Modeling the transport of nuclear proteins along single skeletal muscle cells.

Skeletal muscle cells contain hundreds of myonuclei within a shared cytoplasm, presenting unique challenges for regulating gene expression. Certain transcriptional programs (e.g., postsynaptic machinery) are segregated to specialized domains, while others (e.g., contractile proteins) do not show spatial confinement. Furthermore, local stimuli, such as denervation, can induce transcriptional responses that are propagated along the muscle cells. Regulated transport of nuclear proteins (e.g., transcription factors) between myonuclei represents a potential mechanism for coordinating gene expression. However, the principles underlying the transport of nuclear proteins within multinucleated cells remain poorly defined. Here we used a mosaic transfection model to create myotubes that contained exactly one myonucleus expressing a fluorescent nuclear reporter and monitored its distribution among all myonuclei. We found that the transport properties of these model nuclear proteins in myotubes depended on molecular weight and nuclear import rate, as well as on myotube width. Interestingly, muscle hypertrophy increased the transport of high molecular weight nuclear proteins, while atrophy restricted the transport of smaller nuclear proteins. We have developed a mathematical model of nuclear protein transport within a myotube that recapitulates the results of our in vitro experiments. To test the relevance to nuclear proteins expressed in skeletal muscle, we studied the transport of two transcription factors-aryl hydrocarbon receptor nuclear translocator and sine oculis homeobox 1-and found that their distributions were similar to the reporter proteins with corresponding molecular weights. Together, these results define a set of variables that can be used to predict the spatial distributions of nuclear proteins within a myotube.

EviNet: a web platform for network enrichment analysis with flexible definition of gene sets.

The new web resource EviNet provides an easily run interface to network enrichment analysis for exploration of novel, experimentally defined gene sets. The major advantages of this analysis are (i) applicability to any genes found in the global network rather than only to those with pathway/ontology term annotations, (ii) ability to connect genes via different molecular mechanisms rather than within one high-throughput platform, and (iii) statistical power sufficient to detect enrichment of very small sets, down to individual genes. The users' gene sets are either defined prior to upload or derived interactively from an uploaded file by differential expression criteria. The pathways and networks used in the analysis can be chosen from the collection menu. The calculation is typically done within seconds or minutes and the stable URL is provided immediately. The results are presented in both visual (network graphs) and tabular formats using jQuery libraries. Uploaded data and analysis results are kept in separated project directories not accessible by other users. EviNet is available at https://www.evinet.org/.

The novel enterochromaffin marker Lmx1a regulates serotonin biosynthesis in enteroendocrine cell lineages downstream of Nkx2.2.

Intestinal hormone-producing cells represent the largest endocrine system in the body, but remarkably little is known about enteroendocrine cell type specification in the embryo and adult. We analyzed stage- and cell type-specific deletions of Nkx2.2 and its functional domains in order to characterize its role in the development and maintenance of enteroendocrine cell lineages in the mouse duodenum and colon. Although Nkx2.2 regulates enteroendocrine cell specification in the duodenum at all stages examined, it controls the differentiation of progressively fewer enteroendocrine cell populations when deleted from Ngn3(+) progenitor cells or in the adult duodenum. During embryonic development Nkx2.2 regulates all enteroendocrine cell types, except gastrin and preproglucagon. In developing Ngn3(+) enteroendocrine progenitor cells, Nkx2.2 is not required for the specification of neuropeptide Y and vasoactive intestinal polypeptide, indicating that a subset of these cell populations derive from an Nkx2.2-independent lineage. In adult duodenum, Nkx2.2 becomes dispensable for cholecystokinin and secretin production. In all stages and Nkx2.2 mutant conditions, serotonin-producing enterochromaffin cells were the most severely reduced enteroendocrine lineage in the duodenum and colon. We determined that the transcription factor Lmx1a is expressed in enterochromaffin cells and functions downstream of Nkx2.2. Lmx1a-deficient mice have reduced expression of Tph1, the rate-limiting enzyme for serotonin biosynthesis. These data clarify the function of Nkx2.2 in the specification and homeostatic maintenance of enteroendocrine populations, and identify Lmx1a as a novel enterochromaffin cell marker that is also essential for the production of the serotonin biosynthetic enzyme Tph1.

Differential requirement of SUFU in tissue development discovered in a hypomorphic mouse model.

