Direction pathway analysis of large-scale proteomics data reveals novel features of the insulin action pathway

Decoding the hallmarks of allograft dysfunction with a comprehensive pan-organ transcriptomic atlas

The pathogenesis of allograft (dys)function has been increasingly studied using 'omics'-based technologies, but the focus on individual organs has created knowledge gaps that neither unify nor distinguish pathological mechanisms across allografts. Here we present a comprehensive study of human pan-organ allograft dysfunction, analyzing 150 datasets with more than 12,000 samples across four commonly transplanted solid organs (heart, lung, liver and kidney, n = 1,160, 1,241, 1,216 and 8,853 samples, respectively) that we leveraged to explore transcriptomic differences among allograft dysfunction (delayed graft function, acute rejection and fibrosis), tolerance and stable graft function. We identified genes that correlated robustly with allograft dysfunction across heart, lung, liver and kidney transplantation. Furthermore, we developed a transfer learning omics prediction framework that, by borrowing information across organs, demonstrated superior classifications compared to models trained on single organs. These findings were validated using a single-center prospective kidney transplant cohort study (a collective 329 samples across two timepoints), providing insights supporting the potential clinical utility of our approach. Our study establishes the capacity for machine learning models to learn across organs and presents a transcriptomic transplant resource that can be employed to develop pan-organ biomarkers of allograft dysfunction.

Inferring kinase activity from phosphoproteomic data: Tool comparison and recent applications

Aberrant cellular signaling pathways are a hallmark of cancer and other diseases. One of the most important signaling mechanisms involves protein phosphorylation/dephosphorylation. Protein phosphorylation is catalyzed by protein kinases, and over 530 protein kinases have been identified in the human genome. Aberrant kinase activity is one of the drivers of tumorigenesis and cancer progression and results in altered phosphorylation abundance of downstream substrates. Upstream kinase activity can be inferred from the global collection of phosphorylated substrates. Mass spectrometry-based phosphoproteomic experiments nowadays routinely allow identification and quantitation of >10k phosphosites per biological sample. This substrate phosphorylation footprint can be used to infer upstream kinase activities using tools like Kinase Substrate Enrichment Analysis (KSEA), Posttranslational Modification Substrate Enrichment Analysis (PTM-SEA), and Integrative Inferred Kinase Activity Analysis (INKA). Since the topic of kinase activity inference is very active with many new approaches reported in the past 3 years, we would like to give an overview of the field. In this review, an inventory of kinase activity inference tools, their underlying algorithms, statistical frameworks, kinase-substrate databases, and user-friendliness is presented. The most widely-used tools are compared in-depth. Subsequently, recent applications of the tools are described focusing on clinical tissues and hematological samples. Two main application areas for kinase activity inference tools can be discerned. (1) Maximal biological insights can be obtained from large data sets with group comparisons using multiple complementary tools (e.g., PTM-SEA and KSEA or INKA). (2) In the oncology context where personalized treatment requires analysis of single samples, INKA for example, has emerged as tool that can prioritize actionable kinases for targeted inhibition.

Trans-omic profiling uncovers molecular controls of early human cerebral organoid formation

Defining the molecular networks orchestrating human brain formation is crucial for understanding neurodevelopment and neurological disorders. Challenges in acquiring early brain tissue have incentivized the use of three-dimensional human pluripotent stem cell (hPSC)-derived neural organoids to recapitulate neurodevelopment. To elucidate the molecular programs that drive this highly dynamic process, here, we generate a comprehensive trans-omic map of the phosphoproteome, proteome, and transcriptome of the exit of pluripotency and neural differentiation toward human cerebral organoids (hCOs). These data reveal key phospho-signaling events and their convergence on transcriptional factors to regulate hCO formation. Comparative analysis with developing human and mouse embryos demonstrates the fidelity of our hCOs in modeling embryonic brain development. Finally, we demonstrate that biochemical modulation of AKT signaling can control hCO differentiation. Together, our data provide a comprehensive resource to study molecular controls in human embryonic brain development and provide a guide for the future development of hCO differentiation protocols.

