resequenced genomes of three avian clades spanning 50 million years of evolution, we tested whether population genetic summary statistics reflecting genomewide variation in N_e would covary among populations within clades, and importantly, also among clades where lineage sorting has been completed. All statistics including population‐scaled recombination rate (ρ), nucleotide diversity (π) and measures of genetic differentiation between populations (F_ST, PBS, d_xy) were significantly correlated across all phylogenetic distances. Moreover, genomic regions with elevated levels of genetic differentiation were associated with inferred pericentromeric and subtelomeric regions. The phylogenetic stability of diversity landscapes and stable association with genomic features support a role of linked selection not necessarily associated with adaptation and speciation in shaping patterns of genomewide heterogeneity in genetic diversity.     

Transforming growth factor-β protein inversely regulates in vivo differentiation of interleukin-17 (IL-17)-producing CD4+ and CD8+ T cells

TGF-β is a pleiotropic cytokine that predominantly exerts inhibitory functions in the immune system. Unexpectedly, the in vitro differentiation of both Th17 and Tc17 cells requires TGF-β. However, animals that are impaired in TGF-β signaling (TGF-βRIIDN mice) display multiorgan autoimmune disorders. Here we show that CD4(+) T cells from TGF-βRIIDN mice are resistant to Th17 cell differentiation and, paradoxically, that CD8(+) T cells from these animals spontaneously acquire an IL-17-producing phenotype. Neutralization of IL-17 or depletion of CD8(+) T cells dramatically inhibited inflammation in TGF-βRIIDN mice. Therefore, the absence of TGF-β triggers spontaneous differentiation of IL-17-producing CD8(+) T cells, suggesting that the in vivo and in vitro conditions that promote the differentiation of IL-17-producing CD8(+) T cells are distinct.     

MSMBuilder: Statistical Models for Biomolecular Dynamics

MSMBuilder is a software package for building statistical models of high-dimensional time-series data. It is designed with a particular focus on the analysis of atomistic simulations of biomolecular dynamics such as protein folding and conformational change. MSMBuilder is named for its ability to construct Markov state models (MSMs), a class of models that has gained favor among computational biophysicists. In addition to both well-established and newer MSM methods, the package includes complementary algorithms for understanding time-series data such as hidden Markov models and time-structure based independent component analysis. MSMBuilder boasts an easy to use command-line interface, as well as clear and consistent abstractions through its Python application programming interface. MSMBuilder was developed with careful consideration for compatibility with the broader machine learning community by following the design of scikit-learn. The package is used primarily by practitioners of molecular dynamics, but is just as applicable to other computational or experimental time-series measurements.     

