The habitat functional response links seasonal third‐order selection to second‐order landscape characteristics

Determining how animals respond to differences in resource availabilities across spatiotemporal extents is critical to our understanding of organism distributions. Variations in resource distribution leading to changes in spatial arrangements across landscapes are indicative of a habitat functional response. Our goal was to assess how resource availabilities influenced both second‐order (i.e., home ranging behavior) and third‐order (i.e., habitat or resource selection) selection by feral pigs (Sus scrofa) in an agricultural landscape. We defined agriculturally based seasons to estimate home range characteristics using autocorrelated kernel density estimation within each season. We then modeled home range size as a function of resource availability (i.e., resource selection analyses) to determine whether individual behaviors were predicted by shifts in home ranging behavior. Both home range analyses and resource selection analyses indicated seasonal differences in selection for agricultural resources as availabilities changed, suggesting second‐ and third‐order selection is mechanistically linked through a habitat functional response.     

Second‐order cooperation: Cooperative offspring as a living public good arising from second‐order selection on non‐cooperative individuals

Switching rate between cooperating and non‐cooperating genotypes is a crucial social evolution factor, often neglected by game theory‐inspired theoretical and experimental frameworks. We show that the evolution of alleles increasing the mutation or phenotypic switching rates toward cooperation is in itself a social dilemma. Although cooperative offspring are often unlikely to reproduce, due to high cost of cooperation, they can be seen both as a living public good and a part of the extended parental phenotype. The competition between individuals that generate cooperators and ones that do not is often more relevant than the competition between cooperators and non‐cooperators. The dilemma of second‐order cooperation we describe relates directly to eusociality, but can be also interpreted as a division of labor or a soma‐germline distinction. The results of our simulations shine a new light on what Darwin had already termed a “special difficulty” of evolutionary theory and describe a novel type of cooperation dynamics.     

Dynamic observation and quantification of type I/II collagen in chondrogenesis of mesenchymal stem cells by second‐order susceptibility microscopy

Second‐order susceptibility (SOS) microscopy is used to image and characterize chondrogenesis in cultured human mesenchymal stem cells. SOS analysis shows that the SOS tensor ratios can be used to characterize type I and II collagens in living tissues and that both collagen types are produced at the onset of chondrogenesis. Time‐lapse analysis shows a modulation of extracellular matrix results in a higher rate in increase of type II collagen, as compared to type I collagen. With time, type II collagen content stabilizes at the composition of 70% of total collagen content. SOS microscopy can be used to continuously and noninvasively monitor the production of collagens I and II. With additional development, this technique can be developed into an effective quality control tool for monitoring extracellular matrix production in engineered tissues.      

C‐terminal deletion of leucine‐rich repeats from YopM reveals a heterogeneous distribution of stability in a cooperatively folded protein

Terminal deletions of units from α‐helical repeat proteins have provided insight into the physical origins of their cooperativity. To test if the same principles governing cooperativity apply to β‐sheet‐containing repeat proteins, we have created a series of C‐terminal deletion constructs from a large leucine‐rich repeat (LRR) protein, YopM. We have examined the structure and stability of the resulting deletion constructs by a combination of solution spectroscopy, equilibrium denaturation studies, and limited proteolysis. Surprisingly, a high degree of nonuniformity was found in the stability distribution of YopM. Unlike previously studied repeat proteins, we identified several key LRR that on deletion disrupt nearby structure, at distances as far away as up to three repeats, in YopM. This partial unfolding model is supported by limited proteolysis studies and by point substitution in repeats predicted to be disordered as a result of deletion of adjacent repeats. We show that key internal‐ and terminal‐caps must be present to maintain the structural integrity in adjacent regions (roughly four LRRs long) of decreased stability. The finding that full‐length YopM maintains a high level of cooperativity in equilibrium unfolding underscores the importance of interfacial interactions in stabilizing locally unstable regions of structure.     