Concurrent inhibition of ALK and SRC kinases disrupts the ALK lung tumor cell proteome

Precision oncology has revolutionized the treatment of ALK-positive lung cancer with targeted therapies. However, an unmet clinical need still to address is the treatment of refractory tumors that contain drug-induced resistant mutations in the driver oncogene or exhibit resistance through the activation of diverse mechanisms. In this study, we established mouse tumor-derived cell models representing the two most prevalent EML4-ALK variants in human lung adenocarcinomas and characterized their proteomic profiles to gain insights into the underlying resistance mechanisms. We showed that Eml4-Alk variant 3 confers a worse response to ALK inhibitors, suggesting its role in promoting resistance to targeted therapy. In addition, proteomic analysis of brigatinib-treated cells revealed the upregulation of SRC kinase, a protein frequently activated in cancer. Co-targeting of ALK and SRC showed remarkable inhibitory effects in both ALK-driven murine and ALK-patient-derived lung tumor cells. This combination induced cell death through a multifaceted mechanism characterized by profound perturbation of the (phospho)proteomic landscape and a synergistic suppressive effect on the mTOR pathway. Our study demonstrates that the simultaneous inhibition of ALK and SRC can potentially overcome resistance mechanisms and enhance clinical outcomes in ALK-positive lung cancer patients. ONE SENTENCE SUMMARY: Co-targeting ALK and SRC enhances ALK inhibitor response in lung cancer by affecting the proteomic profile, offering hope for overcoming resistance and improving clinical outcomes.

Tumor-Extrinsic Axl Expression Shapes an Inflammatory Microenvironment Independent of Tumor Cell Promoting Axl Signaling in Hepatocellular Carcinoma

The activation of the receptor tyrosine kinase Axl by Gas6 is a major driver of tumorigenesis. Despite recent insights, tumor cell-intrinsic and -extrinsic Axl functions are poorly understood in hepatocellular carcinoma (HCC). Thus, we analyzed the cell-specific aspects of Axl in liver cancer cells and in the tumor microenvironment. We show that tumor-intrinsic Axl expression decreased the survival of mice and elevated the number of pulmonary metastases in a model of resection-based tumor recurrence. Axl expression increased the invasion of hepatospheres by the activation of Akt signaling and a partial epithelial-to-mesenchymal transition (EMT). However, the liver tumor burden of Axl+/+ mice induced by diethylnitrosamine plus carbon tetrachloride was reduced compared to systemic Axl-/- mice. Tumors of Axl+/+ mice were highly infiltrated with cytotoxic cells, suggesting a key immune-modulatory role of Axl. Interestingly, hepatocyte-specific Axl deficiency did not alter T cell infiltration, indicating that these changes are independent of tumor cell-intrinsic Axl. In this context, we observed an upregulation of multiple chemokines in Axl+/+ compared to Axl-/- tumors, correlating with HCC patient data. In line with this, Axl is associated with a cytotoxic immune signature in HCC patients. Together these data show that tumor-intrinsic Axl expression fosters progression, while tumor-extrinsic Axl expression shapes an inflammatory microenvironment.

Phosphoproteomics of three exercise modalities identifies canonical signaling and C18ORF25 as an AMPK substrate regulating skeletal muscle function

Exercise induces signaling networks to improve muscle function and confer health benefits. To identify divergent and common signaling networks during and after different exercise modalities, we performed a phosphoproteomic analysis of human skeletal muscle from a cross-over intervention of endurance, sprint, and resistance exercise. This identified 5,486 phosphosites regulated during or after at least one type of exercise modality and only 420 core phosphosites common to all exercise. One of these core phosphosites was S67 on the uncharacterized protein C18ORF25, which we validated as an AMPK substrate. Mice lacking C18ORF25 have reduced skeletal muscle fiber size, exercise capacity, and muscle contractile function, and this was associated with reduced phosphorylation of contractile and Ca2+ handling proteins. Expression of C18ORF25 S66/67D phospho-mimetic reversed the decreased muscle force production. This work defines the divergent and canonical exercise phosphoproteome across different modalities and identifies C18ORF25 as a regulator of exercise signaling and muscle function.

