Broad functional profiling of fission yeast proteins using phenomics and machine learning.

Many proteins remain poorly characterized even in well-studied organisms, presenting a bottleneck for research. We applied phenomics and machine-learning approaches with Schizosaccharomyces pombe for broad cues on protein functions. We assayed colony-growth phenotypes to measure the fitness of deletion mutants for 3509 non-essential genes in 131 conditions with different nutrients, drugs, and stresses. These analyses exposed phenotypes for 3492 mutants, including 124 mutants of 'priority unstudied' proteins conserved in humans, providing varied functional clues. For example, over 900 proteins were newly implicated in the resistance to oxidative stress. Phenotype-correlation networks suggested roles for poorly characterized proteins through 'guilt by association' with known proteins. For complementary functional insights, we predicted Gene Ontology (GO) terms using machine learning methods exploiting protein-network and protein-homology data (NET-FF). We obtained 56,594 high-scoring GO predictions, of which 22,060 also featured high information content. Our phenotype-correlation data and NET-FF predictions showed a strong concordance with existing PomBase GO annotations and protein networks, with integrated analyses revealing 1675 novel GO predictions for 783 genes, including 47 predictions for 23 priority unstudied proteins. Experimental validation identified new proteins involved in cellular aging, showing that these predictions and phenomics data provide a rich resource to uncover new protein functions.

Zebrafish as a model for cardiac disease; Cryo-EM structure of native cardiac thin filaments from Danio Rerio.

Actin, tropomyosin and troponin, the proteins that comprise the contractile apparatus of the cardiac thin filament, are highly conserved across species. We have used cryo-EM to study the three-dimensional structure of the zebrafish cardiac thin and actin filaments. With 70% of human genes having an obvious zebrafish orthologue, and conservation of 85% of disease-causing genes, zebrafish are a good animal model for the study of human disease. Our structure of the zebrafish thin filament reveals the molecular interactions between the constituent proteins, showing that the fundamental organisation of the complex is the same as that reported in the human reconstituted thin filament. A reconstruction of zebrafish cardiac F-actin demonstrates no deviations from human cardiac actin over an extended length of 14 actin subunits. Modelling zebrafish homology models into our maps enabled us to compare, in detail, the similarity with human models. The structural similarities of troponin-T in particular, a region known to contain a hypertrophic cardiomyopathy 'hotspot', confirm the suitability of zebrafish to study these disease-causing mutations.

CATH: increased structural coverage of functional space.

CATH (https://www.cathdb.info) identifies domains in protein structures from wwPDB and classifies these into evolutionary superfamilies, thereby providing structural and functional annotations. There are two levels: CATH-B, a daily snapshot of the latest domain structures and superfamily assignments, and CATH+, with additional derived data, such as predicted sequence domains, and functionally coherent sequence subsets (Functional Families or FunFams). The latest CATH+ release, version 4.3, significantly increases coverage of structural and sequence data, with an addition of 65,351 fully-classified domains structures (+15%), providing 500 238 structural domains, and 151 million predicted sequence domains (+59%) assigned to 5481 superfamilies. The FunFam generation pipeline has been re-engineered to cope with the increased influx of data. Three times more sequences are captured in FunFams, with a concomitant increase in functional purity, information content and structural coverage. FunFam expansion increases the structural annotations provided for experimental GO terms (+59%). We also present CATH-FunVar web-pages displaying variations in protein sequences and their proximity to known or predicted functional sites. We present two case studies (1) putative cancer drivers and (2) SARS-CoV-2 proteins. Finally, we have improved links to and from CATH including SCOP, InterPro, Aquaria and 2DProt.

Comprehensive analysis of long noncoding RNA expression in dorsal root ganglion reveals cell-type specificity and dysregulation after nerve injury.

Dorsal root ganglion (DRG) neurons provide connectivity between peripheral tissues and the spinal cord. Transcriptional plasticity within DRG sensory neurons after peripheral nerve injury contributes to nerve repair but also leads to maladaptive plasticity, including the development of neuropathic pain. This study presents tissue and neuron-specific expression profiling of both known and novel long noncoding RNAs (LncRNAs) in the rodent DRG after nerve injury. We have identified a large number of novel LncRNAs expressed within the rodent DRG, a minority of which were syntenically conserved between the mouse, rat, and human, and including, both intergenic and antisense LncRNAs. We have also identified neuron type-specific LncRNAs in the mouse DRG and LncRNAs that are expressed in human IPS cell-derived sensory neurons. We show significant plasticity in LncRNA expression after nerve injury, which in mice is strain and gender dependent. This resource is publicly available and will aid future studies of DRG neuron identity and the transcriptional landscape in both the naive and injured DRG. We present our work regarding novel antisense and intergenic LncRNAs as an online searchable database, accessible from PainNetworks (http://www.painnetworks.org/). We have also integrated all annotated gene expression data in PainNetworks, so they can be examined in the context of their protein interactions.

