Risk exposure trade-offs in the ontogeny of sexual segregation in Antarctic fur seal pups

Sexual segregation has important ecological implications, but its initial development in early life stages is poorly understood. We investigated the roles of size dimorphism, social behavior, and predation risk on the ontogeny of sexual segregation in Antarctic fur seal, Arctocephalus gazella, pups at South Georgia. Beaches and water provide opportunities for pup social interaction and learning (through play and swimming) but increased risk of injury and death (from other seals, predatory birds, and harsh weather), whereas tussock grass provides shelter from these risks but less developmental opportunities. One hundred pups were sexed and weighed, 50 on the beach and 50 in tussock grass, in January, February, and March annually from 1989 to 2018. Additionally, 19 male and 16 female pups were GPS-tracked during lactation from December 2012. Analysis of pup counts and habitat use of GPS-tracked pups suggested that females had a slightly higher association with tussock grass habitats and males with beach habitats. GPS-tracked pups traveled progressively further at sea as they developed, and males traveled further than females toward the end of lactation. These sex differences may reflect contrasting drivers of pup behavior: males being more risk prone to gain social skills and lean muscle mass and females being more risk averse to improve chances of survival, ultimately driven by their different reproductive roles. We conclude that sex differences in habitat use can develop in a highly polygynous species prior to the onset of major sexual size dimorphism, which hints that these sex differences will increasingly diverge in later life

Apraxia in progressive nonfluent aphasia

The clinical and neuroanatomical correlates of specific apraxias in neurodegenerative disease are not well understood. Here we addressed this issue in progressive nonfluent aphasia (PNFA), a canonical subtype of frontotemporal lobar degeneration that has been consistently associated with apraxia of speech (AOS) and in some cases orofacial apraxia, limb apraxia and/or parkinsonism. Sixteen patients with PNFA according to current consensus criteria were studied. Three patients had a corticobasal syndrome (CBS) and two a progressive supranuclear palsy (PSP) syndrome. Speech, orofacial and limb praxis functions were assessed using the Apraxia Battery for Adults-2 and a voxel-based morphometry (VBM) analysis was conducted on brain MRI scans from the patient cohort in order to identify neuroanatomical correlates. All patients had AOS based on reduced diadochokinetic rate, 69% of cases had an abnormal orofacial apraxia score and 44% of cases (including the three CBS cases and one case with PSP) had an abnormal limb apraxia score. Severity of orofacial apraxia (but not AOS or limb apraxia) correlated with estimated clinical disease duration. The VBM analysis identified distinct neuroanatomical bases for each form of apraxia: the severity of AOS correlated with left posterior inferior frontal lobe atrophy; orofacial apraxia with left middle frontal, premotor and supplementary motor cortical atrophy; and limb apraxia with left inferior parietal lobe atrophy. Our findings show that apraxia of various kinds can be a clinical issue in PNFA and demonstrate that specific apraxias are clinically and anatomically dissociable within this population of patients

Detection of Epileptogenic Cortical Malformations with Surface-Based MRI Morphometry

Magnetic resonance imaging has revolutionized the detection of structural abnormalities in patients with epilepsy. However, many focal abnormalities remain undetected in routine visual inspection. Here we use an automated, surface-based method for quantifying morphometric features related to epileptogenic cortical malformations to detect abnormal cortical thickness and blurred gray-white matter boundaries. Using MRI morphometry at 3T with surface-based spherical averaging techniques that precisely align anatomical structures between individual brains, we compared single patients with known lesions to a large normal control group to detect clusters of abnormal cortical thickness, gray-white matter contrast, local gyrification, sulcal depth, jacobian distance and curvature. To assess the effects of threshold and smoothing on detection sensitivity and specificity, we systematically varied these parameters with different thresholds and smoothing levels. To test the effectiveness of the technique to detect lesions of epileptogenic character, we compared the detected structural abnormalities to expert-tracings, intracranial EEG, pathology and surgical outcome in a homogeneous patient sample. With optimal parameters and by combining thickness and GWC, the surface-based detection method identified 92% of cortical lesions (sensitivity) with few false positives (96% specificity), successfully discriminating patients from controls 94% of the time. The detected structural abnormalities were related to the seizure onset zones, abnormal histology and positive outcome in all surgical patients. However, the method failed to adequately describe lesion extent in most cases. Automated surface-based MRI morphometry, if used with optimized parameters, may be a valuable additional clinical tool to improve the detection of subtle or previously occult malformations and therefore could improve identification of patients with intractable focal epilepsy who may benefit from surgery