Suppressor of Fused (SUFU) is an essential negative regulator of the Hedgehog (HH) pathway and involved in GLI transcription factor regulation. Due to early embryonic lethality of Sufu-/- mice, investigations of SUFU's role later in development are limited to conditional, tissue-specific knockout models. In this study we developed a mouse model (SufuEx456(fl)/Ex456(fl)) with hypomorphic features where embryos were viable up to E18.5, although with a spectrum of developmental defects of varying severity, including polydactyly, exencephaly and omphalocele. Development of certain tissues, like the skeleton, was more affected than that of others such as skin, which remained largely normal. Interestingly, no apparent changes in the dorso-ventral patterning of the neural tube at E9.0 could be seen. Thus, this model provides an opportunity to globally study SUFU's molecular function in organogenesis beyond E9.5. Molecularly, SufuEx456(fl)/Ex456(fl) embryos displayed aberrant mRNA splicing and drastically reduced levels of Sufu wild-type mRNA and SUFU protein in all tissues. As a consequence, at E9.5 the levels of all three different GLI proteins were reduced. Interestingly, despite the reduction of GLI3 protein levels, the critical ratio of the GLI3 full-length transcriptional activator versus GLI3 truncated repressor remained unchanged compared to wild-type embryos. This suggests that the limited amount of SUFU protein present is sufficient for GLI processing but not for stabilization. Our data demonstrate that tissue development is differentially affected in response to the reduced SUFU levels, providing novel insight regarding the requirements of different levels of SUFU for proper organogenesis.

Characterization of association of human mitochondrial lysyl-tRNA synthetase with HIV-1 Pol and tRNA3Lys.

BACKGROUND: An important step in human immunodeficiency virus type 1 (HIV-1) replication is the packaging of tRNA3Lys from the host cell, which plays the role of primer RNA in the process of initiation of reverse transcription. The viral GagPol polyprotein precursor, and the human mitochondrial lysyl-tRNA synthetase (mLysRS) from the host cell, have been proposed to be involved in the packaging process. More specifically, the catalytic domain of mLysRS is supposed to interact with the transframe (TF or p6*) and integrase (IN) domains of the Pol region of the GagPol polyprotein. RESULTS: In this work, we report a quantitative characterization of the protein:protein interactions between mLysRS and its viral partners, the Pol polyprotein, and the isolated integrase and transframe domains of Pol. A dissociation constant of 1.3 ± 0.2 nM was determined for the Pol:mLysRS interaction, which exemplifies the robustness of this association. The protease and reverse transcriptase domains of GagPol are dispensable in this association, but the TF and IN domains have to be connected by a linker polypeptide to recapitulate a high affinity partner for mLysRS. The binding of the viral proteins to mLysRS does not dramatically enhance the binding affinity of mLysRS for tRNA3Lys. CONCLUSIONS: These data support the conclusion that the complex formed between GagPol, mLysRS and tRNA3Lys, which involves direct interactions between the IN and TF domains of Pol with mLysRS, is more robust than suggested by the previous models supposed to be involved in the packaging of tRNA3Lys into HIV-1 particles.

Dictionary Learning Phase Retrieval from Noisy Diffraction Patterns.

This paper proposes a novel algorithm for image phase retrieval, i.e., for recovering complex-valued images from the amplitudes of noisy linear combinations (often the Fourier transform) of the sought complex images. The algorithm is developed using the alternating projection framework and is aimed to obtain high performance for heavily noisy (Poissonian or Gaussian) observations. The estimation of the target images is reformulated as a sparse regression, often termed sparse coding, in the complex domain. This is accomplished by learning a complex domain dictionary from the data it represents via matrix factorization with sparsity constraints on the code (i.e., the regression coefficients). Our algorithm, termed dictionary learning phase retrieval (DLPR), jointly learns the referred to dictionary and reconstructs the unknown target image. The effectiveness of DLPR is illustrated through experiments conducted on complex images, simulated and real, where it shows noticeable advantages over the state-of-the-art competitors.

Multivalent insulin receptor activation using insulin-DNA origami nanostructures.

