Isolation and Comparative Transcriptome Analysis of Human Fetal and iPSC-Derived Cone Photoreceptor Cells.

Loss of cone photoreceptors, crucial for daylight vision, has the greatest impact on sight in retinal degeneration. Transplantation of stem cell-derived L/M-opsin cones, which form 90% of the human cone population, could provide a feasible therapy to restore vision. However, transcriptomic similarities between fetal and stem cell-derived cones remain to be defined, in addition to development of cone cell purification strategies. Here, we report an analysis of the human L/M-opsin cone photoreceptor transcriptome using an AAV2/9.pR2.1:GFP reporter. This led to the identification of a cone-enriched gene signature, which we used to demonstrate similar gene expression between fetal and stem cell-derived cones. We then defined a cluster of differentiation marker combination that, when used for cell sorting, significantly enriches for cone photoreceptors from the fetal retina and stem cell-derived retinal organoids, respectively. These data may facilitate more efficient isolation of human stem cell-derived cones for use in clinical transplantation studies.

Analysis of temporal transcription expression profiles reveal links between protein function and developmental stages of Drosophila melanogaster.

Accurate gene or protein function prediction is a key challenge in the post-genome era. Most current methods perform well on molecular function prediction, but struggle to provide useful annotations relating to biological process functions due to the limited power of sequence-based features in that functional domain. In this work, we systematically evaluate the predictive power of temporal transcription expression profiles for protein function prediction in Drosophila melanogaster. Our results show significantly better performance on predicting protein function when transcription expression profile-based features are integrated with sequence-derived features, compared with the sequence-derived features alone. We also observe that the combination of expression-based and sequence-based features leads to further improvement of accuracy on predicting all three domains of gene function. Based on the optimal feature combinations, we then propose a novel multi-classifier-based function prediction method for Drosophila melanogaster proteins, FFPred-fly+. Interpreting our machine learning models also allows us to identify some of the underlying links between biological processes and developmental stages of Drosophila melanogaster.

An expanded evaluation of protein function prediction methods shows an improvement in accuracy.

BACKGROUND: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. RESULTS: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. CONCLUSIONS: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.

FFPred 3: feature-based function prediction for all Gene Ontology domains.

Predicting protein function has been a major goal of bioinformatics for several decades, and it has gained fresh momentum thanks to recent community-wide blind tests aimed at benchmarking available tools on a genomic scale. Sequence-based predictors, especially those performing homology-based transfers, remain the most popular but increasing understanding of their limitations has stimulated the development of complementary approaches, which mostly exploit machine learning. Here we present FFPred 3, which is intended for assigning Gene Ontology terms to human protein chains, when homology with characterized proteins can provide little aid. Predictions are made by scanning the input sequences against an array of Support Vector Machines (SVMs), each examining the relationship between protein function and biophysical attributes describing secondary structure, transmembrane helices, intrinsically disordered regions, signal peptides and other motifs. This update features a larger SVM library that extends its coverage to the cellular component sub-ontology for the first time, prompted by the establishment of a dedicated evaluation category within the Critical Assessment of Functional Annotation. The effectiveness of this approach is demonstrated through benchmarking experiments, and its usefulness is illustrated by analysing the potential functional consequences of alternative splicing in human and their relationship to patterns of biological features.

Genome3D: exploiting structure to help users understand their sequences.

Genome3D (http://www.genome3d.eu) is a collaborative resource that provides predicted domain annotations and structural models for key sequences. Since introducing Genome3D in a previous NAR paper, we have substantially extended and improved the resource. We have annotated representatives from Pfam families to improve coverage of diverse sequences and added a fast sequence search to the website to allow users to find Genome3D-annotated sequences similar to their own. We have improved and extended the Genome3D data, enlarging the source data set from three model organisms to 10, and adding VIVACE, a resource new to Genome3D. We have analysed and updated Genome3D's SCOP/CATH mapping. Finally, we have improved the superposition tools, which now give users a more powerful interface for investigating similarities and differences between structural models.