Effects of Familial Mutations on the Monomer Structure of Aβ42

Amyloid beta (Aβ) peptide plays an important role in Alzheimer’s disease. A number of mutations in the Aβ sequence lead to familial Alzheimer’s disease, congophilic amyloid angiopathy, or hereditary cerebral hemorrhage with amyloid. Using molecular dynamics simulations of ∼200 μs for each system, we characterize and contrast the consequences of four pathogenic mutations (Italian, Dutch, Arctic, and Iowa) for the structural ensemble of the Aβ monomer. The four familial mutations are found to have distinct consequences for the monomer structure.Amyloid beta (Aβ) peptides have long been thought to play a central role in Alzheimer’s disease (AD). Usually 40 or 42 residues in length, Aβ peptides are proteolytic products of the Aβ precursor protein and they aggregate to form the fibrillar plaques in AD patients’ brains. Besides fibrillar plaques, Aβ oligomers are also neurotoxic. The significance and nature of Aβ oligomerization has recently become a focus of intensive research studies and debates (1,2). Notably, numerous pathogenic mutations have been identified in the Aβ precursor protein sequence and in the enzymes involved in Aβ processing (3). These mutations generally lead to early onset of AD or cerebral amyloid angiopathy. Understanding how the pathogenic mutations alter Aβ oligomerization/aggregation is essential to our understanding of the disease mechanism.Four of these pathogenic mutations (Italian E22K, Dutch E22Q, Arctic E22G, and Iowa D23N) cluster in the region of E22 and D23 in the Aβ sequence (distal from proteolytic cleavage sites) and they have higher neurotoxicity compared to wild-type (WT) Aβ (4). These mutations are thought to modify the physicochemistry of the peptide. For example, kinetic studies (4) show that the E22K and E22Q mutations lead to faster peptide aggregation, whereas the E22G and D23N mutations result in slightly slower aggregation than WT Aβ₄₂ (although the E22G mutation shows increased protofibril formation (5)). Recent solid-state NMR studies also suggest that rather than the in-register β-sheet conformation adopted by WT Aβ, the Iowa D23N mutant forms amyloid fibrils with antiparallel β-sheet structure (6).To understand how the mutations modify the peptide oligomerization/aggregation it is critical to characterize the starting point of the process, the monomers. Unfortunately, investigating the early phase of the oligomerization process experimentally is a challenging task due to the high aggregation propensity of Aβ and its intrinsic disorder. Therefore, a number of computational approaches have been adopted to investigate the consequences of mutations for the monomer structure (7–16). However, due to the high computational demands of explicit-solvent molecular dynamics (MD) simulations to simulate full-length Aβ peptides, most of these computational studies are either on Aβ fragments (to decrease the system size) using explicit-solvent simulations (8–12) or on full-length Aβ using implicit-solvent simulations (which are less computationally demanding and enable longer simulation times, but lack explicit water molecules in the simulations to fully describe water-peptide interactions) (13–15). In a very recent report, explicit-solvent simulations were used to study the effects of the E22Q mutation on full-length Aβ; however, rather limited data (<10 μs) were collected (16). Thus, characterizing full-length Aβ monomers remains quite a daunting task even with simulations.To characterize the effects of mutations on full-length Aβ monomer using explicit-solvent MD simulations, we employed distributed computing (17) to simulate the WT Aβ₄₂, Aβ₄₂-E22K, Aβ₄₂-E22Q, Aβ₄₂-E22G, and Aβ₄₂-D23N monomers. MD simulations of >200 μs were performed for each system and AMBER ff99sb (18) and the tip3p water model (19) were used for force field parameters. Peptide configurations in the MD trajectories were clustered with the root mean-square deviation metric to identify representative conformations (i.e., states) and transitions between these states were counted. Markov state model analysis was then performed where the master equations were solved and the equilibrium population of each state deduced (20). Details of the MD simulation procedures and Markov state model analysis can be found in the Supporting Material.Each of the five Aβ monomer systems exhibits great structural diversity and can only be characterized in an ensemble fashion (rather than described by a handful of representative configurations). This is in accord with the notion that full-length Aβ peptides are intrinsically disordered (21,22). Using the Dictionary of Secondary Structure of Proteins program (23) to assign secondary structure, it is clear that the five Aβ monomer systems are found overall not well structured, although small β-hairpins and α-helices are observed. In Fig. 1 we plot the residue-dependent extended β propensity and α-helix propensity, in the top and bottom panels, respectively, for each Aβ monomer system. Although we are reasonably confident of the convergence behavior of the α-helix propensity, we note that the convergence of the extended β-propensity might be more challenging and demand a much longer sampling time than the current aggregate simulation time of ∼200 μs (24).Open in a separate windowFigure 1Ensemble-averaged %population of β-strand (top) and α-helix (bottom) propensity for all five monomer systems. The sequence of the WT Aβ₄₂ is given on the x axis.We observe in Fig. 1 that all five Aβ monomer systems share a rather similar residue-dependent tendency to form an extended β-structure, although minor differences are present. On the other hand, these pathogenic mutations alter the α-helix propensity quite significantly. The E22K and E22Q mutations increase the α-helix propensity in the region of residues 20–23. All four mutations (E22K, E22Q, E22G, and D23N) decrease the α-helix propensity in the region of residues 33–36.Notably, we find that in all five systems only short stretches of α-helices are formed. That is, when a residue is involved in α-helix formation, it participates in forming mostly short helical segments (consisting of only four helical residues). To provide more insight into the changes of α-helix propensity due to the mutations, in Fig. S1 we plot the tendency of forming short α-helices along the sequence for all five systems. Each data point in Fig. S1 represents the propensity to form an α-helix of four residues in length, ending at the specific residue. For example, in the structural ensemble adopted by the WT peptide, ∼5.5% of the conformations have a short α-helix of size four, involving residues 15–18. We see from Fig. S1 that the E22K and E22Q mutations induce the formation of two short helices in residues 19–22 and 20–23. The higher α-helix propensity in this region for the E22K mutant compared to the WT was previously attributed to the elimination of the electrostatic repulsion between E22 and D23 in the WT by the mutation and the longer aliphatic chain of K22 in the mutant compared to E22 in the WT (9,22). This is consistent with the observation that the E22Q mutation also induces helix formation in this region (by eliminating the electrostatic repulsion between E22 and D23 in the WT) but to a lesser extent, possibly due to the shorter aliphatic chain of Q22 compared to K22.In the E22G mutant, although the mutation eliminates the electrostatic repulsion between E22 and D23 in the WT peptide, glycine is known to be a helix breaker (25), leading to diminished α-helix propensity in the region around residue G22 seen in Fig. S1.In the D23N mutant, although the mutation eliminates the electrostatic repulsion between E22 and D23 in the WT peptide, it does not induce (or rather even slightly decreases) helix formation around residue 23. This may be due to the short aliphatic chain of N23 but it is possible that the mutation induces some nonlocal effects on the peptide structure, disfavoring helix formation in this region.It is worth noting that all four mutations (E22K, E22Q, E22G, and D23N) virtually eliminate the α-helix propensity in the region of residues 33–36. This region is rather far away from the mutation sites in sequence but its α-helix propensity is nonetheless affected. The origin of such a nonlocal effect is less straightforward to explain and further analysis will aid untangling this behavior. Nonetheless, the diminished α-helix propensity in the region of residues 33–36 appears to be a consistent feature across all four mutants.The four mutations studied here (E22K, E22Q, E22G, and D23N) have been thought to modify the physicochemistry of the peptide and alter the oligomerization/aggregation process, leading to higher neurotoxicity. In predicting intrinsic aggregation propensities using peptide sequences, all four mutants are suggested to be more aggregation prone (26). On the other hand, kinetic studies show that only the E22K and E22Q mutants aggregate more quickly, whereas the E22G and D23N mutations result in slightly slower aggregation than WT Aβ₄₂ (4). Our simulation results suggest these pathogenic mutations have complicated effects on the monomer structure—all four mutations decrease helix propensity in residues 33–36, whereas only the E22K and E22Q mutations increase helix propensity in residues 20–23. It is interesting to note that α-helix propensity is generally thought to anticorrelate with aggregation propensity; however, recent studies have suggested an important role of α-helical intermediates in amyloid oligomerization (27–29). Our studies suggest that it would be of great value to investigate how the distinct patterns of α-helix propensity in these five systems may propagate to give rise to different oligomerization kinetics or even mechanisms. The pathogenic mutations studied here have complex effects on the oligomerization of the peptide. The characterization of the monomer structural ensembles reported here should aid understanding of such an important and complicated process.     