A DIGE study on the effects of salbutamol on the rat muscle proteome - an exemplar of best practice for data sharing in proteomics

BACKGROUND: Proteomic techniques allow researchers to perform detailed analyses of cellular states and many studies are published each year, which highlight large numbers of proteins quantified in different samples. However, currently few data sets make it into public databases with sufficient metadata to allow other groups to verify findings, perform data mining or integrate different data sets. The Proteomics Standards Initiative has released a series of "Minimum Information About a Proteomics Experiment" guideline documents (MIAPE modules) and accompanying data exchange formats. This article focuses on proteomic studies based on gel electrophoresis and demonstrates how the corresponding MIAPE modules can be fulfilled and data deposited in public databases, using a new experimental data set as an example. FINDINGS: We have performed a study of the effects of an anabolic agent (salbutamol) at two different time points on the protein complement of rat skeletal muscle cells, quantified by difference gel electrophoresis. In the DIGE study, a total of 31 non-redundant proteins were identified as being potentially modulated at 24 h post treatment and 110 non redundant proteins at 96 h post-treatment. Several categories of function have been highlighted as strongly enriched, providing candidate proteins for further study. We also use the study as an example of best practice for data deposition. CONCLUSIONS: We have deposited all data sets from this study in public databases for further analysis by the community. We also describe more generally how gel-based protein identification data sets can now be deposited in the PRoteomics IDEntifications database (PRIDE), using a new software tool, the PRIDESpotMapper, which we developed to work in conjunction with the PRIDE Converter application. We also demonstrate how the ProteoRed MIAPE generator tool can be used to create and share a complete and compliant set of MIAPE reports for this experiment and others

Comprehensive analysis of correlation coefficients estimated from pooling heterogeneous microarray data

Background The synthesis of information across microarray studies has been performed by combining statistical results of individual studies (as in a mosaic), or by combining data from multiple studies into a large pool to be analyzed as a single data set (as in a melting pot of data). Specific issues relating to data heterogeneity across microarray studies, such as differences within and between labs or differences among experimental conditions, could lead to equivocal results in a melting pot approach. Results We applied statistical theory to determine the specific effect of different means and heteroskedasticity across 19 groups of microarray data on the sign and magnitude of gene-to-gene Pearson correlation coefficients obtained from the pool of 19 groups. We quantified the biases of the pooled coefficients and compared them to the biases of correlations estimated by an effect-size model. Mean differences across the 19 groups were the main factor determining the magnitude and sign of the pooled coefficients, which showed largest values of bias as they approached ±1. Only heteroskedasticity across the pool of 19 groups resulted in less efficient estimations of correlations than did a classical meta-analysis approach of combining correlation coefficients. These results were corroborated by simulation studies involving either mean differences or heteroskedasticity across a pool of N \u3e 2 groups. Conclusions The combination of statistical results is best suited for synthesizing the correlation between expression profiles of a gene pair across several microarray studies

Automating Genomic Data Mining via a Sequence-based Matrix Format and Associative Rule Set

There is an enormous amount of information encoded in each genome – enough to create living, responsive and adaptive organisms. Raw sequence data alone is not enough to understand function, mechanisms or interactions. Changes in a single base pair can lead to disease, such as sickle-cell anemia, while some large megabase deletions have no apparent phenotypic effect. Genomic features are varied in their data types and annotation of these features is spread across multiple databases. Herein, we develop a method to automate exploration of genomes by iteratively exploring sequence data for correlations and building upon them. First, to integrate and compare different annotation sources, a sequence matrix (SM) is developed to contain position-dependant information. Second, a classification tree is developed for matrix row types, specifying how each data type is to be treated with respect to other data types for analysis purposes. Third, correlative analyses are developed to analyze features of each matrix row in terms of the other rows, guided by the classification tree as to which analyses are appropriate. A prototype was developed and successful in detecting coinciding genomic features among genes, exons, repetitive elements and CpG islands

