In the present study we used regression analyses to evaluate the effects of stearic acid (18:0) on total cholesterol (TC), low-density-lipoprotein-cholesterol (LDL-C), and high-density-lipoprotein-cholesterol (HDL-C) concentrations (mmol/L). Using data from 18 articles, we developed the following predictive equations (monounsaturated fatty acids, MUFAs; polyunsaturated fatty acids, PUFAs): delta TC = 0.0522 delta 12:0-16:0 - 0.0008 delta 18:0 - 0.0124 delta MUFA - 0.0248 delta PUFA; delta LDL-C = 0.0378 delta 12:0-16:0 + 0.0018 delta 18:0 - 0.0178 delta MUFA - 0.0248 delta PUFA; delta HDL-C = 0.0160 delta 12:0-16:0 - 0.0016 delta 18:0 + 0.0101 delta MUFA + 0.0062 delta PUFA. Our analyses revealed that unlike the other long-chain saturated fatty acids (SFAs), stearic acid had no effect on TC and lipoprotein cholesterol concentrations in men and women. MUFAs elicited an independent hypocholesterolemic effect that we believe is due to the small amount of 12:0-16:0 in the experimental diets evaluated. The observation that stearic acid has unique effects on TC, LDL-C, and HDL-C provides additional compelling evidence that it be distinguished from the other major SFAs in blood cholesterol predictive equations.

Protein sequences evolve under selection pressures imposed by functional and biophysical requirements, resulting in site-dependent rates of amino acid substitution. Relative solvent accessibility (RSA) and local packing density (LPD) have emerged as the best candidates to quantify structural constraint. Recent research assumes that RSA is the main determinant of sequence divergence. However, it is not yet clear which is the best predictor of substitution rates. To address this issue, we compared RSA and LPD with site-specific rates of evolution for a diverse data set of enzymes. In contrast with recent studies, we found that LPD measures correlate better than RSA with evolutionary rate. Moreover, the independent contribution of RSA is minor. Taking into account that LPD is related to backbone flexibility, we put forward the possibility that the rate of evolution of a site is determined by the ease with which the backbone deforms to accommodate mutations.

Functional and biophysical constraints result in site-dependent patterns of protein sequence variability. It is commonly assumed that the key structural determinant of site-specific rates of evolution is the Relative Solvent Accessibility (RSA). However, a recent study found that amino acid substitution rates correlate better with two Local Packing Density (LPD) measures, the Weighted Contact Number (WCN) and the Contact Number (CN), than with RSA. This work aims at a more thorough assessment. To this end, in addition to substitution rates, we considered four other sequence variability scores, four measures of solvent accessibility (SA), and other CN measures. We compared all properties for each protein of a structurally and functionally diverse representative dataset of monomeric enzymes. We show that the best sequence variability measures take into account phylogenetic tree topology. More importantly, we show that both LPD measures (WCN and CN) correlate better than all of the SA measures, regardless of the sequence variability score used. Moreover, the independent contribution of the best LPD measure is approximately four times larger than that of the best SA measure. This study strongly supports the conclusion that a site's packing density rather than its solvent accessibility is the main structural determinant of its rate of evolution.

To understand the gene regulation of an organism of interest, a comprehensive genome annotation is essential. While some features, such as coding sequences, can be computationally predicted with high accuracy based purely on the genomic sequence, others, such as promoter elements or noncoding RNAs, are harder to detect. RNA sequencing (RNA-seq) has proven to be an efficient method to identify these genomic features and to improve genome annotations. However, processing and integrating RNA-seq data in order to generate high-resolution annotations is challenging, time consuming, and requires numerous steps. We have constructed a powerful and modular tool called ANNOgesic that provides the required analyses and simplifies RNA-seq-based bacterial and archaeal genome annotation. It can integrate data from conventional RNA-seq and differential RNA-seq and predicts and annotates numerous features, including small noncoding RNAs, with high precision. The software is available under an open source license (ISCL) at https://pypi.org/project/ANNOgesic/.