Insulin binds the insulin receptor (IR) and regulates anabolic processes in target tissues. Impaired IR signalling is associated with multiple diseases, including diabetes, cancer and neurodegenerative disorders. IRs have been reported to form nanoclusters at the cell membrane in several cell types, even in the absence of insulin binding. Here we exploit the nanoscale spatial organization of the IR to achieve controlled multivalent receptor activation. To control insulin nanoscale spatial organization and valency, we developed rod-like insulin-DNA origami nanostructures carrying different numbers of insulin molecules with defined spacings. Increasing the insulin valency per nanostructure markedly extended the residence time of insulin-DNA origami nanostructures at the receptors. Both insulin valency and spacing affected the levels of IR activation in adipocytes. Moreover, the multivalent insulin design associated with the highest levels of IR activation also induced insulin-mediated transcriptional responses more effectively than the corresponding monovalent insulin nanostructures. In an in vivo zebrafish model of diabetes, treatment with multivalent-but not monovalent-insulin nanostructures elicited a reduction in glucose levels. Our results show that the control of insulin multivalency and spatial organization with nanoscale precision modulates the IR responses, independent of the insulin concentration. Therefore, we propose insulin nanoscale organization as a design parameter in developing new insulin therapies.

Mustn1 is a smooth muscle cell-secreted microprotein that modulates skeletal muscle extracellular matrix composition.

OBJECTIVE: Skeletal muscle plasticity and remodeling are critical for adapting tissue function to use, disuse, and regeneration. The aim of this study was to identify genes and molecular pathways that regulate the transition from atrophy to compensatory hypertrophy or recovery from injury. Here, we have used a mouse model of hindlimb unloading and reloading, which causes skeletal muscle atrophy, and compensatory regeneration and hypertrophy, respectively. METHODS: We analyzed mouse skeletal muscle at the transition from hindlimb unloading to reloading for changes in transcriptome and extracellular fluid proteome. We then used qRT-PCR, immunohistochemistry, and bulk and single-cell RNA sequencing data to determine Mustn1 gene and protein expression, including changes in gene expression in mouse and human skeletal muscle with different challenges such as exercise and muscle injury. We generated Mustn1-deficient genetic mouse models and characterized them in vivo and ex vivo with regard to muscle function and whole-body metabolism. We isolated smooth muscle cells and functionally characterized them, and performed transcriptomics and proteomics analysis of skeletal muscle and aorta of Mustn1-deficient mice. RESULTS: We show that Mustn1 (Musculoskeletal embryonic nuclear protein 1, also known as Mustang) is highly expressed in skeletal muscle during the early stages of hindlimb reloading. Mustn1 expression is transiently elevated in mouse and human skeletal muscle in response to intense exercise, resistance exercise, or injury. We find that Mustn1 expression is highest in smooth muscle-rich tissues, followed by skeletal muscle fibers. Muscle from heterozygous Mustn1-deficient mice exhibit differences in gene expression related to extracellular matrix and cell adhesion, compared to wild-type littermates. Mustn1-deficient mice have normal muscle and aorta function and whole-body glucose metabolism. We show that Mustn1 is secreted from smooth muscle cells, and that it is present in arterioles of the muscle microvasculature and in muscle extracellular fluid, particularly during the hindlimb reloading phase. Proteomics analysis of muscle from Mustn1-deficient mice confirms differences in extracellular matrix composition, and female mice display higher collagen content after chemically induced muscle injury compared to wild-type littermates. CONCLUSIONS: We show that, in addition to its previously reported intracellular localization, Mustn1 is a microprotein secreted from smooth muscle cells into the muscle extracellular space. We explore its role in muscle ECM deposition and remodeling in homeostasis and upon muscle injury. The role of Mustn1 in fibrosis and immune infiltration upon muscle injury and dystrophies remains to be investigated, as does its potential for therapeutic interventions.

A homeodomain feedback circuit underlies step-function interpretation of a Shh morphogen gradient during ventral neural patterning.

The deployment of morphogen gradients is a core strategy to establish cell diversity in developing tissues, but little is known about how small differences in the concentration of extracellular signals are translated into robust patterning output in responding cells. We have examined the activity of homeodomain proteins, which are presumed to operate downstream of graded Shh signaling in neural patterning, and describe a feedback circuit between the Shh pathway and homeodomain transcription factors that establishes non-graded regulation of Shh signaling activity. Nkx2 proteins intrinsically strengthen Shh responses in a feed-forward amplification and are required for ventral floor plate and p3 progenitor fates. Conversely, Pax6 has an opposing function to antagonize Shh signaling, which provides intrinsic resistance to Shh responses and is important to constrain the inductive capacity of the Shh gradient over time. Our data further suggest that patterning of floor plate cells and p3 progenitors is gated by a temporal switch in neuronal potential, rather than by different Shh concentrations. These data establish that dynamic, non-graded changes in responding cells are essential for Shh morphogen interpretation, and provide a rationale to explain mechanistically the phenomenon of cellular memory of morphogen exposure.

