Use of multi-perturbation Shapley analysis in lesion studies of functional networks: The case of upper limb paresis.

Understanding the impact of variation in lesion topography on the expression of functional impairments following stroke is important, as it may pave the way to modeling structure-function relations in statistical terms while pointing to constraints for adaptive remapping and functional recovery. Multi-perturbation Shapley-value analysis (MSA) is a relatively novel game-theoretical approach for multivariate lesion-symptom mapping. In this methodological paper, we provide a comprehensive explanation of MSA. We use synthetic data to assess the method's accuracy and perform parameter optimization. We then demonstrate its application using a cohort of 107 first-event subacute stroke patients, assessed for upper limb (UL) motor impairment (Fugl-Meyer Assessment scale). Under the conditions tested, MSA could correctly detect simulated ground-truth lesion-symptom relationships with a sensitivity of 75% and specificity of ~90%. For real behavioral data, MSA disclosed a strong hemispheric effect in the relative contribution of specific regions-of-interest (ROIs): poststroke UL motor function was mostly contributed by damage to ROIs associated with movement planning (supplementary motor cortex and superior frontal gyrus) following left-hemispheric damage (LHD) and by ROIs associated with movement execution (primary motor and somatosensory cortices and the ventral brainstem) following right-hemispheric damage (RHD). Residual UL motor ability following LHD was found to depend on a wider array of brain structures compared to the residual motor ability of RHD patients. The results demonstrate that MSA can provide a unique insight into the relative importance of different hubs in neural networks, which is difficult to obtain using standard univariate methods.

Quantitative microscopy of mole rat eosinophil granule morphology.

Mole rat bone marrow cells and peritoneal eosinophils are used to study granule morphological maturation by quantitative microscopy. The bulk eosinophil granule content is pre-stored in unique granular structures known as crystalloid or secondary granules. Mole rat eosinophil granules exhibit the basic structure of an electron-dense crystalloid core surrounded by a lighter, homogeneous matrix. Morphometric analysis demonstrated that bone marrow-derived eosinophil sphere-like granules display a periodic, multimodal granule volume distribution. In contrast, peritoneal eosinophils display cigar-shaped granules, whose crystalloid cores are more variable in size and shape as compared to bone marrow eosinophil granules. Using a morphometric approach, we deduced that the basic granule volume quantum is similar in both cases, suggesting that the sphere-like young eosinophil granules turn into dense ellipsoidal ones by intragranular processes in which both volume and membrane surface are conserved. Crystalloid granule mediators are known to be widely associated with allergic inflammatory events, which may damage the host tissue following secretion to the extracellular environment. Based on mathematical modeling, we suggest that this deviation from sphere-like to ellipsoidal shape reflects an adaptive response of the mole rat to its unique solitary life.

Multiple knockout analysis of genetic robustness in the yeast metabolic network.

Genetic robustness characterizes the constancy of the phenotype in face of heritable perturbations. Previous investigations have used comprehensive single and double gene knockouts to study gene essentiality and pairwise gene interactions in the yeast Saccharomyces cerevisiae. Here we conduct an in silico multiple knockout investigation of a flux balance analysis model of the yeast's metabolic network. Cataloging gene sets that provide mutual functional backup, we identify sets of up to eight interacting genes and characterize the 'k robustness' (the depth of backup interactions) of each gene. We find that 74% (360) of the metabolic genes participate in processes that are essential to growth in a standard laboratory environment, compared with only 13% previously found to be essential using single knockouts. The genes' k robustness is shown to be a solid indicator of their biological buffering capacity and is correlated with both the genes' environmental specificity and their evolutionary retention.

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome.

BACKGROUND: Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. METHODS: We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort. FINDINGS: Evaluating ENLIGHT's performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient's treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy. CONCLUSIONS: ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome. FUNDING: This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

Genome-scale analysis of translation elongation with a ribosome flow model.

We describe the first large scale analysis of gene translation that is based on a model that takes into account the physical and dynamical nature of this process. The Ribosomal Flow Model (RFM) predicts fundamental features of the translation process, including translation rates, protein abundance levels, ribosomal densities and the relation between all these variables, better than alternative ('non-physical') approaches. In addition, we show that the RFM can be used for accurate inference of various other quantities including genes' initiation rates and translation costs. These quantities could not be inferred by previous predictors. We find that increasing the number of available ribosomes (or equivalently the initiation rate) increases the genomic translation rate and the mean ribosome density only up to a certain point, beyond which both saturate. Strikingly, assuming that the translation system is tuned to work at the pre-saturation point maximizes the predictive power of the model with respect to experimental data. This result suggests that in all organisms that were analyzed (from bacteria to Human), the global initiation rate is optimized to attain the pre-saturation point. The fact that similar results were not observed for heterologous genes indicates that this feature is under selection. Remarkably, the gap between the performance of the RFM and alternative predictors is strikingly large in the case of heterologous genes, testifying to the model's promising biotechnological value in predicting the abundance of heterologous proteins before expressing them in the desired host.