Scalable web services for the PSIPRED Protein Analysis Workbench.

Here, we present the new UCL Bioinformatics Group's PSIPRED Protein Analysis Workbench. The Workbench unites all of our previously available analysis methods into a single web-based framework. The new web portal provides a greatly streamlined user interface with a number of new features to allow users to better explore their results. We offer a number of additional services to enable computationally scalable execution of our prediction methods; these include SOAP and XML-RPC web server access and new HADOOP packages. All software and services are available via the UCL Bioinformatics Group website at http://bioinf.cs.ucl.ac.uk/.

FFPred 2.0: improved homology-independent prediction of gene ontology terms for eukaryotic protein sequences.

To understand fully cell behaviour, biologists are making progress towards cataloguing the functional elements in the human genome and characterising their roles across a variety of tissues and conditions. Yet, functional information - either experimentally validated or computationally inferred by similarity - remains completely missing for approximately 30% of human proteins. FFPred was initially developed to bridge this gap by targeting sequences with distant or no homologues of known function and by exploiting clear patterns of intrinsic disorder associated with particular molecular activities and biological processes. Here, we present an updated and improved version, which builds on larger datasets of protein sequences and annotations, and uses updated component feature predictors as well as revised training procedures. FFPred 2.0 includes support vector regression models for the prediction of 442 Gene Ontology (GO) terms, which largely expand the coverage of the ontology and of the biological process category in particular. The GO term list mainly revolves around macromolecular interactions and their role in regulatory, signalling, developmental and metabolic processes. Benchmarking experiments on newly annotated proteins show that FFPred 2.0 provides more accurate functional assignments than its predecessor and the ProtFun server do; also, its assignments can complement information obtained using BLAST-based transfer of annotations, improving especially prediction in the biological process category. Furthermore, FFPred 2.0 can be used to annotate proteins belonging to several eukaryotic organisms with a limited decrease in prediction quality. We illustrate all these points through the use of both precision-recall plots and of the COGIC scores, which we recently proposed as an alternative numerical evaluation measure of function prediction accuracy.

Genome3D: a UK collaborative project to annotate genomic sequences with predicted 3D structures based on SCOP and CATH domains.

Genome3D, available at http://www.genome3d.eu, is a new collaborative project that integrates UK-based structural resources to provide a unique perspective on sequence-structure-function relationships. Leading structure prediction resources (DomSerf, FUGUE, Gene3D, pDomTHREADER, Phyre and SUPERFAMILY) provide annotations for UniProt sequences to indicate the locations of structural domains (structural annotations) and their 3D structures (structural models). Structural annotations and 3D model predictions are currently available for three model genomes (Homo sapiens, E. coli and baker's yeast), and the project will extend to other genomes in the near future. As these resources exploit different strategies for predicting structures, the main aim of Genome3D is to enable comparisons between all the resources so that biologists can see where predictions agree and are therefore more trusted. Furthermore, as these methods differ in whether they build their predictions using CATH or SCOP, Genome3D also contains the first official mapping between these two databases. This has identified pairs of similar superfamilies from the two resources at various degrees of consensus (532 bronze pairs, 527 silver pairs and 370 gold pairs).

Estimation of the time course of neurotransmitter release at central synapses from the first latency of postsynaptic currents.

