Categorical, low-dimensional decomposition of human odor space with non-negative matrix factorization

Recent studies using Principal Components Analysis (PCA) support low-dimensional models of odor space, in which one or two dimensions - with hedonic valence featuring prominently - explain most odor variability. Here we use non-negative matrix factorization (NMF) - a nonlinear optimization method - to discover an alternative, reduced-dimensional representation of the Dravnieks odor database (144 odors x 146 descriptors). NMF is theoretically well-suited for these types of analyses, as odor profiling data is inherently non-negative (e.g. descriptors either apply, or do not). We divided the dataset into training and testing halves, and found that RMSD testing error attained a minimum for subspace choice of 25, motivating this as an upper bound for odor perceptual space dimensionality. More parsimonious representations were found by comparing reconstruction errors (fraction of unexplained variance) of NMF with reconstruction errors of PCA on scrambled data (PCAsd). For subspace sizes > 10, NMF error was indistinguishable from PCAsd error, indicating no gain in retaining more than 10 perceptual dimensions. As is typical of NMF basis sets, the 10 odor dimensions we obtain are sparse (only a small subset of the 146 descriptors apply), and categorical (represent a positive valued quality). Moreover, these 10 dimensions were near-orthogonal, with a mean angle of 73 degrees between all pairs of basis vectors. Investigating the distribution of odors in this 10-dimensional space, we find marked clustering (Figure 1), with each odor being well-defined by its membership in a single dimension, and to the exclusion of others. In ongoing work, we are using graph-kernel methods to define a rudimentary mapping between physicochemical features of odorants and the 10 descriptor dimensions

ATPase Subdomain IA Is a Mediator of Interdomain Allostery in Hsp70 Molecular Chaperones

The versatile functions of the heat shock protein 70 (Hsp70) family of molecular chaperones rely on allosteric interactions between their nucleotide-binding and substrate-binding domains, NBD and SBD. Understanding the mechanism of interdomain allostery is essential to rational design of Hsp70 modulators. Yet, despite significant progress in recent years, how the two Hsp70 domains regulate each other's activity remains elusive. Covariance data from experiments and computations emerged in recent years as valuable sources of information towards gaining insights into the molecular events that mediate allostery. In the present study, conservation and covariance properties derived from both sequence and structural dynamics data are integrated with results from Perturbation Response Scanning and in vivo functional assays, so as to establish the dynamical basis of interdomain signal transduction in Hsp70s. Our study highlights the critical roles of SBD residues D481 and T417 in mediating the coupled motions of the two domains, as well as that of G506 in enabling the movements of the α-helical lid with respect to the β-sandwich. It also draws attention to the distinctive role of the NBD subdomains: Subdomain IA acts as a key mediator of signal transduction between the ATP- and substrate-binding sites, this function being achieved by a cascade of interactions predominantly involving conserved residues such as V139, D148, R167 and K155. Subdomain IIA, on the other hand, is distinguished by strong coevolutionary signals (with the SBD) exhibited by a series of residues (D211, E217, L219, T383) implicated in DnaJ recognition. The occurrence of coevolving residues at the DnaJ recognition region parallels the behavior recently observed at the nucleotide-exchange-factor recognition region of subdomain IIB. These findings suggest that Hsp70 tends to adapt to co-chaperone recognition and activity via coevolving residues, whereas interdomain allostery, critical to chaperoning, is robustly enabled by conserved interactions. © 2014 General et al

Application of a risk-management framework for integration of stromal tumor-infiltrating lymphocytes in clinical trials

Stromal tumor-infiltrating lymphocytes (sTILs) are a potential predictive biomarker for immunotherapy response in metastatic triple-negative breast cancer (TNBC). To incorporate sTILs into clinical trials and diagnostics, reliable assessment is essential. In this review, we propose a new concept, namely the implementation of a risk-management framework that enables the use of sTILs as a stratification factor in clinical trials. We present the design of a biomarker risk-mitigation workflow that can be applied to any biomarker incorporation in clinical trials. We demonstrate the implementation of this concept using sTILs as an integral biomarker in a single-center phase II immunotherapy trial for metastatic TNBC (TONIC trial, NCT02499367), using this workflow to mitigate risks of suboptimal inclusion of sTILs in this specific trial. In this review, we demonstrate that a web-based scoring platform can mitigate potential risk factors when including sTILs in clinical trials, and we argue that this framework can be applied for any future biomarker-driven clinical trial setting