Examining post‐translational modification‐mediated protein–protein interactions using a chemical proteomics approach

Post‐translational modifications (PTM) of proteins can control complex and dynamic cellular processes via regulating interactions between key proteins. To understand these regulatory mechanisms, it is critical that we can profile the PTM‐dependent protein–protein interactions. However, identifying these interactions can be very difficult using available approaches, as PTMs can be dynamic and often mediate relatively weak protein–protein interactions. We have recently developed CLASPI (cross‐linking‐assisted and stable isotope labeling in cell culture‐based protein identification), a chemical proteomics approach to examine protein–protein interactions mediated by methylation in human cell lysates. Here, we report three extensions of the CLASPI approach. First, we show that CLASPI can be used to analyze methylation‐dependent protein–protein interactions in lysates of fission yeast, a genetically tractable model organism. For these studies, we examined trimethylated histone H3 lysine‐9 (H3K9Me₃)‐dependent protein–protein interactions. Second, we demonstrate that CLASPI can be used to examine phosphorylation‐dependent protein–protein interactions. In particular, we profile proteins recognizing phosphorylated histone H3 threonine‐3 (H3T3‐Phos), a mitotic histone “mark” appearing exclusively during cell division. Our approach identified survivin, the only known H3T3‐Phos‐binding protein, as well as other proteins, such as MCAK and KIF2A, that are likely to be involved in weak but selective interactions with this histone phosphorylation “mark”. Finally, we demonstrate that the CLASPI approach can be used to study the interplay between histone H3T3‐Phos and trimethylation on the adjacent residue lysine 4 (H3K4Me₃). Together, our findings indicate the CLASPI approach can be broadly applied to profile protein–protein interactions mediated by PTMs.     

Using aggregate data to estimate the standard error of a treatment–covariate interaction in an individual patient data meta‐analysis

Subgroup analyses are important to medical research because they shed light on the heterogeneity of treatment effectts. A treatment–covariate interaction in an individual patient data (IPD) meta‐analysis is the most reliable means to estimate how a subgroup factor modifies a treatment's effectiveness. However, owing to the challenges in collecting participant data, an approach based on aggregate data might be the only option. In these circumstances, it would be useful to assess the relative efficiency and power loss of a subgroup analysis without patient‐level data. We present methods that use aggregate data to estimate the standard error of an IPD meta‐analysis’ treatment–covariate interaction for regression models of a continuous or dichotomous patient outcome. Numerical studies indicate that the estimators have good accuracy. An application to a previously published meta‐regression illustrates the practical utility of the methodology.     

Tardive dyskinesia risk with first‐ and second‐generation antipsychotics in comparative randomized controlled trials: a meta‐analysis

Tardive dyskinesia (TD) risk with D2/serotonin receptor antagonists or D2 receptor partial agonists (second‐generation antipsychotics, SGAs) is considered significantly lower than with D2 antagonists (first‐generation antipsychotics, FGAs). As some reports questioned this notion, we meta‐analyzed randomized controlled studies (RCTs) to estimate the risk ratio (RR) and annualized rate ratio (RaR) of TD comparing SGAs vs. FGAs and SGAs vs. SGAs. Additionally, we calculated raw and annualized pooled TD rates for each antipsychotic. Data from 57 head‐to‐head RCTs, including 32 FGA and 86 SGA arms, were meta‐analyzed, yielding 32 FGA‐SGA pairs and 35 SGA‐SGA pairs. The annualized TD incidence across FGA arms was 6.5% (95% CI: 5.3‐7.8%) vs. 2.6% (95% CI: 2.0‐3.1%) across SGA arms. TD risk and annualized rates were lower with SGAs vs. FGAs (RR=0.47, 95% CI: 0.39‐0.57, p<0.0001, k=28; RaR=0.35, 95% CI: 0.28‐0.45, p<0.0001, number‐needed‐to‐treat, NNT=20). Meta‐regression showed no FGA dose effect on FGA‐SGA comparisons (Z=?1.03, p=0.30). FGA‐SGA TD RaRs differed by SGA comparator (Q=21.8, df=7, p=0.003), with a significant advantage of olanzapine and aripiprazole over other non‐clozapine SGAs in exploratory pairwise comparisons. SGA‐SGA comparisons confirmed the olanzapine advantage vs. non‐clozapine SGAs (RaR=0.66, 95% CI: 0.49‐0.88, p=0.006, k=17, NNT=100). This meta‐analysis confirms a clinically meaningfully lower TD risk with SGAs vs. FGAs, which is not driven by high dose FGA comparators, and documents significant differences with respect to this risk between individual SGAs.     