Quantal basis of vesicle growth and information content, a unified approach.

Secretory vesicles express a periodic multimodal size distribution. The successive modes are integral multiples of the smallest mode (G(1)). The vesicle content ranges from macromolecules (proteins, mucopolysaccharides and hormones) to low molecular weight molecules (neurotransmitters). A steady-state model has been developed to emulate a mechanism for the introduction of vesicles of monomer size, which grow by a unit addition mechanism, G(1)+G(n)-->G(n+1) which, at a later stage are eliminated from the system. We describe a model of growth and elimination transition rates which adequately illustrates the distributions of vesicle population size at steady-state and upon elimination. Consequently, prediction of normal behavior and pathological perturbations is feasible. Careful analysis of spontaneous secretion, as compared to short burst-induced secretion, suggests that the basic character-code for reliable communication should be within a range of only 8-10 vesicles' burst which may serve as a yes/no message.

Coping with participant heterogeneity in dichotomous responses.

Participant heterogeneity induces spurious dependence that may obscure dependence patterns displayed by a covariance matrix. A parsimonious method is proposed for the reduction of this confounding effect. The method is applied to dichotomous behavioral response data of participants diagnosed with schizophrenia, as assessed by the Pragmatic Protocol (J. Speech Hear. Disord. 1987; 52:105-119).

Migration rather than proliferation transcriptomic signatures are strongly associated with breast cancer patient survival.

The efficacy of prospective cancer treatments is routinely estimated by in vitro cell-line proliferation screens. However, it is unclear whether tumor aggressiveness and patient survival are influenced more by the proliferative or the migratory properties of cancer cells. To address this question, we experimentally measured proliferation and migration phenotypes across more than 40 breast cancer cell-lines. Based on the latter, we built and validated individual predictors of breast cancer proliferation and migration levels from the cells' transcriptomics. We then apply these predictors to estimate the proliferation and migration levels of more than 1000 TCGA breast cancer tumors. Reassuringly, both estimates increase with tumor's aggressiveness, as qualified by its stage, grade, and subtype. However, predicted tumor migration levels are significantly more strongly associated with patient survival than the proliferation levels. We confirmed these findings by conducting siRNA knock-down experiments on the highly migratory MDA-MB-231 cell lines and deriving gene knock-down based proliferation and migration signatures. We show that cytoskeletal drugs might be more beneficial in patients with high predicted migration levels. Taken together, these results testify to the importance of migration levels in determining patient survival.

A machine learning predictor of facial attractiveness revealing human-like psychophysical biases.

Recent psychological studies have strongly suggested that humans share common visual preferences for facial attractiveness. Here, we present a learning model that automatically extracts measurements of facial features from raw images and obtains human-level performance in predicting facial attractiveness ratings. The machine's ratings are highly correlated with mean human ratings, markedly improving on recent machine learning studies of this task. Simulated psychophysical experiments with virtually manipulated images reveal preferences in the machine's judgments that are remarkably similar to those of humans. Thus, a model trained explicitly to capture a specific operational performance criteria, implicitly captures basic human psychophysical characteristics.

Analysis of Human Brain Structure Reveals that the Brain "Types" Typical of Males Are Also Typical of Females, and Vice Versa.

Findings of average differences between females and males in the structure of specific brain regions are often interpreted as indicating that the typical male brain is different from the typical female brain. An alternative interpretation is that the brain types typical of females are also typical of males, and sex differences exist only in the frequency of rare brain types. Here we contrasted the two hypotheses by analyzing the structure of 2176 human brains using three analytical approaches. An anomaly detection analysis showed that brains from females are almost as likely to be classified as "normal male brains," as brains from males are, and vice versa. Unsupervised clustering algorithms revealed that common brain "types" are similarly common in females and in males and that a male and a female are almost as likely to have the same brain "type" as two females or two males are. Large sex differences were found only in the frequency of some rare brain "types." Last, supervised clustering algorithms revealed that the brain "type(s)" typical of one sex category in one sample could be typical of the other sex category in another sample. The present findings demonstrate that even when similarity and difference are defined mathematically, ignoring biological or functional relevance, sex category (i.e., whether one is female or male), is not a major predictor of the variability of human brain structure. Rather, the brain types typical of females are also typical of males, and vice versa, and large sex differences are found only in the prevalence of some rare brain types. We discuss the implications of these findings to studies of the structure and function of the human brain.

Gene expression of Caenorhabditis elegans neurons carries information on their synaptic connectivity.