Measurement of the release time course (RTC) and of the quantal content is important for quantifying synaptic precision and understanding the molecular basis of the release process at central synapses. In theory, the RTC can be determined directly from the histogram of first latencies of quantal events only if a maximum of one vesicle is released per trial, but at most synapses multiple vesicles are released. Traditionally, first latency histograms have been corrected for multiple releases using a simple correction, derived by Barrett and Stevens (BS; 1972b) for quantifying release at the neuromuscular junction. This correction has also been used to quantify release at central synapses. We show, by combining an analytical approach and numerical simulations of stochastic quantal release, that the BS correction gives a biased estimate for RTC and quantal content. The bias increases with release probability, and is therefore particularly problematic for central synapses. We show that this is due to assuming infinite availability of releasable vesicles and we derive a formula for estimating the RTC from first latencies without this assumption. The resulting 'binomial correction' requires knowledge of the maximum number of quanta that can be released following an action potential (N), which can be estimated with variance-mean analysis. We show with simulations that estimating RTC and quantal content from first latencies using the binomial correction is robust in the presence of noise and when release probability is non-uniform. We also provide an alternative method for estimating RTC from the first latencies when N cannot be determined.

Signaling properties of stratum oriens interneurons in the hippocampus of transgenic mice expressing EGFP in a subset of somatostatin-containing cells.

GABAergic interneurons constitute a heterogeneous group of cells that exert a powerful control on network excitability and are responsible for the oscillatory behavior crucial for information processing in the brain. These cells have been differently classified according to their morphological, neurochemical, and physiological characteristics. Here, whole cell patch clamp recordings were used to further characterize, in transgenic mice expressing EGFP in a subpopulation of GABAergic interneurons containing somatostatin (GIN mice), the functional properties of EGFP-positive cells in stratum oriens of the CA1 region of the hippocampus, in slice cultures obtained from P8 old animals. These cells showed passive and active membrane properties similar to those found in stratum oriens interneurons projecting to stratum lacunosum-moleculare. Moreover, they exhibited different firing patterns that were maintained upon membrane depolarization: irregular (48%), regular (30%), and clustered (22%). Trains of action potentials in interneurons evoked in a minority of principal cells (3/45) small amplitude GABAergic currents that at 20 Hz underwent short-term depression. In contrast, excitatory connections between principal cells and EGFP-positive interneurons were highly reliable (17/55) and exhibited a frequency and use-dependent facilitation particularly in the gamma band. In addition, recordings from paired of interconnected EGFP-positive cells revealed in 47% of the cases electrical coupling, which was abolished by carbenoxolone (200 microM). On average, the coupling coefficient was 0.21 +/- 0.07. When electrical coupling was particularly strong it acted as a powerful low-pass filter, thus contributing to alter the output of individual cells. In conclusion, it appears that the dynamic interaction between cells with various firing patterns could differently affect GABAergic signaling, leading, as suggested by simulation data, to a wide range of interneuronal communication within the hippocampal network.

Persistent decrease in synaptic efficacy induced by nicotine at Schaffer collateral-CA1 synapses in the immature rat hippocampus.

Neuronal nicotinic acetylcholine receptors (nAChRs) are widely distributed within the brain where they contribute to the regulation of higher cognitive functions. The loss of the cholinergic function in Alzheimer's disease patients, along with the well-known memory enhancing effect of nicotine, emphasizes the role of cholinergic signalling in memory functions. The hippocampus, a key structure in learning and memory, is endowed with nAChRs localized at pre- and postsynaptic levels. In previous work on the immature hippocampus we have shown that, at low probability (P) synapses, activation of alpha7 nAChRs by nicotine or by endogenously released acetylcholine persistently enhanced glutamate release and converted 'presynaptically silent' synapses into functional ones. Here we show that in the same preparation, at high P synapses, nicotine induces long-term depression of AMPA- and NMDA-mediated synaptic currents. This effect was mediated by presynaptic alpha7- and beta2-containing receptors and was associated with an increase in the paired pulse ratio and in the coefficient of variation. High P synapses could be converted into low P and vice versa by changing the extracellular Ca2+/Mg2+ ratio. In these conditions nicotine was able to persistently potentiate or depress synaptic responses depending on the initial P-values. A bi-directional control of synaptic plasticity by nicotine would considerably enhance the computational properties of the network during a critical period of postnatal development thus contributing to sculpt the neuronal circuit.