A novel brain partition highlights the modular skeleton shared by structure and function

Elucidating the intricate relationship between brain structure and function, both in healthy and pathological conditions, is a key challenge for modern neuroscience. Recent progress in neuroimaging has helped advance our understanding of this important issue, with diffusion images providing information about structural connectivity (SC) and functional magnetic resonance imaging shedding light on resting state functional connectivity (rsFC). Here, we adopt a systems approach, relying on modular hierarchical clustering, to study together SC and rsFC datasets gathered independently from healthy human subjects. Our novel approach allows us to find a common skeleton shared by structure and function from which a new, optimal, brain partition can be extracted. We describe the emerging common structure-function modules (SFMs) in detail and compare them with commonly employed anatomical or functional parcellations. Our results underline the strong correspondence between brain structure and resting-state dynamics as well as the emerging coherent organization of the human brain.

Structure-function analysis of USP1: insights into the role of Ser313 phosphorylation site and the effect of cancer-associated mutations on autocleavage

Background: In complex with its cofactor UAF1, the USP1 deubiquitinase plays an important role in cellular processes related to cancer, including the response to DNA damage. The USP1/UAF1 complex is emerging as a novel target in cancer therapy, but several aspects of its function and regulation remain to be further clarified. These include the role of the serine 313 phosphorylation site, the relative contribution of different USP1 sequence motifs to UAF1 binding, and the potential effect of cancer-associated mutations on USP1 regulation by autocleavage. Methods: We have generated a large set of USP1 structural variants, including a catalytically inactive form (C90S), non-phosphorylatable (S313A) and phosphomimetic (S313D) mutants, deletion mutants lacking potential UAF1 binding sites, a mutant (GG/AA) unable to undergo autocleavage at the well-characterized G670/G671 diglycine motif, and four USP1 mutants identified in tumor samples that cluster around this cleavage site (G667A, L669P, K673T and A676T). Using cell-based assays, we have determined the ability of these mutants to bind UAF1, to reverse DNA damage-induced monoubiquitination of PCNA, and to undergo autocleavage. Results: A non-phosphorylatable S313A mutant of USP1 retained the ability to bind UAF1 and to reverse PCNA ubiquitination in cell-based assays. Regardless of the presence of a phosphomimetic S313D mutation, deletion of USP1 fragment 420-520 disrupted UAF1 binding, as determined using a nuclear relocation assay. The UAF1 binding site in a second UAF1-interacting DUB, USP46, was mapped to a region homologous to USP1(420-520). Regarding USP1 autocleavage, co-expression of the C90S and GG/AA mutants did not result in cleavage, while the cancer-associated mutation L669P was found to reduce cleavage efficiency. Conclusions: USP1 phosphorylation at S313 is not critical for PCNA deubiquitination, neither for binding to UAF1 in a cellular environment. In this context, USP1 amino acid motif 420-520 is necessary and sufficient for UAF1 binding. This motif, and a homologous amino acid segment that mediates USP46 binding to UAF1, map to the Fingers sub-domain of these DUBs. On the other hand, our results support the view that USP1 autocleavage may occur in cis, and can be altered by a cancer-associated mutation.

A Radial Basis Function Approach to Financial Time Series Analysis

Nonlinear multivariate statistical techniques on fast computers offer the potential to capture more of the dynamics of the high dimensional, noisy systems underlying financial markets than traditional models, while making fewer restrictive assumptions. This thesis presents a collection of practical techniques to address important estimation and confidence issues for Radial Basis Function networks arising from such a data driven approach, including efficient methods for parameter estimation and pruning, a pointwise prediction error estimator, and a methodology for controlling the "data mining'' problem. Novel applications in the finance area are described, including customized, adaptive option pricing and stock price prediction.

Muscle function and body composition profile in adolescents withrestrictive anorexia nervosa: Does resistance training help?

The aim of this study was to analyze the effects of short-term resistance training on the body composition profile and muscle function in a group of Anorexia Nervosa restricting type (AN-R) patients. The sample consisted of AN-R female adolescents (12.8 ± 0.6 years) allocated into the control and intervention groups (n¼18 each). Body composition and relative strength were assessed at baseline, after 8 weeks and 4 weeks following the intervention. Body mass index (BMI) increased throughout the study (p = 0.011). Significant skeletal muscle mass (SMM) gains were found in the intervention group (p = 0.045, d = 0.6) that correlated to the change in BMI (r = 0.51, p < 0.031). Meanwhile, fat mass (FM) gains were significant in the control group (p = 0.047, d = 0.6) and correlated (r > 0.60) with change in BMI in both the groups. Significant relative strength increases (p < 0.001) were found in the intervention group and were sustained over time.

