Kinetic Analysis of Protein Stability Reveals Age-Dependent Degradation.

Do young and old protein molecules have the same probability to be degraded? We addressed this question using metabolic pulse-chase labeling and quantitative mass spectrometry to obtain degradation profiles for thousands of proteins. We find that >10% of proteins are degraded non-exponentially. Specifically, proteins are less stable in the first few hours of their life and stabilize with age. Degradation profiles are conserved and similar in two cell types. Many non-exponentially degraded (NED) proteins are subunits of complexes that are produced in super-stoichiometric amounts relative to their exponentially degraded (ED) counterparts. Within complexes, NED proteins have larger interaction interfaces and assemble earlier than ED subunits. Amplifying genes encoding NED proteins increases their initial degradation. Consistently, decay profiles can predict protein level attenuation in aneuploid cells. Together, our data show that non-exponential degradation is common, conserved, and has important consequences for complex formation and regulation of protein abundance.

Time-resolved DNA release from an O-antigen-specific Salmonella bacteriophage with a contractile tail.

Myoviruses, bacteriophages with T4-like architecture, must contract their tails prior to DNA release. However, quantitative kinetic data on myovirus particle opening are lacking, although they are promising tools in bacteriophage-based antimicrobial strategies directed against Gram-negative hosts. For the first time, we show time-resolved DNA ejection from a bacteriophage with a contractile tail, the multi-O-antigen-specific Salmonella myovirus Det7. DNA release from Det7 was triggered by lipopolysaccharide (LPS) O-antigen receptors and notably slower than in noncontractile-tailed siphoviruses. Det7 showed two individual kinetic steps for tail contraction and particle opening. Our in vitro studies showed that highly specialized tailspike proteins (TSPs) are necessary to attach the particle to LPS. A P22-like TSP confers specificity for the Salmonella Typhimurium O-antigen. Moreover, crystal structure analysis at 1.63 Šresolution confirmed that Det7 recognized the Salmonella Anatum O-antigen via an ϵ15-like TSP, DettilonTSP. DNA ejection triggered by LPS from either host showed similar velocities, so particle opening is thus a process independent of O-antigen composition and the recognizing TSP. In Det7, at permissive temperatures TSPs mediate O-antigen cleavage and couple cell surface binding with DNA ejection, but no irreversible adsorption occurred at low temperatures. This finding was in contrast to short-tailed Salmonella podoviruses, illustrating that tailed phages use common particle-opening mechanisms but have specialized into different infection niches.

Goodness of fit testing in dynamic single-molecule force spectroscopy.

Dynamic single-molecule force spectroscopy (SMFS) is a powerful method to characterize the mechanical stability of biomolecules. We address the problem that the standard manner of reporting the extracted energy landscape parameters does not reveal the intrinsic statistical errors associated with them. This problem becomes particularly relevant when SMFS is used to compare two or more different molecular systems. Here, we propose two methods that allow for a straightforward test of statistical significance. We illustrate the power of the methods by applying them to the experimental results obtained for three dimeric coiled coils of different lengths. Both methods are general and may be applied to any problem involving the fit of models with two correlated parameters.

Quantitative assessment of ribosome drop-off in E. coli.

Premature ribosome drop-off is one of the major errors in translation of mRNA by ribosomes. However, repeated analyses of Ribo-seq data failed to quantify its strength inE. coli Relying on a novel highly sensitive data analysis method we show that a significant rate of ribosome drop-off is measurable and can be quantified also when cells are cultured under non-stressing conditions. Moreover, we find that the drop-off rate is highly variable, depending on multiple factors. In particular, under environmental stress such as amino acid starvation or ethanol intoxication, the drop-off rate markedly increases.

The Role of SufS Is Restricted to Fe-S Cluster Biosynthesis in Escherichia coli.

In Escherichia coli, two different systems that are important for the coordinate formation of Fe-S clusters have been identified, namely, the ISC and SUF systems. The ISC system is the housekeeping Fe-S machinery, which provides Fe-S clusters for numerous cellular proteins. The IscS protein of this system was additionally revealed to be the primary sulfur donor for several sulfur-containing molecules with important biological functions, among which are the molybdenum cofactor (Moco) and thiolated nucleosides in tRNA. Here, we show that deletion of central components of the ISC system in addition to IscS leads to an overall decrease in Fe-S cluster enzyme and molybdoenzyme activity in addition to a decrease in the number of Fe-S-dependent thiomodifications of tRNA, based on the fact that some proteins involved in Moco biosynthesis and tRNA thiolation are Fe-S-dependent. Complementation of the ISC deficient strains with the suf operon restored the activity of Fe-S-containing proteins, including the MoaA protein, which is involved in the conversion of 5'GTP to cyclic pyranopterin monophosphate in the fist step of Moco biosynthesis. While both systems share a high degree of similarity, we show that the function of their respective l-cysteine desulfurase IscS or SufS is specific for each cellular pathway. It is revealed that SufS cannot play the role of IscS in sulfur transfer for the formation of 2-thiouridine, 4-thiouridine, or the dithiolene group of molybdopterin, being unable to interact with TusA or ThiI. The results demonstrate that the role of the SUF system is exclusively restricted to Fe-S cluster assembly in the cell.