Time-resolved Phosphoproteome and Proteome Analysis Reveals Kinase Signalling on Master Transcription Factors During Myogenesis

Myogenesis is governed by signaling networks that are tightly regulated in a time-dependent manner. Although different protein kinases have been identified, knowledge of the global signaling networks and their downstream substrates during myogenesis remains incomplete. Here, we map the myogenic differentiation of C2C12 cells using phosphoproteomics and proteomics. From these data, we infer global kinase activity and predict the substrates that are involved in myogenesis. We found that multiple mitogen-activated protein kinases (MAPKs) mark the initial wave of signaling cascades. Further phosphoproteomic and proteomic profiling with MAPK1/3 and MAPK8/9 specific inhibitions unveil their shared and distinctive roles in myogenesis. Lastly, we identified and validated the transcription factor nuclear factor 1 X-type (NFIX) as a novel MAPK1/3 substrate and demonstrated the functional impact of NFIX phosphorylation on myogenesis. Altogether, these data characterize the dynamics, interactions, and downstream control of kinase signaling networks during myogenesis on a global scale.

•

Phosphoproteomic and proteomic maps of myogenic differentiation of C2C12 cells

•

Myogenic kinome activity and kinase-substrates prediction using machine learning

•

MAPK1/3 and MAPK8/9 inhibition unveil shared and distinctive effects on myogenesis

•

Validation of NFIX phosphorylation by MAPK1/3 and its impact on myogenesis

UbE3-APA: a bioinformatic strategy to elucidate ubiquitin E3 ligase activities in quantitative proteomics study

MOTIVATION

Ubiquitination is widely involved in protein homeostasis and cell signaling. Ubiquitin E3 ligases are critical regulators of ubiquitination that recognize and recruit specific ubiquitination targets for the final rate-limiting step of ubiquitin transfer reactions. Understanding the ubiquitin E3 ligase activities will provide knowledge in the upstream regulator of the ubiquitination pathway and reveal potential mechanisms in biological processes and disease progression. Recent advances in mass spectrometry-based proteomics have enabled deep profiling of ubiquitylome in a quantitative manner. Yet, functional analysis of ubiquitylome dynamics and pathway activity remains challenging.

RESULTS

Here, we developed a UbE3-APA, a computational algorithm and stand-alone python-based software for Ub E3 ligase Activity Profiling Analysis. Combining an integrated annotation database with statistical analysis, UbE3-APA identifies significantly activated or suppressed E3 ligases based on quantitative ubiquitylome proteomics datasets. Benchmarking the software with published quantitative ubiquitylome analysis confirms the genetic manipulation of SPOP enzyme activity through overexpression and mutation. Application of the algorithm in the re-analysis of a large cohort of ubiquitination proteomics study revealed the activation of PARKIN and the co-activation of other E3 ligases in mitochondria depolarization-induced mitophagy process. We further demonstrated the application of the algorithm in the DIA (data-independent acquisition)-based quantitative ubiquitylome analysis.

AVAILABILITY AND IMPLEMENTATION

Source code and binaries are freely available for download at URL: https://github.com/Chenlab-UMN/Ub-E3-ligase-Activity-Profiling-Analysis, implemented in python and supported on Linux and MS Windows.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Transcriptomic Analysis Identifies A Tolerogenic Dendritic Cell Signature

Dendritic cells (DC) are central to regulating innate and adaptive immune responses. Strategies that modify DC function provide new therapeutic opportunities in autoimmune diseases and transplantation. Current pharmacological approaches can alter DC phenotype to induce tolerogenic DC (tolDC), a maturation-resistant DC subset capable of directing a regulatory immune response that are being explored in current clinical trials. The classical phenotypic characterization of tolDC is limited to cell-surface marker expression and anti-inflammatory cytokine production, although these are not specific. TolDC may be better defined using gene signatures, but there is no consensus definition regarding genotypic markers. We address this shortcoming by analyzing available transcriptomic data to yield an independent set of differentially expressed genes that characterize human tolDC. We validate this transcriptomic signature and also explore gene differences according to the method of tolDC generation. As well as establishing a novel characterization of tolDC, we interrogated its translational utility in vivo, demonstrating this geneset was enriched in the liver, a known tolerogenic organ. Our gene signature will potentially provide greater understanding regarding transcriptional regulators of tolerance and allow researchers to standardize identification of tolDC used for cellular therapy in clinical trials.

