Alexander Franks

Reversible, Specific, Active Aggregates of Endogenous Proteins Assemble upon Heat Stress

Heat causes protein misfolding and aggregation and, in eukaryotic cells, triggers aggregation of proteins and RNA into stress granules. We have carried out extensive proteomic studies to quantify heat-triggered aggregation and subsequent disaggregation in budding yeast, identifying >170 endogenous proteins aggregating within minutes of heat shock in multiple subcellular compartments. We demonstrate that these aggregated proteins are not misfolded and destined for degradation. Stable-isotope labeling reveals that even severely aggregated endogenous proteins are disaggregated without degradation during recovery from shock, contrasting with the rapid degradation observed for many exogenous thermolabile proteins. Although aggregation likely inactivates many cellular proteins, in the case of a heterotrimeric aminoacyl-tRNA synthetase complex, the aggregated proteins remain active with unaltered fidelity. We propose that most heat-induced aggregation of mature proteins reflects the operation of an adaptive, autoregulatory process of functionally significant aggregate assembly and disassembly that aids cellular adaptation to thermal stress.

Accounting for Experimental Noise Reveals That mRNA Levels, Amplified by Post-Transcriptional Processes, Largely Determine Steady-State Protein Levels in Yeast

Cells respond to their environment by modulating protein levels through mRNA transcription and post-transcriptional control. Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy, missing systematically, and collinear---properties of mRNA and protein measurements---which motivated us to revisit this subject. Noise-robust analyses of 24 studies of budding yeast reveal that mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels, but rise much more rapidly. Regulation of translation suffices to explain this nonlinear effect, revealing post-transcriptional amplification of, rather than competition with, transcriptional signals. These results substantially revise widely credited models of protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.

Characterizing the spatial structure of defensive skill in professional basketball

Although basketball is a dualistic sport, with all players competing on both offense and defense, almost all of the sports conventional metrics are designed to summarize offensive play. As a result, player valuations are largely based on offensive performances and to a much lesser degree on defensive ones. Steals, blocks and defensive rebounds provide only a limited summary of defensive effectiveness, yet they persist because they summarize salient events that are easy to observe. Due to the inefficacy of traditional defensive statistics, the state of the art in defensive analytics remains qualitative, based on expert intuition and analysis that can be prone to human biases and imprecision. Fortunately, emerging optical player tracking systems have the potential to enable a richer quantitative characterization of basketball performance, particularly defensive performance. Unfortunately, due to computational and methodological complexities, that potential remains unmet. This paper attempts to fill this void, combining spatial and spatio-temporal processes, matrix factorization techniques and hierarchical regression models with player tracking data to advance the state of defensive analytics in the NBA. Our approach detects, characterizes and quantifies multiple aspects of defensive play in basketball, supporting some common understandings of defensive effectiveness, challenging others and opening up many new insights into the defensive elements of basketball.

Post-transcriptional regulation across human tissues

Transcriptional and post-transcriptional regulation shape tissue-type-specific proteomes, but their relative contributions remain contested. Estimates of the factors determining protein levels in human tissues do not distinguish between (i) the factors determining the variability between the abundances of different proteins, i.e., mean-level-variability and, (ii) the factors determining the physiological variability of the same protein across different tissue types, i.e., across-tissues variability. We sought to estimate the contribution of transcript levels to these two orthogonal sources of variability, and found that scaled mRNA levels can account for most of the mean-level-variability but not necessarily for across-tissues variability. The reliable quantification of the latter estimate is limited by substantial measurement noise. However, protein-to-mRNA ratios exhibit substantial across-tissues variability that is functionally concerted and reproducible across different datasets, suggesting extensive post-transcriptional regulation. These results caution against estimating protein fold-changes from mRNA fold-changes between different cell-types, and highlight the contribution of post-transcriptional regulation to shaping tissue-type-specific proteomes.

Estimating a Structured Covariance Matrix From Multilab Measurements in High-Throughput Biology

We consider the problem of quantifying the degree of coordination between transcription and translation, in yeast. Several studies have reported a surprising lack of coordination over the years, in organisms as different as yeast and humans, using diverse technologies. However, a close look at this literature suggests that the lack of reported correlation may not reflect the biology of regulation. These reports do not control for between-study biases and structure in the measurement errors, ignore key aspects of how the data connect to the estimand, and systematically underestimate the correlation as a consequence. Here, we design a careful meta-analysis of 27 yeast datasets, supported by a multilevel model, full uncertainty quantification, a suite of sensitivity analyses, and novel theory, to produce a more accurate estimate of the correlation between mRNA and protein levels—a proxy for coordination. From a statistical perspective, this problem motivates new theory on the impact of noise, model misspecifications, and nonignorable missing data on estimates of the correlation between high-dimensional responses. We find that the correlation between mRNA and protein levels is quite high under the studied conditions, in yeast, suggesting that post-transcriptional regulation plays a less prominent role than previously thought.