Confirmation of Data Mining Based Predictions of Protein Function

King, R. D. and Wise, P. H. and Clare, A. (2004) Confirmation of Data Mining Based Predictions of Protein Function. Bioinformatics 20(7), 1110-1118

Predicting gene function in Saccharomyces cerevisiae

Clare, A. and King R.D. (2003) Predicting gene function in Saccharomyces cerevisiae. 2nd European Conference on Computational Biology (ECCB '03). (published as a journal supplement in Bioinformatics 19: ii42-ii49)

Automated cross-modal mapping in robotic eye/hand systems using plastic radial basis function networks

Q. Meng and M. H Lee, Automated cross-modal mapping in robotic eye/hand systems using plastic radial basis function networks, Connection Science, 19(1), pp 25-52, 2007.

Error-driven active learning in growing radial basis function networks for early robot learning

Q. Meng and M.H. Lee, 'Error-driven active learning in growing radial basis function networks for early robot learning', 2006 IEEE International Conference on Robotics and Automation (IEEE ICRA 2006), 2984-90, Orlando, Florida, USA.

Limits of ventricular function: from athlete's heart to a failing heart

The interest in the study of ventricular function has grown considerably in the last decades. In this review, we analyse the extreme values of ventricular function as obtained with Doppler echocardiography. We mainly focus on the parameters that have been used throughout the history of Doppler echocardiography to assess left ventricular (LV) systolic and diastolic function. The ‘athlete's heart’ would be the highest expression of ventricular function whereas its lowest expression is represented by the failing heart, independently from the original aetiology leading to this condition. There are, however, morphological similarities (dilation and hypertrophy) between the athlete's and the failing heart, which emerge as physiological and pathophysiological adaptations, respectively. The introduction of new assessment techniques, specifically speckle tracking, may provide new insight into the properties that determine ventricular filling, specifically left ventricular twisting. The concept of ventricular function must be always considered, although it may not be always possible to distinguish the normal heart of sedentary individuals from that of highly trained hearts based solely on echocardiographic or basic studies.

The function of the self-attention network

This commentary links Humphrey and Sui’s proposed Self-attention Network (SAN) to the memory advantage associated with self-relevant information (i.e., the self-reference effect). Articulating this link elucidates the functional quality of the SAN in ensuring that information of potential importance to self is not lost. This adaptive system for self-processing mirrors the cognitive response to threat stimuli, which also elicit attentional biases and produce characteristically enhanced, episodic representations in memory. Understanding the link between the SAN and memory is key to comprehending more broadly the operation of the self in cognition.

The effects of ageing and exercise on skeletal muscle structure and function

Musculoskeletal ageing is associated with profound morphological and functional changes that increase fall risk and disease incidence and is characterised by age-related reductions in motor unit number and atrophy of muscle fibres, particularly type II fibres. Decrements in functional strength and power are relatively modest until the 6th decade, after which the rate of loss exponentially accelerates, particularly beyond the 8th decade of life. Physical activity is a therapeutic modality that can significantly attenuate age-related decline. The underlying signature of ageing, as manifested by perturbed redox homeostasis, leads to a blunting of acute and chronic redox regulated exercise adaptations. Impaired redox regulated exercise adaptations are mechanistically related to altered exercise-induced reactive oxygen and nitrogen species generation and a resultant failure to properly activate redox regulated signaling cascades. Despite the aforementioned specific impairment in redox signaling, exercise induces a plethora of beneficial effects, irrespective of age. There is, therefore, strong evidence for promoting regular physical exercise, especially progressive resistance training as a lifelong habitual practice.

Integration of relational and hierarchical network information for protein function prediction

BACKGROUND:In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.RESULTS:We propose a probabilistic framework to integrate information in relational data, in the form of a protein-protein interaction network, and a hierarchically structured database of terms, in the form of the GO database, for the purpose of protein function prediction. At the heart of our framework is a factorization of local neighborhood information in the protein-protein interaction network across successive ancestral terms in the GO hierarchy. We introduce a classifier within this framework, with computationally efficient implementation, that produces GO-term predictions that naturally obey a hierarchical 'true-path' consistency from root to leaves, without the need for further post-processing.CONCLUSION:A cross-validation study, using data from the yeast Saccharomyces cerevisiae, shows our method offers substantial improvements over both standard 'guilt-by-association' (i.e., Nearest-Neighbor) and more refined Markov random field methods, whether in their original form or when post-processed to artificially impose 'true-path' consistency. Further analysis of the results indicates that these improvements are associated with increased predictive capabilities (i.e., increased positive predictive value), and that this increase is consistent uniformly with GO-term depth. Additional in silico validation on a collection of new annotations recently added to GO confirms the advantages suggested by the cross-validation study. Taken as a whole, our results show that a hierarchical approach to network-based protein function prediction, that exploits the ontological structure of protein annotation databases in a principled manner, can offer substantial advantages over the successive application of 'flat' network-based methods.