PhosR enables processing and functional analysis of phosphoproteomic data

Mass spectrometry (MS)-based phosphoproteomics has revolutionized our ability to profile phosphorylation-based signaling in cells and tissues on a global scale. To infer the action of kinases and signaling pathways in phosphoproteomic experiments, we present PhosR, a set of tools and methodologies implemented in a suite of R packages facilitating comprehensive analysis of phosphoproteomic data. By applying PhosR to both published and new phosphoproteomic datasets, we demonstrate capabilities in data imputation and normalization by using a set of "stably phosphorylated sites" and in functional analysis for inferring active kinases and signaling pathways. In particular, we introduce a "signalome" construction method for identifying a collection of signaling modules to summarize and visualize the interaction of kinases and their collective actions on signal transduction. Together, our data and findings demonstrate the utility of PhosR in processing and generating biological knowledge from MS-based phosphoproteomic data.

Global redox proteome and phosphoproteome analysis reveals redox switch in Akt

Protein oxidation sits at the intersection of multiple signalling pathways, yet the magnitude and extent of crosstalk between oxidation and other post-translational modifications remains unclear. Here, we delineate global changes in adipocyte signalling networks following acute oxidative stress and reveal considerable crosstalk between cysteine oxidation and phosphorylation-based signalling. Oxidation of key regulatory kinases, including Akt, mTOR and AMPK influences the fidelity rather than their absolute activation state, highlighting an unappreciated interplay between these modifications. Mechanistic analysis of the redox regulation of Akt identified two cysteine residues in the pleckstrin homology domain (C60 and C77) to be reversibly oxidized. Oxidation at these sites affected Akt recruitment to the plasma membrane by stabilizing the PIP3 binding pocket. Our data provide insights into the interplay between oxidative stress-derived redox signalling and protein phosphorylation networks and serve as a resource for understanding the contribution of cellular oxidation to a range of diseases.

Multi-omic Profiling Reveals Dynamics of the Phased Progression of Pluripotency

Pluripotency is highly dynamic and progresses through a continuum of pluripotent stem cell states. The two states that bookend the pluripotency continuum, naive and primed, are well characterized, but our understanding of the intermediate states and transitions between them remains incomplete. Here, we dissect the dynamics of pluripotent state transitions underlying pre- to post-implantation epiblast differentiation. Through comprehensive mapping of the proteome, phosphoproteome, transcriptome, and epigenome of embryonic stem cells transitioning from naive to primed pluripotency, we find that rapid, acute, and widespread changes to the phosphoproteome precede ordered changes to the epigenome, transcriptome, and proteome. Reconstruction of the kinase-substrate networks reveals signaling cascades, dynamics, and crosstalk. Distinct waves of global proteomic changes mark discrete phases of pluripotency, with cell-state-specific surface markers tracking pluripotent state transitions. Our data provide new insights into multi-layered control of the phased progression of pluripotency and a foundation for modeling mechanisms regulating pluripotent state transitions (www.stemcellatlas.org).

Mitochondrial CoQ deficiency is a common driver of mitochondrial oxidants and insulin resistance

Insulin resistance in muscle, adipocytes and liver is a gateway to a number of metabolic diseases. Here, we show a selective deficiency in mitochondrial coenzyme Q (CoQ) in insulin-resistant adipose and muscle tissue. This defect was observed in a range of in vitro insulin resistance models and adipose tissue from insulin-resistant humans and was concomitant with lower expression of mevalonate/CoQ biosynthesis pathway proteins in most models. Pharmacologic or genetic manipulations that decreased mitochondrial CoQ triggered mitochondrial oxidants and insulin resistance while CoQ supplementation in either insulin-resistant cell models or mice restored normal insulin sensitivity. Specifically, lowering of mitochondrial CoQ caused insulin resistance in adipocytes as a result of increased superoxide/hydrogen peroxide production via complex II. These data suggest that mitochondrial CoQ is a proximal driver of mitochondrial oxidants and insulin resistance, and that mechanisms that restore mitochondrial CoQ may be effective therapeutic targets for treating insulin resistance.