Autonomic Cardiovascular Responses in Acclimatized Lowlanders on Prolonged Stay at High Altitude: A Longitudinal Follow Up Study

Acute exposure to hypobaric hypoxia at high altitude is reported to cause sympathetic dominance that may contribute to the pathophysiology of high altitude illnesses. The effect of prolonged stay at high altitude on autonomic functions, however, remains to be explored. Thus, the present study aimed at investigating the effect of high altitude on autonomic neural control of cardiovascular responses by monitoring heart rate variability (HRV) during chronic hypobaric hypoxia. Baseline electrocardiography (ECG) data was acquired from the volunteers at mean sea level (MSL) (<250 m) in Rajasthan. Following induction of the study population to high altitude (4500–4800 m) in Ladakh region, ECG data was acquired from the volunteers after 6 months (ALL 6) and 18 months of induction (ALL 18). Out of 159 volunteers who underwent complete investigation during acquisition of baseline data, we have only included the data of 104 volunteers who constantly stayed at high altitude for 18 months to complete the final follow up after 18 months. HRV parameters, physiological indices and biochemical changes in serum were investigated. Our results show sympathetic hyperactivation along with compromise in parasympathetic activity in ALL 6 and ALL 18 when compared to baseline data. Reduction of sympathetic activity and increased parasympathetic response was however observed in ALL 18 when compared to ALL 6. Our findings suggest that autonomic response is regulated by two distinct mechanisms in the ALL 6 and ALL 18. While the autonomic alterations in the ALL 6 group could be attributed to increased sympathetic activity resulting from increased plasma catecholamine concentration, the sympathetic activity in ALL 18 group is associated with increased concentration of serum coronary risk factors and elevated homocysteine. These findings have important clinical implications in assessment of susceptibility to cardio-vascular risks in acclimatized lowlanders staying for prolonged duration at high altitude.     