Multivariate Data Analysis Methodology to Solve Data Challenges Related to Scale‐Up Model Validation and Missing Data on a Micro‐Bioreactor System

Multivariate data analysis (MVDA) is a highly valuable and significantly underutilized resource in biomanufacturing. It offers the opportunity to enhance understanding and leverage useful information from complex high‐dimensional data sets, recorded throughout all stages of therapeutic drug manufacture. To help standardize the application and promote this resource within the biopharmaceutical industry, this paper outlines a novel MVDA methodology describing the necessary steps for efficient and effective data analysis. The MVDA methodology is followed to solve two case studies: a “small data” and a “big data” challenge. In the “small data” example, a large‐scale data set is compared to data from a scale‐down model. This methodology enables a new quantitative metric for equivalence to be established by combining a two one‐sided test with principal component analysis. In the “big data” example, this methodology enables accurate predictions of critical missing data essential to a cloning study performed in the ambr15 system. These predictions are generated by exploiting the underlying relationship between the off‐line missing values and the on‐line measurements through the generation of a partial least squares model. In summary, the proposed MVDA methodology highlights the importance of data pre‐processing, restructuring, and visualization during data analytics to solve complex biopharmaceutical challenges.     

Principal component analysis of chemical shift perturbation data of a multiple‐ligand‐binding system for elucidation of respective binding mechanism

Chemical shift perturbations (CSPs) in NMR spectra provide useful information about the interaction of a protein with its ligands. However, in a multiple‐ligand‐binding system, determining quantitative parameters such as a dissociation constant (K_d) is difficult. Here, we used a method we named CS‐PCA, a principal component analysis (PCA) of chemical shift (CS) data, to analyze the interaction between bovine β‐lactoglobulin (βLG) and 1‐anilinonaphthalene‐8‐sulfonate (ANS), which is a multiple‐ligand‐binding system. The CSP on the binding of ANS involved contributions from two distinct binding sites. PCA of the titration data successfully separated the CSP pattern into contributions from each site. Docking simulations based on the separated CSP patterns provided the structures of βLG–ANS complexes for each binding site. In addition, we determined the K_d values as 3.42 × 10⁻⁴M² and 2.51 × 10⁻³M for Sites 1 and 2, respectively. In contrast, it was difficult to obtain reliable K_d values for respective sites from the isothermal titration calorimetry experiments. Two ANS molecules were found to bind at Site 1 simultaneously, suggesting that the binding occurs cooperatively with a partial unfolding of the βLG structure. On the other hand, the binding of ANS to Site 2 was a simple attachment without a significant conformational change. From the present results, CS‐PCA was confirmed to provide not only the positions and the K_d values of binding sites but also information about the binding mechanism. Thus, it is anticipated to be a general method to investigate protein–ligand interactions. Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Fmoc‐based peptide thioester synthesis with self‐purifying effect: heading to native chemical ligation in parallel formats

The chemical synthesis of proteins has facilitated functional studies of proteins due to the site‐specific incorporation of post‐translational modifications, labels, and non‐proteinogenic amino acids. Moreover, native chemical ligation provides facile access to proteins by chemical means. However, the application of the native chemical ligation reaction in the synthesis of parallel formats such as protein arrays has been complicated because of the often cumbersome and time‐consuming synthesis of the required peptide thioesters. An Fmoc‐based peptide thioester synthesis with self‐purification on the sulfonamide ‘safety‐catch’ linker widens this bottleneck because HPLC purification can be avoided. The method is based on an on‐resin cyclization–thiolysis reaction sequence. A macrocyclization via the N‐terminus of the full‐length peptide followed by a thiolytic C‐terminal ring opening allows selective detachment of the truncation products and the full‐length peptide. A brief overview of the chemical aspects of this method is provided including the optimization steps and the automation process. Furthermore, the application of the cyclization–thiolysis approach combined with the native chemical ligation reaction in the parallel synthesis of a library of 16 SH3‐domain variants of SHO1 in yeast is described, demonstrating the value of this new technique for the chemical synthesis of protein arrays. Copyright © 2013 European Peptide Society and John Wiley & Sons, Ltd.     