After we eat, our blood sugar levels increase. To counteract this, the pancreas releases a hormone called insulin. Part of insulin’s effect is to promote the uptake of sugar from the blood into muscle and fat tissue for storage. Under certain conditions, such as obesity, this process can become defective, leading to a condition known as insulin resistance. This condition makes a number of human diseases more likely to develop, including type 2 diabetes. Working out how insulin resistance develops could therefore unveil new treatment strategies for these diseases.

Mitochondria – structures that produce most of a cell’s energy supply – appear to play a role in the development of insulin resistance. Mitochondria convert nutrients such as fats and sugars into molecules called ATP that fuel the many processes required for life. However, ATP production can also generate potentially harmful intermediates often referred to as ‘reactive oxygen species’ or ‘oxidants’. Previous studies have suggested that an increase in the amount of oxidants produced in mitochondria can cause insulin resistance.

Fazakerley et al. therefore set out to identify the reason for increased oxidants in mitochondria, and did so by analysing the levels of proteins and oxidants found in cells grown in the laboratory, and mouse and human tissue samples. This led them to find that concentrations of a molecule called coenzyme Q (CoQ), an essential component of mitochondria that helps to produce ATP, were lower in mitochondria from insulin-resistant fat and muscle tissue. Further experiments suggested a link between the lower levels of CoQ and the higher levels of oxidants in the mitochondria. Replenishing the mitochondria of the lab-grown cells and insulin-resistant mice with CoQ restored ‘normal’ oxidant levels and prevented the development of insulin resistance.

Strategies that aim to increase mitochondria CoQ levels may therefore prevent or reverse insulin resistance. Although CoQ supplements are readily available, swallowing CoQ does not efficiently deliver CoQ to mitochondria in humans, so alternative treatment methods must be found. It is also of interest that statins, common drugs taken by millions of people around the world to lower cholesterol, also lower CoQ and have been reported to increase the risk of developing type 2 diabetes. Further research is therefore needed to investigate whether CoQ might provide the link between statins and type 2 diabetes.

mTORC1 Is a Major Regulatory Node in the FGF21 Signaling Network in Adipocytes

FGF21 improves the metabolic profile of obese animals through its actions on adipocytes. To elucidate the signaling network responsible for mediating these effects, we quantified dynamic changes in the adipocyte phosphoproteome following acute exposure to FGF21. FGF21 regulated a network of 821 phosphosites on 542 proteins. A major FGF21-regulated signaling node was mTORC1/S6K. In contrast to insulin, FGF21 activated mTORC1 via MAPK rather than through the canonical PI3K/AKT pathway. Activation of mTORC1/S6K by FGF21 was surprising because this is thought to contribute to deleterious metabolic effects such as obesity and insulin resistance. Rather, mTORC1 mediated many of the beneficial actions of FGF21 in vitro, including UCP1 and FGF21 induction, increased adiponectin secretion, and enhanced glucose uptake without any adverse effects on insulin action. This study provides a global view of FGF21 signaling and suggests that mTORC1 may act to facilitate FGF21-mediated health benefits in vivo.