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

Systematic Detection of Epistatic Interactions Based on Allele Pair Frequencies

Epistatic genetic interactions are key for understanding the genetic contribution to complex traits. Epistasis is always defined with respect to some trait such as growth rate or fitness. Whereas most existing epistasis screens explicitly test for a trait, it is also possible to implicitly test for fitness traits by searching for the over- or under-representation of allele pairs in a given population. Such analysis of imbalanced allele pair frequencies of distant loci has not been exploited yet on a genome-wide scale, mostly due to statistical difficulties such as the multiple testing problem. We propose a new approach called Imbalanced Allele Pair frequencies (ImAP) for inferring epistatic interactions that is exclusively based on DNA sequence information. Our approach is based on genome-wide SNP data sampled from a population with known family structure. We make use of genotype information of parent-child trios and inspect 3×3 contingency tables for detecting pairs of alleles from different genomic positions that are over- or under-represented in the population. We also developed a simulation setup which mimics the pedigree structure by simultaneously assuming independence of the markers. When applied to mouse SNP data, our method detected 168 imbalanced allele pairs, which is substantially more than in simulations assuming no interactions. We could validate a significant number of the interactions with external data, and we found that interacting loci are enriched for genes involved in developmental processes

Annotation of the Drosophila melanogaster euchromatic genome: a systematic review

BACKGROUND: The recent completion of the Drosophila melanogaster genomic sequence to high quality and the availability of a greatly expanded set of Drosophila cDNA sequences, aligning to 78% of the predicted euchromatic genes, afforded FlyBase the opportunity to significantly improve genomic annotations. We made the annotation process more rigorous by inspecting each gene visually, utilizing a comprehensive set of curation rules, requiring traceable evidence for each gene model, and comparing each predicted peptide to SWISS-PROT and TrEMBL sequences. RESULTS: Although the number of predicted protein-coding genes in Drosophila remains essentially unchanged, the revised annotation significantly improves gene models, resulting in structural changes to 85% of the transcripts and 45% of the predicted proteins. We annotated transposable elements and non-protein-coding RNAs as new features, and extended the annotation of untranslated (UTR) sequences and alternative transcripts to include more than 70% and 20% of genes, respectively. Finally, cDNA sequence provided evidence for dicistronic transcripts, neighboring genes with overlapping UTRs on the same DNA sequence strand, alternatively spliced genes that encode distinct, non-overlapping peptides, and numerous nested genes. CONCLUSIONS: Identification of so many unusual gene models not only suggests that some mechanisms for gene regulation are more prevalent than previously believed, but also underscores the complex challenges of eukaryotic gene prediction. At present, experimental data and human curation remain essential to generate high-quality genome annotations

miR-23b regulates cytoskeletal remodeling, motility and metastasis by directly targeting multiple transcripts

Uncontrolled cell proliferation and cytoskeletal remodeling are responsible for tumor development and ultimately metastasis. A number of studies have implicated microRNAs in the regulation of cancer cell invasion and migration. Here, we show that miR-23b regulates focal adhesion, cell spreading, cell-cell junctions and the formation of lamellipodia in breast cancer (BC), implicating a central role for it in cytoskeletal dynamics. Inhibition of miR-23b, using a specific sponge construct, leads to an increase of cell migration and metastatic spread in vivo, indicating it as a metastatic suppressor microRNA. Clinically, low miR-23b expression correlates with the development of metastases in BC patients. Mechanistically, miR-23b is able to directly inhibit a number of genes implicated in cytoskeletal remodeling in BC cells. Through intracellular signal transduction, growth factors activate the transcription factor AP-1, and we show that this in turn reduces miR-23b levels by direct binding to its promoter, releasing the pro-invasive genes from translational inhibition. In aggregate, miR-23b expression invokes a sophisticated interaction network that co-ordinates a wide range of cellular responses required to alter the cytoskeleton during cancer cell motility