Electrostatic interactions play an essential role in the binding of oleic acid with α‐lactalbumin in the HAMLET‐like complex: A study using charge‐specific chemical modifications

H uman α ‐lactalbumin m ade le thal to t umor cells (HAMLET) and its analogs are partially unfolded protein‐oleic acid (OA) complexes that exhibit selective tumoricidal activity normally absent in the native protein itself. To understand the nature of the interaction between protein and OA moieties, charge‐specific chemical modifications of lysine side chains involving citraconylation, acetylation, and guanidination were employed and the biophysical and biological properties were probed. Upon converting the original positively‐charged lysine residues to negatively‐charged citraconyl or neutral acetyl groups, the binding of OA to protein was eliminated, as were any cytotoxic activities towards osteosarcoma cells. Retention of the positive charges by converting lysine residues to homoarginine groups (guanidination); however, yielded unchanged binding of OA to protein and identical tumoricidal activity to that displayed by the wild‐type α‐lactalbumin‐oleic acid complex. With the addition of OA, the wild‐type and guanidinated α‐lactalbumin proteins underwent substantial conformational changes, such as partial unfolding, loss of tertiary structure, but retention of secondary structure. In contrast, no significant conformational changes were observed in the citraconylated and acetylated α‐lactalbumins, most likely because of the absence of OA binding. These results suggest that electrostatic interactions between the positively‐charged basic groups on α‐lactalbumin and the negatively‐charged carboxylate groups on OA molecules play an essential role in the binding of OA to α‐lactalbumin and that these interactions appear to be as important as hydrophobic interactions. Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Using Integrative Modeling Platform to compute,validate, and archive a model of a protein complex structure

Biology is advanced by producing structural models of biological systems, such as protein complexes. Some systems are recalcitrant to traditional structure determination methods. In such cases, it may still be possible to produce useful models by integrative structure determination that depends on simultaneous use of multiple types of data. An ensemble of models that are sufficiently consistent with the data is produced by a structural sampling method guided by a data‐dependent scoring function. The variation in the ensemble of models quantified the uncertainty of the structure, generally resulting from the uncertainty in the input information and actual structural heterogeneity in the samples used to produce the data. Here, we describe how to generate, assess, and interpret ensembles of integrative structural models using our open source Integrative Modeling Platform program ( https://integrativemodeling.org ).     

Using a cross‐model loadings plot to identify protein spots causing 2‐DE gels to become outliers in PCA

The multivariate method PCA is an exploratory tool often used to get an overview of multivariate data, such as the quantified spot volumes of digitized 2‐DE gels. PCA can reveal hidden structures present in the data, and thus enables identification of potential outliers and clustering. Based on PCA, we here present an approach for identification of protein spots causing 2‐DE gels to become outliers. The approach can potentially obviate analytical exclusion of entire 2‐DE gels.     

Hot‐spot analysis to dissect the functional protein–protein interface of a tRNA‐modifying enzyme

Interference with protein–protein interactions of interfaces larger than 1500 Ų by small drug‐like molecules is notoriously difficult, particularly if targeting homodimers. The tRNA modifying enzyme Tgt is only functionally active as a homodimer. Thus, blocking Tgt dimerization is a promising strategy for drug therapy as this protein is key to the development of Shigellosis. Our goal was to identify hot‐spot residues which, upon mutation, result in a predominantly monomeric state of Tgt. The detailed understanding of the spatial location and stability contribution of the individual interaction hot‐spot residues and the plasticity of motifs involved in the interface formation is a crucial prerequisite for the rational identification of drug‐like inhibitors addressing the respective dimerization interface. Using computational analyses, we identified hot‐spot residues that contribute particularly to dimer stability: a cluster of hydrophobic and aromatic residues as well as several salt bridges. This in silico prediction led to the identification of a promising double mutant, which was validated experimentally. Native nano‐ESI mass spectrometry showed that the dimerization of the suggested mutant is largely prevented resulting in a predominantly monomeric state. Crystal structure analysis and enzyme kinetics of the mutant variant further support the evidence for enhanced monomerization and provide first insights into the structural consequences of the dimer destabilization. Proteins 2014; 82:2713–2732. © 2014 Wiley Periodicals, Inc.     

