Acute exposure to high temperature affects expression of heat shock proteins in altricial avian embryos.

As the world warms, understanding the fundamental mechanisms available to organisms to protect themselves from thermal stress is becoming ever more important. Heat shock proteins are highly conserved molecular chaperones which serve to maintain cellular processes during stress, including thermal extremes. Developing animals may be particularly vulnerable to elevated temperatures, but the relevance of heat shock proteins for developing altricial birds exposed to a thermal stressor has never been investigated. Here, we sought to test whether three stress-induced genes - HSPD1, HSPA2, HSP90AA1 - and two constitutively expressed genes - HSPA8, HSP90B1 - are upregulated in response to acute thermal shock in zebra finch (Taeniopygia guttata) embryos half-way through incubation. Tested on a gradient from 37.5 °C (control) to 45 °C, we found that all genes, except HSPD1, were upregulated. However, not all genes initiated upregulation at the same temperature. For all genes, the best fitting model included a correlate of developmental stage that, although it was never significant after multiple-test correction, hints that heat shock protein upregulation might increase through embryonic development. Together, these results show that altricial avian embryos are capable of upregulating a known protective mechanism against thermal stress, and suggest that these highly conserved cellular mechanisms may be a vital component of early developmental protection under climate change.

Understanding sequencing data as compositions: an outlook and review.

Motivation: Although seldom acknowledged explicitly, count data generated by sequencing platforms exist as compositions for which the abundance of each component (e.g. gene or transcript) is only coherently interpretable relative to other components within that sample. This property arises from the assay technology itself, whereby the number of counts recorded for each sample is constrained by an arbitrary total sum (i.e. library size). Consequently, sequencing data, as compositional data, exist in a non-Euclidean space that, without normalization or transformation, renders invalid many conventional analyses, including distance measures, correlation coefficients and multivariate statistical models. Results: The purpose of this review is to summarize the principles of compositional data analysis (CoDA), provide evidence for why sequencing data are compositional, discuss compositionally valid methods available for analyzing sequencing data, and highlight future directions with regard to this field of study. Supplementary information: Supplementary data are available at Bioinformatics online.

Solving for X: Evidence for sex-specific autism biomarkers across multiple transcriptomic studies.

Autism spectrum disorder (ASD) is a markedly heterogeneous condition with a varied phenotypic presentation. Its high concordance among siblings, as well as its clear association with specific genetic disorders, both point to a strong genetic etiology. However, the molecular basis of ASD is still poorly understood, although recent studies point to the existence of sex-specific ASD pathophysiologies and biomarkers. Despite this, little is known about how exactly sex influences the gene expression signatures of ASD probands. In an effort to identify sex-dependent biomarkers and characterize their function, we present an analysis of a single paired-end postmortem brain RNA-Seq data set and a meta-analysis of six blood-based microarray data sets. Here, we identify several genes with sex-dependent dysregulation, and many more with sex-independent dysregulation. Moreover, through pathway analysis, we find that these sex-independent biomarkers have substantially different biological roles than the sex-dependent biomarkers, and that some of these pathways are ubiquitously dysregulated in both postmortem brain and blood. We conclude by synthesizing the discovered biomarker profiles with the extant literature, by highlighting the advantage of studying sex-specific dysregulation directly, and by making a call for new transcriptomic data that comprise large female cohorts.

Benchmarking differential expression analysis tools for RNA-Seq: normalization-based vs. log-ratio transformation-based methods.