Isobaric labeling has the promise of combining high sample multiplexing with precise quantification. However, normalization issues and the missing value problem of complete n -plexes hamper quantification across more than one n -plex. Here, we introduce two novel algorithms implemented in MaxQuant that substantially improve the data analysis with multiple n -plexes. First, isobaric matching between runs makes use of the three-dimensional MS1 features to transfer identifications from identified to unidentified MS/MS spectra between liquid chromatography–mass spectrometry runs in order to utilize reporter ion intensities in unidentified spectra for quantification. On typical datasets, we observe a significant gain in MS/MS spectra that can be used for quantification. Second, we introduce a novel PSM-level normalization, applicable to data with and without the common reference channel. It is a weighted median-based method, in which the weights reflect the number of ions that were used for fragmentation. On a typical dataset, we observe complete removal of batch effects and dominance of the biological sample grouping after normalization. Furthermore, we provide many novel processing and normalization options in Perseus, the companion software for the downstream analysis of quantitative proteomics results. All novel tools and algorithms are available with the regular MaxQuant and Perseus releases, which are downloadable at .

Under unfavorable growth conditions, bacteria enter stationary phase and can maintain cell viability over prolonged periods with no increase in cell number. To obtain insights into the regulatory mechanisms that allow bacteria to resume growth when conditions become favorable again (outgrowth), we performed global transcriptome analyses at different stages of growth for the alphaproteobacterium Rhodobacter sphaeroides. The majority of genes were not differentially expressed across growth phases. After a short stationary phase (about 20 h after growth starts to slow down), only 7% of the genes showed altered expression (fold change of Ͼ1.6 or less than Ϫ1.6, corresponding to a log 2 fold change of Ͼ0.65 or less than Ϫ0.65, respectively) compared to expression at exponential phase. Outgrowth induced a distinct response in gene expression which was strongly influenced by the length of the preceding stationary phase. After a long stationary phase (about 64 h after growth starts to slow down), a much larger number of genes (15.1%) was induced in outgrowth than after a short stationary phase (1.7%). Many of those genes are known members of the RpoHI/RpoHII regulons and have established functions in stress responses. A main effect of RpoHI on the transcriptome in outgrowth after a long stationary phase was confirmed. Growth experiments with mutant strains further support an important function in outgrowth after prolonged stationary phase for the RpoHI and RpoHII sigma factors.IMPORTANCE In natural environments, the growth of bacteria is limited mostly by lack of nutrients or other unfavorable conditions. It is important for bacterial populations to efficiently resume growth after being in stationary phase, which may last for long periods. Most previous studies on growth-phase-dependent gene expression did not address outgrowth after stationary phase. This study on growth-phase-dependent gene regulation in a model alphaproteobacterium reveals, for the first time, that the length of the stationary phase strongly impacts the transcriptome during outgrowth. The alternative sigma factors RpoHI and RpoHII, which are important regulators of stress responses in alphaproteobacteria, play a major role during outgrowth following prolonged stationary phase. These findings provide the first insight into the regulatory mechanisms enabling efficient outgrowth.

Knotted proteins are more commonly observed in recent years due to the enormously growing number of structures in the Protein Data Bank (PDB). Studies show that the knot regions contribute to both ligand binding and enzyme activity in proteins such as the chromophore-binding domain of phytochrome, ketol–acid reductoisomerase or SpoU methyltransferase. However, there are still many misidentified knots published in the literature due to the absence of a convenient web tool available to the general biologists. Here, we present the first web server to detect the knots in proteins as well as provide information on knotted proteins in PDB—the protein KNOT (pKNOT) web server. In pKNOT, users can either input PDB ID or upload protein coordinates in the PDB format. The pKNOT web server will detect the knots in the protein using the Taylor's smoothing algorithm. All the detected knots can be visually inspected using a Java-based 3D graphics viewer. We believe that the pKNOT web server will be useful to both biologists in general and structural biologists in particular.

Due to advances in structural biology, an increasing number of protein structures of unknown function have been deposited in Protein Data Bank (PDB). These proteins are usually characterized by novel structures and sequences. Conventional comparative methodology (such as sequence alignment, structure comparison, or template search) is unable to determine their function. Thus, it is important to identify protein's function directly from its structure, but this is not an easy task. One of the strategies used is to analyze whether there are distinctive structure-derived features associated with functional residues. If so, one may be able to identify the functional residues directly from a single structure. Recently, we have shown that protein weighted contact number is related to atomic thermal fluctuations and can be used to derive motional correlations in proteins. In this report, we analyze the weighted contact-number profiles of both catalytic residues and non-catalytic residues for a dataset of 760 structures. We found that catalytic residues have distinct distributions of weighted contact numbers from those of non-catalytic residues. Using this feature, we are able to effectively differentiate catalytic residues from other residues with a single optimized threshold value. Our method is simple to implement and compares favourably with other more sophisticated methods. In addition, we discuss the physics behind the relationship between catalytic residues and their contact numbers as well as other features (such as residue centrality or B-factors) associated with catalytic residues.