The claim that genetic properties of neurons significantly influence their synaptic network structure is a common notion in neuroscience. The nematode Caenorhabditis elegans provides an exciting opportunity to approach this question in a large-scale quantitative manner. Its synaptic connectivity network has been identified, and, combined with cellular studies, we currently have characteristic connectivity and gene expression signatures for most of its neurons. By using two complementary analysis assays we show that the expression signature of a neuron carries significant information about its synaptic connectivity signature, and identify a list of putative genes predicting neural connectivity. The current study rigorously quantifies the relation between gene expression and synaptic connectivity signatures in the C. elegans nervous system and identifies subsets of neurons where this relation is highly marked. The results presented and the genes identified provide a promising starting point for further, more detailed computational and experimental investigations.

Quantitative analysis of genetic and neuronal multi-perturbation experiments.

Perturbation studies, in which functional performance is measured after deletion, mutation, or lesion of elements of a biological system, have been traditionally employed in many fields in biology. The vast majority of these studies have been qualitative and have employed single perturbations, often resulting in little phenotypic effect. Recently, newly emerging experimental techniques have allowed researchers to carry out concomitant multi-perturbations and to uncover the causal functional contributions of system elements. This study presents a rigorous and quantitative multi-perturbation analysis of gene knockout and neuronal ablation experiments. In both cases, a quantification of the elements' contributions, and new insights and predictions, are provided. Multi-perturbation analysis has a potentially wide range of applications and is gradually becoming an essential tool in biology.

Function Suggests Nano-Structure: Quantitative Structural Support for SNARE-Mediated Pore Formation.

Granule secretory content is released in either basal or calcium-activated complete exocytosis mode. A vital element in these processes is the establishment of a fusion pore between the granule membrane and the plasma membrane, initiated by the formation of a circular rosette docking arrangement of SNARE protein complexes. The controversially disputed number of SNARE complexes needed for granule priming leading to the formation of the fusion pore, is granule-size dependent and varies between secretion modes. Resorting to a statistical mechanics approach that views SNARE complexes and Ca(2+) ions as interacting particles, we have developed a relationship that links secretion rate to SNARE rosette size, Ca(2+) concentration and Ca(2+) ion cooperativity. Data are presented and discussed which suggest this SNARE-dependent generalization of existing narrow-range biophysical models that correlate secretion rate with Ca(2+) concentration and maximal Ca(2+) ion cooperativity. Evidence from dozens of examples in the literature advocate for this relation, which holds through the entire biological range. The coalescence of so many areas of diverse research methodologies has greatly augmented our understanding of so many different sequences of granule life cycle. Accordingly, these new tools may become valuable in a variety of electrophysiological experiments.

The econobiology of pancreatic acinar cells granule inventory and the stealthy nano-machine behind it.

The pancreatic gland secretes most of the enzymes and many other macromolecules needed for food digestion in the gastrointestinal tract. These molecules play an important role in digestion, host defense and lubrication. The secretion of pancreatic proteins ensures the availability of the correct mix of proteins when needed. This review describes model systems available for the study of the econobiology of secretory granule content. The secretory pancreatic molecules are stored in large dense-core secretory granules that may undergo either constitutive or evoked secretion, and constitute the granule inventory of the cell. It is proposed that the Golgi complex functions as a distribution center for secretory proteins in pancreatic acinar cells, packing the newly formed secretory molecules into maturing secretory granules, also known functionally as condensing vacuoles. Mathematical modelling brings forward a process underlying granule inventory maintenance at various physiological states of condensation and aggregation by homotypic fusion. These models suggest unique but simple mechanisms accountable for inventory buildup and size, as well as for the distribution of secretory molecules into different secretory pathways in pancreatic acinar cells.

An Example of an Improvable Rao-Blackwell Improvement, Inefficient Maximum Likelihood Estimator, and Unbiased Generalized Bayes Estimator.

The Rao-Blackwell theorem offers a procedure for converting a crude unbiased estimator of a parameter θ into a "better" one, in fact unique and optimal if the improvement is based on a minimal sufficient statistic that is complete. In contrast, behind every minimal sufficient statistic that is not complete, there is an improvable Rao-Blackwell improvement. This is illustrated via a simple example based on the uniform distribution, in which a rather natural Rao-Blackwell improvement is uniformly improvable. Furthermore, in this example the maximum likelihood estimator is inefficient, and an unbiased generalized Bayes estimator performs exceptionally well. Counterexamples of this sort can be useful didactic tools for explaining the true nature of a methodology and possible consequences when some of the assumptions are violated. [Received December 2014. Revised September 2015.].

The stealthy nano-machine behind mast cell granule size distribution.