The ability of an attaching and effacing pathogen to trigger localized actin assembly contributes to virulence by promoting mucosal attachment

Enterohaemorrhagic Escherichia coli (EHEC) colonizes the intestine and causes bloody diarrhoea and kidney failure by producing Shiga toxin. Upon binding intestinal cells, EHEC triggers a change in host cell shape, generating actin ‘pedestals’ beneath bound bacteria. To investigate the importance of pedestal formation to disease, we infected genetically engineered mice incapable of supporting pedestal formation by an EHEC‐like mouse pathogen, or wild type mice with a mutant of that pathogen incapable of generating pedestals. We found that pedestal formation promotes attachment of bacteria to the intestinal mucosa and vastly increases the severity of Shiga toxin‐mediated disease.     

Amblyomma americanum salivary gland homolog of nSec1 is essential for saliva protein secretion

Soluble N-ethylmaleimide-sensitive factor attachment protein receptor proteins assemble in tight core complexes which promote fusion of carrier vesicles with target compartments. Members of this class of proteins are expressed in all eukaryotic cells and distributed in distinct subcellular compartments. All vesicle transport mechanisms known to date have an essential requirement for a member of the Sec1 protein family, including the nSec1 in regulated exocytosis. A homolog of nSec1 was cloned and sequenced from the salivary glands of partially fed female ticks. Double-stranded RNA was used to specifically reduce the amount of nSec1 mRNA and protein in female adult tick salivary glands. This reduction was accompanied by a decrease in anticoagulant protein release by the glands and by abnormalities in feeding by dsRNA treated ticks. We report the efficacy of double-stranded RNA-mediated interference in "knocking down" nSec1 both in vivo and in vitro in tick salivary glands and the applicability of this technique for studying the mechanism of exocytosis in tick salivary glands.     

pH-responsive sulfonamide/PEI system for tumor specific gene delivery: an in vitro study

The feasibility of pH-sensitive polymeric nanoparticles that effectively target the acidic extracellular matrix of tumors is demonstrated. Plasmid DNA was complexed with polyethyleneimine (PEI) and further with a pH-sensitive diblock copolymer, poly(methacryloyl sulfadimethoxine) (PSD)-block-PEG (PSD-b-PEG), to obtain naonparticles. The shielding/deshielding of nanoparticles was tested along with cell viability and transfection efficiency at physiological and tumor pH. The nanoparticles composed of DNA/PEI/PSD-b-PEG were 300 nm in size and showed low cytotoxicity and transfection at pH 7.4 due to shielding of PEI by PSD-b-PEG. The PSD-b-PEG bound to PEI/DNA complex decreased the interaction of PEI positive charges with cells and reduced the cytotoxicity by 60%. At pH 6.6, the nanoparticles demonstrated high cytotoxicity and transfection, indicating PSD-b-PEG detachment from the nanoparticles and permit PEI to interact with cells. PSD-b-PEG is able to discern the small difference in pH between normal and tumor tissues and hence has remarkable potential in drug targeting to tumor areas.     

Sequence coevolution between RNA and protein characterized by mutual information between residue triplets

Coevolving residues in a multiple sequence alignment provide evolutionary clues of biophysical interactions in 3D structure. Despite a rich literature describing amino acid coevolution within or between proteins and nucleic acid coevolution within RNA, to date there has been no direct evidence of coevolution between protein and RNA. The ribosome, a structurally conserved macromolecular machine composed of over 50 interacting protein and RNA chains, provides a natural example of RNA/protein interactions that likely coevolved. We provide the first direct evidence of RNA/protein coevolution by characterizing the mutual information in residue triplets from a multiple sequence alignment of ribosomal protein L22 and neighboring 23S RNA. We define residue triplets as three positions in the multiple sequence alignment, where one position is from the 23S RNA and two positions are from the L22 protein. We show that residue triplets with high mutual information are more likely than residue doublets to be proximal in 3D space. Some high mutual information residue triplets cluster in a connected series across the L22 protein structure, similar to patterns seen in protein coevolution. We also describe RNA nucleotides for which switching from one nucleotide to another (or between purines and pyrimidines) results in a change in amino acid distribution for proximal amino acid positions. Multiple crystal structures for evolutionarily distinct ribosome species can provide structural evidence for these differences. For one residue triplet, a pyrimidine in one species is a purine in another, and RNA/protein hydrogen bonds are present in one species but not the other. The results provide the first direct evidence of RNA/protein coevolution by using higher order mutual information, suggesting that biophysical constraints on interacting RNA and protein chains are indeed a driving force in their evolution.