A mixture-of-modelers approach to forecasting NCAA tournament outcomes

Abstract Predicting the outcome of a single sporting event is difficult; predicting all of the outcomes for an entire tournament is a monumental challenge. Despite the difficulties, millions of people compete each year to forecast the outcome of the NCAA men’s basketball tournament, which spans 63 games over 3 weeks. Statistical prediction of game outcomes involves a multitude of possible covariates and information sources, large performance variations from game to game, and a scarcity of detailed historical data. In this paper, we present the results of a team of modelers working together to forecast the 2014 NCAA men’s basketball tournament. We present not only the methods and data used, but also several novel ideas for post-processing statistical forecasts and decontaminating data sources. In particular, we highlight the difficulties in using publicly available data and suggest techniques for improving their relevance.

The companion dog as a model for human aging and mortality

Around the world, human populations have experienced large increases in average lifespan over the last 150 years, and while individuals are living longer, they are spending more years of life with multiple chronic morbidities. Researchers have used numerous laboratory animal models to understand the biological and environmental factors that influence aging, morbidity, and longevity. However, the most commonly studied animal species, laboratory mice and rats, do not experience environmental conditions similar to those to which humans are exposed, nor do we often diagnose them with many of the naturally occurring pathologies seen in humans. Recently, the companion dog has been proposed as a powerful model to better understand the genetic and environmental determinants of morbidity and mortality in humans. However, it is not known to what extent the age‐related dynamics of morbidity, comorbidity, and mortality are shared between humans and dogs. Here, we present the first large‐scale comparison of human and canine patterns of age‐specific morbidity and mortality. We find that many chronic conditions that commonly occur in human populations (obesity, arthritis, hypothyroidism, and diabetes), and which are associated with comorbidities, are also associated with similarly high levels of comorbidity in companion dogs. We also find significant similarities in the effect of age on disease risk in humans and dogs, with neoplastic, congenital, and metabolic causes of death showing similar age trajectories between the two species. Overall, our study suggests that the companion dog may be an ideal translational model to study the many complex facets of human morbidity and mortality.

Extensive post-transcriptional regulation across human tissues

Transcriptional and post-transcriptional regulation shape tissue-type-specific proteomes, but their relative contributions remain contested. Estimates of the factors determining protein levels in human tissues do not distinguish between (i) the factors determining the variability between the abundances of different proteins, i.e., mean-level-variability and, (ii) the factors determining the physiological variability of the same protein across different tissue types, i.e., across-tissue variability. We sought to estimate the contribution of transcript levels to these two orthogonal sources of variability, and found that mRNA levels can account for most of the mean-level-variability but not necessarily for across-tissue variability. The precise quantification of the latter estimate is limited by substantial measurement noise. However, protein-to-mRNA ratios exhibit substantial across-tissue variability that is functionally concerted and reproducible across different datasets, suggesting extensive post-transcriptional regulation. These results caution against estimating protein fold-changes from mRNA fold-changes between different cell-types, and highlight the contribution of post-transcriptional regulation to shaping tissue-type-specific proteomes.

Accounting for experimental noise reveals that transcription dominates control of steady-state protein levels in yeast

Cells respond to their environment by modulating protein levels through mRNA transcription and post-transcriptional control. Modest correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that transcript levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy, missing systematically, and collinear—properties of mRNA and protein measurements—which motivated us to revisit this subject. Noise-robust analyses of 25 studies of budding yeast reveal that mRNA levels explain roughly 80% of the variation in steady-state protein levels. Post-transcriptional regulation amplifies rather than competes with the transcriptional signal. Measurements are highly reproducible within but not between studies, and are distorted in part by between-study differences in gene expression. These results substantially revise current models of protein-level regulation and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.Cells respond to their environment by modulating protein levels through mRNA transcription and post-transcriptional control. Modest observed correlations between global steady-state mRNA and protein measurements have been interpreted as evidence that mRNA levels determine roughly 40% of the variation in protein levels, indicating dominant post-transcriptional effects. However, the techniques underlying these conclusions, such as correlation and regression, yield biased results when data are noisy, missing systematically, and collinear—properties of mRNA and protein measurements—which motivated us to revisit this subject. Noise-robust analyses of 24 studies of budding yeast reveal that mRNA levels explain more than 85% of the variation in steady-state protein levels. Protein levels are not proportional to mRNA levels, but rise much more rapidly. Regulation of translation suffices to explain this nonlinear effect, revealing post-transcriptional amplification of, rather than competition with, transcriptional signals. These results substantially revise widely credited models of protein-level regulation, and introduce multiple noise-aware approaches essential for proper analysis of many biological phenomena.

Estimating cellular pathways from an ensemble of heterogeneous data sources

Building better models of cellular pathways is one of the major challenges of systems biology and functional genomics. There is a need for methods to build on established expert knowledge and reconcile it with results of high-throughput studies. Moreover, the available data sources are heterogeneous and need to be combined in a way specific for the part of the pathway in which they are most informative. Here, we present a compartment specific strategy to integrate edge, node and path data for the refinement of a network hypothesis. Specifically, we use a local-move Gibbs sampler for refining pathway hypotheses from a compendium of heterogeneous data sources, including novel methodology for integrating protein attributes. We demonstrate the utility of this approach in a case study of the pheromone response MAPK pathway in the yeast S. cerevisiae.