Structural and Functional View of Polypharmacology.

Protein domains mediate drug-protein interactions and this principle can guide the design of multi-target drugs i.e. polypharmacology. In this study, we associate multi-target drugs with CATH functional families through the overrepresentation of targets of those drugs in CATH functional families. Thus, we identify CATH functional families that are currently enriched in drugs (druggable CATH functional families) and we use the network properties of these druggable protein families to analyse their association with drug side effects. Analysis of selected druggable CATH functional families, enriched in drug targets, show that relatives exhibit highly conserved drug binding sites. Furthermore, relatives within druggable CATH functional families occupy central positions in a human protein functional network, cluster together forming network neighbourhoods and are less likely to be within proteins associated with drug side effects. Our results demonstrate that CATH functional families can be used to identify drug-target interactions, opening a new research direction in target identification.

Gene Function Prediction from Functional Association Networks Using Kernel Partial Least Squares Regression.

With the growing availability of large-scale biological datasets, automated methods of extracting functionally meaningful information from this data are becoming increasingly important. Data relating to functional association between genes or proteins, such as co-expression or functional association, is often represented in terms of gene or protein networks. Several methods of predicting gene function from these networks have been proposed. However, evaluating the relative performance of these algorithms may not be trivial: concerns have been raised over biases in different benchmarking methods and datasets, particularly relating to non-independence of functional association data and test data. In this paper we propose a new network-based gene function prediction algorithm using a commute-time kernel and partial least squares regression (Compass). We compare Compass to GeneMANIA, a leading network-based prediction algorithm, using a number of different benchmarks, and find that Compass outperforms GeneMANIA on these benchmarks. We also explicitly explore problems associated with the non-independence of functional association data and test data. We find that a benchmark based on the Gene Ontology database, which, directly or indirectly, incorporates information from other databases, may considerably overestimate the performance of algorithms exploiting functional association data for prediction.

Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation.

The advent of genome-wide RNA interference (RNAi)-based screens puts us in the position to identify genes for all functions human cells carry out. However, for many functions, assay complexity and cost make genome-scale knockdown experiments impossible. Methods to predict genes required for cell functions are therefore needed to focus RNAi screens from the whole genome on the most likely candidates. Although different bioinformatics tools for gene function prediction exist, they lack experimental validation and are therefore rarely used by experimentalists. To address this, we developed an effective computational gene selection strategy that represents public data about genes as graphs and then analyzes these graphs using kernels on graph nodes to predict functional relationships. To demonstrate its performance, we predicted human genes required for a poorly understood cellular function-mitotic chromosome condensation-and experimentally validated the top 100 candidates with a focused RNAi screen by automated microscopy. Quantitative analysis of the images demonstrated that the candidates were indeed strongly enriched in condensation genes, including the discovery of several new factors. By combining bioinformatics prediction with experimental validation, our study shows that kernels on graph nodes are powerful tools to integrate public biological data and predict genes involved in cellular functions of interest.

Transient protein-protein interactions: structural, functional, and network properties.

Transient interactions, which involve protein interactions that are formed and broken easily, are important in many aspects of cellular function. Here we describe structural and functional properties of transient interactions between globular domains and between globular domains, short peptides, and disordered regions. The importance of posttranslational modifications in transient interactions is also considered. We review techniques used in the detection of the different types of transient protein-protein interactions. We also look at the role of transient interactions within protein-protein interaction networks and consider their contribution to different aspects of these networks.

Finding the "dark matter" in human and yeast protein network prediction and modelling.