Zero‐modified Poisson model: Bayesian approach,influence diagnostics,and an application to a Brazilian leptospirosis notification data

In this paper, a Bayesian method for inference is developed for the zero‐modified Poisson (ZMP) regression model. This model is very flexible for analyzing count data without requiring any information about inflation or deflation of zeros in the sample. A general class of prior densities based on an information matrix is considered for the model parameters. A sensitivity study to detect influential cases that can change the results is performed based on the Kullback–Leibler divergence. Simulation studies are presented in order to illustrate the performance of the developed methodology. Two real datasets on leptospirosis notification in Bahia State (Brazil) are analyzed using the proposed methodology for the ZMP model.     

Accounting for tagging‐to‐harvest mortality in a Brownie tag‐recovery model by incorporating radio‐telemetry data

The Brownie tag‐recovery model is useful for estimating harvest rates but assumes all tagged individuals survive to the first hunting season; otherwise, mortality between time of tagging and the hunting season will cause the Brownie estimator to be negatively biased. Alternatively, fitting animals with radio transmitters can be used to accurately estimate harvest rate but may be more costly. We developed a joint model to estimate harvest and annual survival rates that combines known‐fate data from animals fitted with transmitters to estimate the probability of surviving the period from capture to the first hunting season, and data from reward‐tagged animals in a Brownie tag‐recovery model. We evaluated bias and precision of the joint estimator, and how to optimally allocate effort between animals fitted with radio transmitters and inexpensive ear tags or leg bands. Tagging‐to‐harvest survival rates from >20 individuals with radio transmitters combined with 50–100 reward tags resulted in an unbiased and precise estimator of harvest rates. In addition, the joint model can test whether transmitters affect an individual's probability of being harvested. We illustrate application of the model using data from wild turkey, Meleagris gallapavo, to estimate harvest rates, and data from white‐tailed deer, Odocoileus virginianus, to evaluate whether the presence of a visible radio transmitter is related to the probability of a deer being harvested. The joint known‐fate tag‐recovery model eliminates the requirement to capture and mark animals immediately prior to the hunting season to obtain accurate and precise estimates of harvest rate. In addition, the joint model can assess whether marking animals with radio transmitters affects the individual's probability of being harvested, caused by hunter selectivity or changes in a marked animal's behavior.     

Slime protein profiling: a non‐invasive tool for species identification in Onychophora (velvet worms)

Onychophorans (velvet worms) use an adhesive, protein‐based slime secretion for prey capture and defence. The glue‐like slime is ejected via a pair of modified limbs and the sticky threads entangle the victim. In this study, we analysed the protein composition of slime in twelve species of Onychophora from different parts of the world, including two species of Peripatidae from Costa Rica and Brazil and ten species of Peripatopsidae from Australia, using sodium dodecyl sulphate polyacrylamide gel electrophoresis. Our results revealed high intraspecific conservation in protein composition of slime in each species studied. In contrast, the protein profiles differ considerably in both number and position of bands between the species. We observed the highest number of differences (in 20 of 33 considered band positions) between a peripatid and a peripatopsid species, whereas the lowest number of differences (in four band positions) occurs between two closely related egg‐laying species. The reconstructed maximum parsimony cladogram based on the electrophoretic characters largely reflects the phylogenetic relationships of the species studied, suggesting that the slime protein profiles contain useful phylogenetic information. Based on our findings, we suggest that the slime protein profiling is a valuable, non‐invasive method for identifying the onychophoran species. Moreover, this method might help to discover potentially new species of Onychophora, given that the ~200 described species most likely underrepresent the actual diversity of the group.