BACKGROUND: Count data generated by next-generation sequencing assays do not measure absolute transcript abundances. Instead, the data are constrained to an arbitrary "library size" by the sequencing depth of the assay, and typically must be normalized prior to statistical analysis. The constrained nature of these data means one could alternatively use a log-ratio transformation in lieu of normalization, as often done when testing for differential abundance (DA) of operational taxonomic units (OTUs) in 16S rRNA data. Therefore, we benchmark how well the ALDEx2 package, a transformation-based DA tool, detects differential expression in high-throughput RNA-sequencing data (RNA-Seq), compared to conventional RNA-Seq methods such as edgeR and DESeq2. RESULTS: To evaluate the performance of log-ratio transformation-based tools, we apply the ALDEx2 package to two simulated, and two real, RNA-Seq data sets. One of the latter was previously used to benchmark dozens of conventional RNA-Seq differential expression methods, enabling us to directly compare transformation-based approaches. We show that ALDEx2, widely used in meta-genomics research, identifies differentially expressed genes (and transcripts) from RNA-Seq data with high precision and, given sufficient sample sizes, high recall too (regardless of the alignment and quantification procedure used). Although we show that the choice in log-ratio transformation can affect performance, ALDEx2 has high precision (i.e., few false positives) across all transformations. Finally, we present a novel, iterative log-ratio transformation (now implemented in ALDEx2) that further improves performance in simulations. CONCLUSIONS: Our results suggest that log-ratio transformation-based methods can work to measure differential expression from RNA-Seq data, provided that certain assumptions are met. Moreover, these methods have very high precision (i.e., few false positives) in simulations and perform well on real data too. With previously demonstrated applicability to 16S rRNA data, ALDEx2 can thus serve as a single tool for data from multiple sequencing modalities.

Mapping genotype-phenotype associations of nsSNPs in coiled-coil oligomerization domains of the human proteome.

We assessed the impact of disease mutations (DMs) versus polymorphisms (PYs) in coiled-coil (CC) domains in UniProt by modeling the structural and functional impact of variants in silico with the CC prediction program Multicoil. The structural impact of variants was evaluated with respect to three main metrics: the oligomerization score-to determine whether the variant is stabilizing or destabilizing-the oligomerization state, and the register-specific score. The functional impact was queried indirectly in several ways. First, we examined marginally stable CCs that were either stabilized or destabilized by the variant. Second, we looked for variants that altered the register of the wild-type CC near wild-type irregularities of likely functional importance, such as skips and stammers. Third, we searched for variants that altered the oligomerization state of the CC. DMs tended to be more destabilizing than PYs; but interestingly, PYs were more frequently associated with predicted changes in the oligomerization state. The functional impact was also queried by testing the association of CC variants with multiple phenotypes, that is, pleiotropy. Mutations in CC regions of proteins cause 155 different phenotypes and are more frequently associated with pleiotropy than proteins in general. Importantly, the CC region itself often encodes the pleiotropy.

Selection on Mitochondrial Variants Occurs between and within Individuals in an Expanding Invasion.

Mitochondria are critical for life, yet their underlying evolutionary biology is poorly understood. In particular, little is known about interaction between two levels of evolution: between individuals and within individuals (competition between cells, mitochondria or mitochondrial DNA molecules). Rapid evolution is suspected to occur frequently in mitochondrial DNA, whose maternal inheritance predisposes advantageous mutations to sweep rapidly though populations. Rapid evolution is also predicted in response to changed selection regimes after species invasion or removal of pathogens or competitors. Here, using empirical and simulated data from a model invasive bird species, we provide the first demonstration of rapid selection on the mitochondrial genome within individuals in the wild. Further, we show differences in mitochondrial DNA copy number associated with competing genetic variants, which may provide a mechanism for selection. We provide evidence for three rarely documented phenomena: selection associated with mitochondrial DNA abundance, selection on the mitochondrial control region, and contemporary selection during invasion.

Finding biomarkers of experience in animals.

At a time when there is a growing public interest in animal welfare, it is critical to have objective means to assess the way that an animal experiences a situation. Objectivity is critical to ensure appropriate animal welfare outcomes. Existing behavioural, physiological, and neurobiological indicators that are used to assess animal welfare can verify the absence of extremely negative outcomes. But welfare is more than an absence of negative outcomes and an appropriate indicator should reflect the full spectrum of experience of an animal, from negative to positive. In this review, we draw from the knowledge of human biomedical science to propose a list of candidate biological markers (biomarkers) that should reflect the experiential state of non-human animals. The proposed biomarkers can be classified on their main function as endocrine, oxidative stress, non-coding molecular, and thermobiological markers. We also discuss practical challenges that must be addressed before any of these biomarkers can become useful to assess the experience of an animal in real-life.

Transcriptome analysis of pigeon milk production - role of cornification and triglyceride synthesis genes.