Robust derivation of transplantable dopamine neurons from human pluripotent stem cells by timed retinoic acid delivery.

Stem cell therapies for Parkinson's disease (PD) have entered first-in-human clinical trials using a set of technically related methods to produce mesencephalic dopamine (mDA) neurons from human pluripotent stem cells (hPSCs). Here, we outline an approach for high-yield derivation of mDA neurons that principally differs from alternative technologies by utilizing retinoic acid (RA) signaling, instead of WNT and FGF8 signaling, to specify mesencephalic fate. Unlike most morphogen signals, where precise concentration determines cell fate, it is the duration of RA exposure that is the key-parameter for mesencephalic specification. This concentration-insensitive patterning approach provides robustness and reduces the need for protocol-adjustments between hPSC-lines. RA-specified progenitors promptly differentiate into functional mDA neurons in vitro, and successfully engraft and relieve motor deficits after transplantation in a rat PD model. Our study provides a potential alternative route for cell therapy and disease modelling that due to its robustness could be particularly expedient when use of autologous- or immunologically matched cells is considered.

Interaction with factor inhibiting HIF-1 defines an additional mode of cross-coupling between the Notch and hypoxia signaling pathways.

Cells adapt to hypoxia by a cellular response, where hypoxia-inducible factor 1alpha (HIF-1alpha) becomes stabilized and directly activates transcription of downstream genes. In addition to this "canonical" response, certain aspects of the pathway require integration with Notch signaling, i.e., HIF-1alpha can interact with the Notch intracellular domain (ICD) to augment the Notch downstream response. In this work, we demonstrate an additional level of complexity in this cross-talk: factor-inhibiting HIF-1 (FIH-1) regulates not only HIF activity, but also the Notch signaling output and, in addition, plays a role in how Notch signaling modulates the hypoxic response. We show that FIH-1 hydroxylates Notch ICD at two residues (N(1945) and N(2012)) that are critical for the function of Notch ICD as a transactivator within cells and during neurogenesis and myogenesis in vivo. FIH-1 negatively regulates Notch activity and accelerates myogenic differentiation. In its modulation of the hypoxic response, Notch ICD enhances recruitment of HIF-1alpha to its target promoters and derepresses HIF-1alpha function. Addition of FIH-1, which has a higher affinity for Notch ICD than for HIF-1alpha, abrogates the derepression, suggesting that Notch ICD sequesters FIH-1 away from HIF-1alpha. In conclusion, the data reveal posttranslational modification of the activated form of the Notch receptor and an intricate mode of cross-coupling between the Notch and hypoxia signaling pathways.

Nonnegative Blind Source Separation for Ill-Conditioned Mixtures via John Ellipsoid.

Nonnegative blind source separation (nBSS) is often a challenging inverse problem, namely, when the mixing system is ill-conditioned. In this work, we focus on an important nBSS instance, known as hyperspectral unmixing (HU) in remote sensing. HU is a matrix factorization problem aimed at factoring the so-called endmember matrix, holding the material hyperspectral signatures, and the abundance matrix, holding the material fractions at each image pixel. The hyperspectral signatures are usually highly correlated, leading to a fast decay of the singular values (and, hence, high condition number) of the endmember matrix, so HU often introduces an ill-conditioned nBSS scenario. We introduce a new theoretical framework to attack such tough scenarios via the John ellipsoid (JE) in functional analysis. The idea is to identify the maximum volume ellipsoid inscribed in the data convex hull, followed by affinely mapping such ellipsoid into a Euclidean ball. By applying the same affine mapping to the data mixtures, we prove that the endmember matrix associated with the mapped data has condition number 1, the lowest possible, and that these (preconditioned) endmembers form a regular simplex. Exploiting this regular structure, we design a novel nBSS criterion with a provable identifiability guarantee and devise an algorithm to realize the criterion. Moreover, for the first time, the optimization problem for computing JE is exactly solved for a large-scale instance; our solver employs a split augmented Lagrangian shrinkage algorithm with all proximal operators solved by closed-form solutions. The competitiveness of the proposed method is illustrated by numerical simulations and real data experiments.

Near-infrared hyperspectral unmixing based on a minimum volume criterion for fast and accurate chemometric characterization of counterfeit tablets.