Expression and function of the neurotrophic factors GDF5 and GDNF in the nigrostriatal system during development and in rat models of Parkinson's disease

Growth/differentiation factor 5 (GDF5) and glial cell line-derived neurotrophic factor (GDNF) are neurotrophic factors that promote the survival of midbrain dopaminergic neurons in vitro and in vivo. Both factors have potent neurotrophic and neuroprotective effects in rat models of Parkinson's disease (PD), and may represent promising new therapies for PD. The aim of the present study was to investigate the endogenous expression and function of GDF5 and GDNF in the nigrostriatal dopaminergic system during development and in rat models of PD. Examination of the temporal expression patterns of endogenous GDF5, GDNF, and their respective receptors, in the developing and adult nigrostriatal dopaminergic system suggest that these factors play important roles in promoting the survival and maturation of midbrain dopaminergic neurons during the period of postnatal programmed cell death. The relative levels of GDF5 and GDNF mRNAs in the midbrain and striatum, and their individual temporal expression patterns during development, suggest that their modes of actions are quite distinct in vivo. Furthermore, the sustained expression of GDF5, GDNF, and their receptors into adulthood suggest roles for these factors in the continued support and maintenance of mature nigrostriatal dopaminergic neurons. The present study found that endogenous GDF5, GDNF, and their receptors are differentially expressed in two 6-hydroxydopamine-induced lesion adult rat models of PD. In both terminal and axonal lesion models of PD, GDF5 mRNA levels in the striatum increased at 10 days post-lesion, while GDNF mRNA levels in the nigrostriatal system decreased at 10 and 28 days post-lesion. Thus, despite the fact that exogenous GDF5 and GDNF have similar effects on midbrain dopaminergic neurons in vitro and in vivo, their endogenous responses to a neurotoxic injury are quite distinct. These results highlight the importance of studying the temporal dynamic changes in neurotrophic factor expression during development and in animal models of PD.

Investigation of the function of the LNX family of E3 ubiquitin ligases in the nervous system

The Lnx1 (Ligand of Numb protein X 1) and Lnx2 genes belong to a family of PDZ domain-containing RING finger domain E3 ubiquitin ligases. mRNA expression for both genes have been reported in early murine central nervous system. However, there have been limited reports with regards to the expression patterns for both the proteins in vivo. Hence, we have attempted to characterize the significance of these proteins in the context of morphology and physiology of the central nervous system. Through our studies, we have attempted to examine closely the expression of these two genes in the murine central nervous system. We have also looked at novel interacting ligands for both proteins. Interacting partners have been examined with particular relevance to possible roles of their interactions with LNX1 and LNX2 in the functioning of the nervous system. Moreover, we have performed loss-of-function studies by way of creation and characterization of knockout mice.

Optimisation of immune effector function within the tumour microenvironment

Cancer represents a leading of cause of death in the developed world, inflicting tremendous suffering and plundering billions from health budgets. The traditional treatment approaches of surgery, radiotherapy and chemotherapy have achieved little in terms of cure for this deadly disease. Instead, life is prolonged for many, with dubious quality of life, only for disease to reappear with the inevitable fatal outcome. “Blue sky” thinking is required to tackle this disease and improve outcomes. The realisation and acceptance of the intrinsic role of the immune system in cancer pathogenesis, pathophysiology and treatment represented such a “blue sky” thought. Moreover, the embracement of immunotherapy, the concept of targeting immune cells rather than the tumour cells themselves, represents a paradigm shift in the approach to cancer therapy. The harnessing of immunotherapy demands radical and innovative therapeutic endeavours – endeavours such as gene and cell therapies and RNA interference, which two decades ago existed as mere concepts. This thesis straddles the frontiers of fundamental tumour immunobiology and novel therapeutic discovery, design and delivery. The work undertaken focused on two distinct immune cell populations known to undermine the immune response to cancer – suppressive T cells and macrophages. Novel RNAi mediators were designed, validated and incorporated into clinically relevant gene therapy vectors – involving a traditional lentiviral vector approach, and a novel bacterial vector strategy. Chapter 2 deals with the design of novel RNAi mediators against FOXP3 – a crucial regulator of the immunosuppressive regulatory T cell population. Two mediators were tested and validated. The superior mediator was taken forward as part of work in chapter 3. Chapter 3 deals with transposing the RNA sequence from chapter 2 into a DNA-based construct and subsequent incorporation into a lentiviral-based vector system. The lentiviral vector was shown to mediate gene delivery in vitro and functional RNAi was achieved against FOXP3. Proof of gene delivery was further confirmed in vivo in tumour-bearing animals. Chapter 4 focuses on a different immune cell population – tumour-associated macrophages. Non-invasive bacteria were explored as a specific means of delivering gene therapy to this phagocytic cell type. Proof of delivery was shown in vitro and in vivo. Moreover, in vivo delivery of a gene by this method achieved the desired immune response in terms of cytokine profile. Overall, the data presented here advance exploration within the field of cancer immunotherapy, introduce novel delivery and therapeutic strategies, and demonstrate pre-clinically the potential for such novel anti-cancer therapies.