BACKGROUND: The pigeon crop is specially adapted to produce milk that is fed to newly hatched young. The process of pigeon milk production begins when the germinal cell layer of the crop rapidly proliferates in response to prolactin, which results in a mass of epithelial cells that are sloughed from the crop and regurgitated to the young. We proposed that the evolution of pigeon milk built upon the ability of avian keratinocytes to accumulate intracellular neutral lipids during the cornification of the epidermis. However, this cornification process in the pigeon crop has not been characterised. RESULTS: We identified the epidermal differentiation complex in the draft pigeon genome scaffold and found that, like the chicken, it contained beta-keratin genes. These beta-keratin genes can be classified, based on sequence similarity, into several clusters including feather, scale and claw keratins. The cornified cells of the pigeon crop express several cornification-associated genes including cornulin, S100-A9 and A16-like, transglutaminase 6-like and the pigeon 'lactating' crop-specific annexin cp35. Beta-keratins play an important role in 'lactating' crop, with several claw and scale keratins up-regulated. Additionally, transglutaminase 5 and differential splice variants of transglutaminase 4 are up-regulated along with S100-A10. CONCLUSIONS: This study of global gene expression in the crop has expanded our knowledge of pigeon milk production, in particular, the mechanism of cornification and lipid production. It is a highly specialised process that utilises the normal keratinocyte cellular processes to produce a targeted nutrient solution for the young at a very high turnover.

Metergoline Shares Properties with Atypical Antipsychotic Drugs Identified by Gene Expression Signature Screen.

Novel approaches are required to find new treatments for schizophrenia and other neuropsychiatric disorders. This study utilised a combination of in vitro transcriptomics and in silico analysis with the BROAD Institute's Connectivity Map to identify drugs that can be repurposed to treat psychiatric disorders. Human neuronal (NT2-N) cells were treated with a combination of atypical antipsychotic drugs commonly used to treat psychiatric disorders (such as schizophrenia, bipolar disorder, and major depressive disorder), and differential gene expression was analysed. Biological pathways with an increased gene expression included circadian rhythm and vascular endothelial growth factor signalling, while the adherens junction and cell cycle pathways were transcriptionally downregulated. The Connectivity Map (CMap) analysis screen highlighted drugs that affect global gene expression in a similar manner to these psychiatric disorder treatments, including several other antipsychotic drugs, confirming the utility of this approach. The CMap screen specifically identified metergoline, an ergot alkaloid currently used to treat seasonal affective disorder, as a drug of interest. In mice, metergoline dose-dependently reduced MK-801- or methamphetamine-induced locomotor hyperactivity confirming the potential of metergoline to treat positive symptoms of schizophrenia in an animal model. Metergoline had no effects on prepulse inhibition deficits induced by MK-801 or methamphetamine. Taken together, metergoline appears a promising drug for further studies to be repurposed as a treatment for schizophrenia and possibly other psychiatric disorders.

Intestinal microbiota associated with differential feed conversion efficiency in chickens.

Analysis of model systems, for example in mice, has shown that the microbiota in the gastrointestinal tract can play an important role in the efficiency of energy extraction from diets. The study reported here aimed to determine whether there are correlations between gastrointestinal tract microbiota population structure and energy use in chickens. Efficiency in converting food into muscle mass has a significant impact on the intensive animal production industries, where feed represents the major portion of production costs. Despite extensive breeding and selection efforts, there are still large differences in the growth performance of animals fed identical diets and reared under the same conditions. Variability in growth performance presents management difficulties and causes economic loss. An understanding of possible microbiota drivers of these differences has potentially important benefits for industry. In this study, differences in cecal and jejunal microbiota between broiler chickens with extreme feed conversion capabilities were analysed in order to identify candidate bacteria that may influence growth performance. The jejunal microbiota was largely dominated by lactobacilli (over 99% of jejunal sequences) and showed no difference between the birds with high and low feed conversion ratios. The cecal microbial community displayed higher diversity, and 24 unclassified bacterial species were found to be significantly (<0.05) differentially abundant between high and low performing birds. Such differentially abundant bacteria represent target populations that could potentially be modified with prebiotics and probiotics in order to improve animal growth performance.