OPATs: Omnibus P-value association tests

Combining statistical significances (P-values) from a set of single-locus association tests in genome-wide association studies is a proof-of-principle method for identifying disease-associated genomic segments, functional genes and biological pathways. We review P-value combinations for genome-wide association studies and introduce an integrated analysis tool, Omnibus P-value Association Tests (OPATs), which provides popular analysis methods of P-value combinations. The software OPATs programmed in R and R graphical user interface features a user-friendly interface. In addition to analysis modules for data quality control and single-locus association tests, OPATs provides three types of set-based association test: window-, gene- and biopathway-based association tests. P-value combinations with or without threshold and rank truncation are provided. The significance of a set-based association test is evaluated by using resampling procedures. Performance of the set-based association tests in OPATs has been evaluated by simulation studies and real data analyses. These set-based association tests help boost the statistical power, alleviate the multiple-testing problem, reduce the impact of genetic heterogeneity, increase the replication efficiency of association tests and facilitate the interpretation of association signals by streamlining the testing procedures and integrating the genetic effects of multiple variants in genomic regions of biological relevance. In summary, P-value combinations facilitate the identification of marker sets associated with disease susceptibility and uncover missing heritability in association studies, thereby establishing a foundation for the genetic dissection of complex diseases and traits. OPATs provides an easy-to-use and statistically powerful analysis tool for P-value combinations. OPATs, examples, and user guide can be downloaded from http://www.stat.sinica.edu.tw/hsinchou/genetics/association/OPATs.htm.

A knowledge-based T2-statistic to perform pathway analysis for quantitative proteomic data

Approaches to identify significant pathways from high-throughput quantitative data have been developed in recent years. Still, the analysis of proteomic data stays difficult because of limited sample size. This limitation also leads to the practice of using a competitive null as common approach; which fundamentally implies genes or proteins as independent units. The independent assumption ignores the associations among biomolecules with similar functions or cellular localization, as well as the interactions among them manifested as changes in expression ratios. Consequently, these methods often underestimate the associations among biomolecules and cause false positives in practice. Some studies incorporate the sample covariance matrix into the calculation to address this issue. However, sample covariance may not be a precise estimation if the sample size is very limited, which is usually the case for the data produced by mass spectrometry. In this study, we introduce a multivariate test under a self-contained null to perform pathway analysis for quantitative proteomic data. The covariance matrix used in the test statistic is constructed by the confidence scores retrieved from the STRING database or the HitPredict database. We also design an integrating procedure to retain pathways of sufficient evidence as a pathway group. The performance of the proposed T²-statistic is demonstrated using five published experimental datasets: the T-cell activation, the cAMP/PKA signaling, the myoblast differentiation, and the effect of dasatinib on the BCR-ABL pathway are proteomic datasets produced by mass spectrometry; and the protective effect of myocilin via the MAPK signaling pathway is a gene expression dataset of limited sample size. Compared with other popular statistics, the proposed T²-statistic yields more accurate descriptions in agreement with the discussion of the original publication. We implemented the T²-statistic into an R package T2GA, which is available at https://github.com/roqe/T2GA.

Pathway analysis is a common approach to quickly access the pathways being regulated in the experiments. There are numerous statistics to perform pathway analysis; most of them assume that the genes or proteins are independent of each other for statistical ease. This assumption, however, is unrealistic to the real biological system and may cause false positives in practice. A standard way to address this issue is to measure the associations among genes or proteins. Unfortunately, the estimation of associations requires sufficient sample size, which is usually not available for proteomic data produced by mass spectrometry. In this study, we propose a T²-statistic, which estimates the associations among gene products, to perform pathway analysis for quantitative proteomic data. Instead of calculating the associations directly from data, we use the confidence scores retrieved from protein-protein interaction databases. We also design an integrating procedure to reserve pathways of sufficient evidence as a regulated pathway group. We compare the proposed T²-statistic to other popular statistics using five published experimental datasets, and the T²-statistic yields more accurate descriptions in agreement with the discussion of the original papers.

KinasePA: Phosphoproteomics data annotation using hypothesis driven kinase perturbation analysis

Mass spectrometry (MS)-based quantitative phosphoproteomics has become a key approach for proteome-wide profiling of phosphorylation in tissues and cells. Traditional experimental design often compares a single treatment with a control, whereas increasingly more experiments are designed to compare multiple treatments with respect to a control. To this end, the development of bioinformatic tools that can integrate multiple treatments and visualise kinases and substrates under combinatorial perturbations is vital for dissecting concordant and/or independent effects of each treatment. Here, we propose a hypothesis driven kinase perturbation analysis (KinasePA) to annotate and visualise kinases and their substrates that are perturbed by various combinatorial effects of treatments in phosphoproteomics experiments. We demonstrate the utility of KinasePA through its application to two large-scale phosphoproteomics datasets and show its effectiveness in dissecting kinases and substrates within signalling pathways driven by unique combinations of cellular stimuli and inhibitors. We implemented and incorporated KinasePA as part of the "directPA" R package available from the comprehensive R archive network (CRAN). Furthermore, KinasePA also has an interactive web interface that can be readily applied to annotate user provided phosphoproteomics data (http://kinasepa.pengyiyang.org).