Accurate modelling of biological systems requires a deeper and more complete knowledge about the molecular components and their functional associations than we currently have. Traditionally, new knowledge on protein associations generated by experiments has played a central role in systems modelling, in contrast to generally less trusted bio-computational predictions. However, we will not achieve realistic modelling of complex molecular systems if the current experimental designs lead to biased screenings of real protein networks and leave large, functionally important areas poorly characterised. To assess the likelihood of this, we have built comprehensive network models of the yeast and human proteomes by using a meta-statistical integration of diverse computationally predicted protein association datasets. We have compared these predicted networks against combined experimental datasets from seven biological resources at different level of statistical significance. These eukaryotic predicted networks resemble all the topological and noise features of the experimentally inferred networks in both species, and we also show that this observation is not due to random behaviour. In addition, the topology of the predicted networks contains information on true protein associations, beyond the constitutive first order binary predictions. We also observe that most of the reliable predicted protein associations are experimentally uncharacterised in our models, constituting the hidden or "dark matter" of networks by analogy to astronomical systems. Some of this dark matter shows enrichment of particular functions and contains key functional elements of protein networks, such as hubs associated with important functional areas like the regulation of Ras protein signal transduction in human cells. Thus, characterising this large and functionally important dark matter, elusive to established experimental designs, may be crucial for modelling biological systems. In any case, these predictions provide a valuable guide to these experimentally elusive regions.

Assessment of protein domain fusions in human protein interaction networks prediction: application to the human kinetochore model.

In order to understand how biological systems function it is necessary to determine the interactions and associations between proteins. Some proteins, involved in a common biological process and encoded by separate genes in one organism, can be found fused within a single protein chain in other organisms. By detecting these triplets, a functional relationship can be established between the unfused proteins. Here we use a domain fusion prediction method to predict these protein interactions for the human interactome. We observed that gene fusion events are more related to physical interaction between proteins than to other weaker functional relationships such as participation in a common biological pathway. These results suggest that domain fusion is an appropriate method for predicting protein complexes. The most reliable fused domain predictions were used to build protein-protein interaction (PPI) networks. These predicted PPI network models showed the same topological features as real biological networks and different features from random behaviour. We built the PPI domain fusion sub-network model of the human kinetochore and observed that the majority of the predicted interactions have not yet been experimentally characterised in the publicly available PPI repositories. The study of the human kinetochore domain fusion sub-network reveals undiscovered kinetochore proteins with presumably relevant functions, such as hubs with many connections in the kinetochore sub-network. These results suggest that experimentally hidden regions in the predicted PPI networks contain key functional elements, associated with important functional areas, still undiscovered in the human interactome. Until novel experiments shed light on these hidden regions; domain fusion predictions provide a valuable approach for exploring them.

The ranging behaviour and habitat use of rabbits on farmland and their potential role in the epidemiology of paratuberculosis.

Grazing herbivores avoid grass swards contaminated with faeces as the ingestion of faeces is a common route of micro- and macro-parasite transmission. The recent novel finding that herbivores do not avoid grass swards contaminated with rabbit faeces suggests that disease risk posed to herbivores by rabbits is determined by rabbit ranging and excretory behaviour. Using as a case study rabbits and the risk Mycobacterium avium sub-species paratuberculosis (M. a. paratuberculosis) poses to cattle, the interaction between rabbits and grazing pasture was studied on an infected farm in the east of Scotland in spring and autumn 2000. Radio telemetry, burrow surveys and faecal pellet count data were collected on two areas (Areas 1 and 2) of the farm with different habitat mosaics, to study the potential effects of season and habitat on the spatial distribution of rabbits faeces and thus disease in the environment. Twenty one rabbits were radio tracked and a total of 902 fixes collected. Mean home range sizes (100% minimum convex polygons) were between 2.0 and 7.1 ha per rabbit per season. Home ranges were significantly larger in spring, and in Area 1 which had more moor and woodland and less rough pasture. Rabbits used rough pasture most in Area 1 and gorse scrub in Area 2. In both areas, significantly more burrows were located in gorse scrub than in any other habitat. Most faecal pellets were deposited on the moorland habitat of Area 2 in autumn. In habitats to which grazing livestock had access, the mean rate of faecal deposition was 8571 pellets per ha per day. The greatest risk of disease transmission occurred in habitats of poor grazing quality (e.g., gorse scrub) which were used by rabbits for burrowing and thus contained high concentrations of faeces. The findings of the study are discussed in relation to management practices aimed at reducing disease risk to livestock, including the fencing of scrub and the reduction of rabbit population size to prevent expansion of rabbit burrows from scrub into grazing pastures.