A rapid detection of the nonauthenticity of suspect tablets is a key first step in the fight against pharmaceutical counterfeiting. The chemical characterization of these tablets is the logical next step to evaluate their impact on patient health and help authorities in tracking their source. Hyperspectral unmixing of near-infrared (NIR) image data is an emerging effective technology to infer the number of compounds, their spectral signatures, and the mixing fractions in a given tablet, with a resolution of a few tens of micrometers. In a linear mixing scenario, hyperspectral vectors belong to a simplex whose vertices correspond to the spectra of the compounds present in the sample. SISAL (simplex identification via split augmented Lagrangian), MVSA (minimum volume simplex analysis), and MVES (minimum-volume enclosing simplex) are recent algorithms designed to identify the vertices of the minimum volume simplex containing the spectral vectors and the mixing fractions at each pixel (vector). This work demonstrates the usefulness of these techniques, based on minimum volume criteria, for unmixing NIR hyperspectral data of tablets. The experiments herein reported show that SISAL/MVSA and MVES largely outperform MCR-ALS (multivariate curve resolution-alternating least-squares), which is considered the state-of-the-art in spectral unmixing for analytical chemistry. These experiments are based on synthetic data (studying the effect of noise and the presence/absence of pure pixels) and on a real data set composed of NIR images of counterfeit tablets.

Nonlocal Sparse Tensor Factorization for Semiblind Hyperspectral and Multispectral Image Fusion.

Combining a high-spatial-resolution multispectral image (HR-MSI) with a low-spatial-resolution hyperspectral image (LR-HSI) has become a common way to enhance the spatial resolution of the HSI. The existing state-of-the-art LR-HSI and HR-MSI fusion methods are mostly based on the matrix factorization, where the matrix data representation may be hard to fully make use of the inherent structures of 3-D HSI. We propose a nonlocal sparse tensor factorization approach, called the NLSTF_SMBF, for the semiblind fusion of HSI and MSI. The proposed method decomposes the HSI into smaller full-band patches (FBPs), which, in turn, are factored as dictionaries of the three HSI modes and a sparse core tensor. This decomposition allows to solve the fusion problem as estimating a sparse core tensor and three dictionaries for each FBP. Similar FBPs are clustered together, and they are assumed to share the same dictionaries to make use of the nonlocal self-similarities of the HSI. For each group, we learn the dictionaries from the observed HR-MSI and LR-HSI. The corresponding sparse core tensor of each FBP is computed via tensor sparse coding. Two distinctive features of NLSTF_SMBF are that: 1) it is blind with respect to the point spread function (PSF) of the hyperspectral sensor and 2) it copes with spatially variant PSFs. The experimental results provide the evidence of the advantages of the NLSTF_SMBF method over the existing state-of-the-art methods, namely, in semiblind scenarios.

A Shh/Gli-driven three-node timer motif controls temporal identity and fate of neural stem cells.

How time is measured by neural stem cells during temporal neurogenesis has remained unresolved. By combining experiments and computational modeling, we define a Shh/Gli-driven three-node timer underlying the sequential generation of motor neurons (MNs) and serotonergic neurons in the brainstem. The timer is founded on temporal decline of Gli-activator and Gli-repressor activities established through down-regulation of Gli transcription. The circuitry conforms an incoherent feed-forward loop, whereby Gli proteins not only promote expression of Phox2b and thereby MN-fate but also account for a delayed activation of a self-promoting transforming growth factor-ß (Tgfß) node triggering a fate switch by repressing Phox2b. Hysteresis and spatial averaging by diffusion of Tgfß counteract noise and increase temporal accuracy at the population level, providing a functional rationale for the intrinsically programmed activation of extrinsic switch signals in temporal patterning. Our study defines how time is reliably encoded during the sequential specification of neurons.

A Convergent Image Fusion Algorithm Using Scene-Adapted Gaussian-Mixture-Based Denoising.

We propose a new approach to image fusion, inspired by the recent plug-and-play (PnP) framework. In PnP, a denoiser is treated as a black-box and plugged into an iterative algorithm, taking the place of the proximity operator of some convex regularizer, which is formally equivalent to a denoising operation. This approach offers flexibility and excellent performance, but convergence may be hard to analyze, as most state-of-the-art denoisers lack an explicit underlying objective function. Here, we propose using a scene-adapted denoiser (i.e., targeted to the specific scene being imaged) plugged into the iterations of the alternating direction method of multipliers (ADMM). This approach, which is a natural choice for image fusion problems, not only yields state-of-the-art results, but it also allows proving convergence of the resulting algorithm. The proposed method is tested on two different problems: hyperspectral fusion/sharpening and fusion of blurred-noisy image pairs.

Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization.

Fusing a low spatial resolution hyperspectral image (LR-HSI) with a high spatial resolution multispectral image (HR-MSI) to obtain a high spatial resolution hyperspectral image (HR-HSI) has attracted increasing interest in recent years. In this paper, we propose a coupled sparse tensor factorization (CSTF) based approach for fusing such images. In the proposed CSTF method, we consider an HR-HSI as a three-dimensional tensor and redefine the fusion problem as the estimation of a core tensor and dictionaries of the three modes. The high spatial-spectral correlations in the HR-HSI are modeled by incorporating a regularizer which promotes sparse core tensors. The estimation of the dictionaries and the core tensor are formulated as a coupled tensor factorization of the LR-HSI and of the HR-MSI. Experiments on two remotely sensed HSIs demonstrate the superiority of the proposed CSTF algorithm over current state-of-the-art HSI-MSI fusion approaches.

Phase unwrapping via graph cuts.

Phase unwrapping is the inference of absolute phase from modulo-2pi phase. This paper introduces a new energy minimization framework for phase unwrapping. The considered objective functions are first-order Markov random fields. We provide an exact energy minimization algorithm, whenever the corresponding clique potentials are convex, namely for the phase unwrapping classical Lp norm, with p > or = 1. Its complexity is KT (n, 3n), where K is the length of the absolute phase domain measured in 2pi units and T (n, m) is the complexity of a max-flow computation in a graph with n nodes and m edges. For nonconvex clique potentials, often used owing to their discontinuity preserving ability, we face an NP-hard problem for which we devise an approximate solution. Both algorithms solve integer optimization problems by computing a sequence of binary optimizations, each one solved by graph cut techniques. Accordingly, we name the two algorithms PUMA, for phase unwrappping max-flow/min-cut. A set of experimental results illustrates the effectiveness of the proposed approach and its competitiveness in comparison with state-of-the-art phase unwrapping algorithms.

A new twIst: two-step iterative shrinkage/thresholding algorithms for image restoration.

Iterative shrinkage/thresholding (IST) algorithms have been recently proposed to handle a class of convex unconstrained optimization problems arising in image restoration and other linear inverse problems. This class of problems results from combining a linear observation model with a nonquadratic regularizer (e.g., total variation or wavelet-based regularization). It happens that the convergence rate of these IST algorithms depends heavily on the linear observation operator, becoming very slow when this operator is ill-conditioned or ill-posed. In this paper, we introduce two-step IST (TwIST) algorithms, exhibiting much faster convergence rate than IST for ill-conditioned problems. For a vast class of nonquadratic convex regularizers (l(p) norms, some Besov norms, and total variation), we show that TwIST converges to a minimizer of the objective function, for a given range of values of its parameters. For noninvertible observation operators, we introduce a monotonic version of TwIST (MTwIST); although the convergence proof does not apply to this scenario, we give experimental evidence that MTwIST exhibits similar speed gains over IST. The effectiveness of the new methods are experimentally confirmed on problems of image deconvolution and of restoration with missing samples.

Majorization-minimization algorithms for wavelet-based image restoration.

Standard formulations of image/signal deconvolution under wavelet-based priors/regularizers lead to very high-dimensional optimization problems involving the following difficulties: the non-Gaussian (heavy-tailed) wavelet priors lead to objective functions which are nonquadratic, usually nondifferentiable, and sometimes even nonconvex; the presence of the convolution operator destroys the separability which underlies the simplicity of wavelet-based denoising. This paper presents a unified view of several recently proposed algorithms for handling this class of optimization problems, placing them in a common majorization-minimization (MM) framework. One of the classes of algorithms considered (when using quadratic bounds on nondifferentiable log-priors) shares the infamous "singularity issue" (SI) of "iteratively reweighted least squares" (IRLS) algorithms: the possibility of having to handle infinite weights, which may cause both numerical and convergence issues. In this paper, we prove several new results which strongly support the claim that the SI does not compromise the usefulness of this class of algorithms. Exploiting the unified MM perspective, we introduce a new algorithm, resulting from using l1 bounds for nonconvex regularizers; the experiments confirm the superior performance of this method, when compared to the one based on quadratic majorization. Finally, an experimental comparison of the several algorithms, reveals their relative merits for different standard types of scenarios.