Integrated expression analysis of muscle hypertrophy identifies Asb2 as a negative regulator of muscle mass

The transforming growth factor-β (TGF-β) signaling network is a critical regulator of skeletal muscle mass and function and, thus, is an attractive therapeutic target for combating muscle disease, but the underlying mechanisms of action remain undetermined. We report that follistatin-based interventions (which modulate TGF-β network activity) can promote muscle hypertrophy that ameliorates aging-associated muscle wasting. However, the muscles of old sarcopenic mice demonstrate reduced response to follistatin compared with healthy young-adult musculature. Quantitative proteomic and transcriptomic analyses of young-adult muscles identified a transcription/translation signature elicited by follistatin exposure, which included repression of ankyrin repeat and SOCS box protein 2 (Asb2). Increasing expression of ASB2 reduced muscle mass, thereby demonstrating that Asb2 is a TGF-β network-responsive negative regulator of muscle mass. In contrast to young-adult muscles, sarcopenic muscles do not exhibit reduced ASB2 abundance with follistatin exposure. Moreover, preventing repression of ASB2 in young-adult muscles diminished follistatin-induced muscle hypertrophy. These findings provide insight into the program of transcription and translation events governing follistatin-mediated adaptation of skeletal muscle attributes and identify Asb2 as a regulator of muscle mass implicated in the potential mechanistic dysfunction between follistatin-mediated muscle growth in young and old muscles.

Inferring data-specific micro-RNA function through the joint ranking of micro-RNA and pathways from matched micro-RNA and gene expression data

MOTIVATION

In practice, identifying and interpreting the functional impacts of the regulatory relationships between micro-RNA and messenger-RNA is non-trivial. The sheer scale of possible micro-RNA and messenger-RNA interactions can make the interpretation of results difficult.

RESULTS

We propose a supervised framework, pMim, built upon concepts of significance combination, for jointly ranking regulatory micro-RNA and their potential functional impacts with respect to a condition of interest. Here, pMim directly tests if a micro-RNA is differentially expressed and if its predicted targets, which lie in a common biological pathway, have changed in the opposite direction. We leverage the information within existing micro-RNA target and pathway databases to stabilize the estimation and annotation of micro-RNA regulation making our approach suitable for datasets with small sample sizes. In addition to outputting meaningful and interpretable results, we demonstrate in a variety of datasets that the micro-RNA identified by pMim, in comparison to simpler existing approaches, are also more concordant with what is described in the literature.

AVAILABILITY AND IMPLEMENTATION

This framework is implemented as an R function, pMim, in the package sydSeq available from http://www.ellispatrick.com/r-packages.

CONTACT

jean.yang@sydney.edu.au

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Structural insights into the organization of the cavin membrane coat complex

Caveolae are cell-surface membrane invaginations that play critical roles in cellular processes including signaling and membrane homeostasis. The cavin proteins, in cooperation with caveolins, are essential for caveola formation. Here we show that a minimal N-terminal domain of the cavins, termed HR1, is required and sufficient for their homo- and hetero-oligomerization. Crystal structures of the mouse cavin1 and zebrafish cavin4a HR1 domains reveal highly conserved trimeric coiled-coil architectures, with intersubunit interactions that determine the specificity of cavin-cavin interactions. The HR1 domain contains a basic surface patch that interacts with polyphosphoinositides and coordinates with additional membrane-binding sites within the cavin C terminus to facilitate membrane association and remodeling. Electron microscopy of purified cavins reveals the existence of large assemblies, composed of a repeating rod-like structural element, and we propose that these structures polymerize through membrane-coupled interactions to form the unique striations observed on the surface of caveolae in vivo.