Pathway structure determination in complex stochastic networks with non-exponential dwell times.

Analysis of complex networks has been widely used as a powerful tool for investigating various physical, chemical, and biological processes. To understand the emergent properties of these complex systems, one of the most basic issues is to determine the structure and topology of the underlying networks. Recently, a new theoretical approach based on first-passage analysis has been developed for investigating the relationship between structure and dynamic properties for network systems with exponential dwell time distributions. However, many real phenomena involve transitions with non-exponential waiting times. We extend the first-passage method to uncover the structure of distinct pathways in complex networks with non-exponential dwell time distributions. It is found that the analysis of early time dynamics provides explicit information on the length of the pathways associated to their dynamic properties. It reveals a universal relationship that we have condensed in one general equation, which relates the number of intermediate states on the shortest path to the early time behavior of the first-passage distributions. Our theoretical predictions are confirmed by extensive Monte Carlo simulations.

Unveiling the hidden structure of complex stochastic biochemical networks.

Complex Markov models are widely used and powerful predictive tools to analyze stochastic biochemical processes. However, when the network of states is unknown, it is necessary to extract information from the data to partially build the network and estimate the values of the rates. The short-time behavior of the first-passage time distributions between two states in linear chains has been shown recently to behave as a power of time with an exponent equal to the number of intermediate states. For a general Markov model we derive the complete Taylor expansion of the first-passage time distribution between two arbitrary states. By combining algebraic methods and graph theory approaches it is shown that the first term of the Taylor expansion is determined by the shortest path from the initial state to the final state. When this path is unique, we prove that the coefficient of the first term can be written in terms of the product of the transition rates along the path. It is argued that the application of our results to first-return times may be used to estimate the dependence of rates on external parameters in experimentally measured time distributions.

FANSe: an accurate algorithm for quantitative mapping of large scale sequencing reads.

The most crucial step in data processing from high-throughput sequencing applications is the accurate and sensitive alignment of the sequencing reads to reference genomes or transcriptomes. The accurate detection of insertions and deletions (indels) and errors introduced by the sequencing platform or by misreading of modified nucleotides is essential for the quantitative processing of the RNA-based sequencing (RNA-Seq) datasets and for the identification of genetic variations and modification patterns. We developed a new, fast and accurate algorithm for nucleic acid sequence analysis, FANSe, with adjustable mismatch allowance settings and ability to handle indels to accurately and quantitatively map millions of reads to small or large reference genomes. It is a seed-based algorithm which uses the whole read information for mapping and high sensitivity and low ambiguity are achieved by using short and non-overlapping reads. Furthermore, FANSe uses hotspot score to prioritize the processing of highly possible matches and implements modified Smith-Watermann refinement with reduced scoring matrix to accelerate the calculation without compromising its sensitivity. The FANSe algorithm stably processes datasets from various sequencing platforms, masked or unmasked and small or large genomes. It shows a remarkable coverage of low-abundance mRNAs which is important for quantitative processing of RNA-Seq datasets.

A data-driven estimation of the ribosome drop-off rate in S. cerevisiae reveals a correlation with the genes length.

Ribosomes are the molecular machinery that catalyse all the fundamental steps involved in the translation of mRNAs into proteins. Given the complexity of this process, the efficiency of protein synthesis depends on a large number of factors among which ribosome drop-off (i.e. the premature detachment of the ribosome from the mRNA template) plays an important role. However, an in vitro quantification of the extent to which ribosome drop-off occurs is not trivial due to difficulties in obtaining the needed experimental evidence. In this work we focus on the study of ribosome drop-off in Saccharomyces cerevisiae by using 'Ribofilio', a novel software tool that relies on a high sensitive strategy to estimate the ribosome drop-off rate from ribosome profiling data. Our results show that ribosome drop-off events occur at a significant rate also when S. cerevisiae is cultured in standard conditions. In this context, we also identified a correlation between the ribosome drop-off rate and the genes length: the longer the gene, the lower the drop-off rate.