The classical model of mast cell secretory granule formation suggests that newly synthesized secretory mediators, transported from the rough endoplasmic reticulum to the Golgi complex, undergo post-transitional modification and are packaged for secretion by condensation within membrane-bound granules of unit size. These unit granules may fuse with other granules to form larger granules that reside in the cytoplasm until secreted. A novel stochastic model for mast cell granule growth and elimination (G&E) as well as inventory management is presented. Resorting to a statistical mechanics approach in which SNAP (Soluble NSF Attachment Protein) REceptor (SNARE) components are viewed as interacting particles, the G&E model provides a simple 'nano-machine' of SNARE self-aggregation that can perform granule growth and secretion. Granule stock is maintained as a buffer to meet uncertainty in demand by the extracellular environment and to serve as source of supply during the lead time to produce granules of adaptive content. Experimental work, mathematical calculations, statistical modeling and a rationale for the emergence of nearly last-in, first out inventory management, are discussed.

Quantal Basis of Secretory Granule Biogenesis and Inventory Maintenance: the Surreptitious Nano-machine Behind It.

Proteins are molecular machines with the capacity to perform diverse physical work as response to signals from the environment. Proteins may be found as monomers or polymers, two states that represent an important subset of protein interactions and generate considerable functional diversity, leading to regulatory mechanisms closely akin to decision-making in service systems. Polymerization is not unique to proteins. Other cell compartments (e.g. secretory granules) or tissue states (e.g. miniature end plate potential) are associated with polymerization of some sort, leading to information transport. This data-processing mechanism has similarities with (and led us to the investigation of) granule homotypic polymerization kinetics. Using information theory, we demonstrate the role played by the heterogeneity induced by polymerization: granule size distribution and the stealthy machine behind granule life cycle increase system entropy, which modulates the source/receiver potential that affects communication between the cell and its environment. The granule inventory management by the same nano-machine is discussed.

Statistical analysis of the quantal basis of secretory granule formation.

The size distribution of vesicles exocytosed from secretory cells displays quantal nature, vesicle volume is periodic multi-modal, suggesting that these heterogeneous vesicles are aggregate sums of a variable number of homogeneous basic granules. Whether heterogeneity is a lumping-together artifact of the measurement or an inherent intra-cell feature of the vesicles is an unresolved question. Recent empirical evidence will be provided for the quantal nature of intra-cell vesicle volume, supporting the controversial paradigm of homotypic fusion: basic cytoplasmic granules fuse with each other to create heterogeneously sized vesicles. An EM-algorithm-based method is presented for the conversion of multi-modal to quantal data that provides as by-product estimates of means and variances of basic granule packaging.

Function suggests nano-structure: towards a unified theory for secretion rate, a statistical mechanics approach.

The inventory of secretory granules along the plasma membrane can be viewed as maintained in two restricted compartments. The release-ready pool represents docked granules available for an initial stage of fast, immediate secretion, followed by a second stage of granule set-aside secretion pool, with significantly slower rate. Transmission electron microscopy ultra-structural investigations correlated with electrophysiological techniques and mathematical modelling have allowed the categorization of these secretory vesicle compartments, in which vesicles can be in various states of secretory competence. Using the above-mentioned approaches, the kinetics of single vesicle exocytosis can be worked out. The ultra-fast kinetics, explored in this study, represents the immediately available release-ready pool, in which granules bound to the plasma membrane are exocytosed upon Ca(2+) influx at the SNARE rosette at the base of porosomes. Formalizing Dodge and Rahamimoff findings on the effect of calcium concentration and incorporating the effect of SNARE transient rosette size, we postulate that secretion rate (rate), the number (X) of intracellular calcium ions available for fusion, calcium capacity (0 ≤ M ≤ 5) and the fusion nano-machine size (as measured by the SNARE rosette size K) satisfy the parsimonious M-K relation rate ≈ C × [Ca(2+)](min(X,M))e(-K/2).

Function suggests nano-structure: electrophysiology supports that granule membranes play dice.

Cellular communication depends on membrane fusion mechanisms. SNARE proteins play a fundamental role in all intracellular fusion reactions associated with the life cycle of secretory vesicles, such as vesicle-vesicle and vesicle plasma membrane fusion at the porosome base in the cell plasma membrane. We present growth and elimination (G&E), a birth and death model for the investigation of granule growth, its evoked and spontaneous secretion and their information content. Using a statistical mechanics approach in which SNARE components are viewed as interacting particles, the G&E model provides a simple 'nano-machine' of SNARE self-aggregation behind granule growth and secretion. Results from experimental work, mathematical calculations and statistical modelling suggest that for vesicle growth a minimal aggregation of three SNAREs is required, while for the evoked secretion one SNARE is enough. Furthermore, the required number of SNARE aggregates (which varies between cell types and is nearly proportional to the square root of the mean granule diameter) affects and is statistically identifiable from the size distributions of spontaneous and evoked secreted granules. The new statistical mechanics approach to granule fusion is bound to have a significant changing effect on the investigation of the pathophysiology of secretory mechanisms and methodologies for the investigation of secretion.