Regional and strain-specific gene expression mapping in the adult mouse brain

Pleiotropy, epistasis and the genetic architecture of quantitative traits

Pleiotropy (whereby one genetic polymorphism affects multiple traits) and epistasis (whereby non-linear interactions between genetic polymorphisms affect the same trait) are fundamental aspects of the genetic architecture of quantitative traits. Recent advances in the ability to characterize the effects of polymorphic variants on molecular and organismal phenotypes in human and model organism populations have revealed the prevalence of pleiotropy and unexpected shared molecular genetic bases among quantitative traits, including diseases. By contrast, epistasis is common between polymorphic loci associated with quantitative traits in model organisms, such that alleles at one locus have different effects in different genetic backgrounds, but is rarely observed for human quantitative traits and common diseases. Here, we review the concepts and recent inferences about pleiotropy and epistasis, and discuss factors that contribute to similarities and differences between the genetic architecture of quantitative traits in model organisms and humans.

Relationship between sex biases in gene expression and sex biases in autism and Alzheimer's disease

BACKGROUND

Sex differences in the brain may play an important role in sex-differential prevalence of neuropsychiatric conditions.

METHODS

In order to understand the transcriptional basis of sex differences, we analyzed multiple, large-scale, human postmortem brain RNA-Seq datasets using both within-region and pan-regional frameworks.

RESULTS

We find evidence of sex-biased transcription in many autosomal genes, some of which provide evidence for pathways and cell population differences between chromosomally male and female individuals. These analyses also highlight regional differences in the extent of sex-differential gene expression. We observe an increase in specific neuronal transcripts in male brains and an increase in immune and glial function-related transcripts in female brains. Integration with single-nucleus data suggests this corresponds to sex differences in cellular states rather than cell abundance. Integration with case-control gene expression studies suggests a female molecular predisposition towards Alzheimer's disease, a female-biased disease. Autism, a male-biased diagnosis, does not exhibit a male predisposition pattern in our analysis.

CONCLUSION

Overall, these analyses highlight mechanisms by which sex differences may interact with sex-biased conditions in the brain. Furthermore, we provide region-specific analyses of sex differences in brain gene expression to enable additional studies at the interface of gene expression and diagnostic differences.

Towards reliable reconstruction of the mouse brain corticothalamic connectivity using diffusion MRI

Diffusion magnetic resonance imaging (dMRI) tractography has yielded intriguing insights into brain circuits and their relationship to behavior in response to gene mutations or neurological diseases across a number of species. Still, existing tractography approaches suffer from limited sensitivity and specificity, leading to uncertain interpretation of the reconstructed connections. Hence, in this study, we aimed to optimize the imaging and computational pipeline to achieve the best possible spatial overlaps between the tractography and tracer-based axonal projection maps within the mouse brain corticothalamic network. We developed a dMRI-based atlas of the mouse forebrain with structural labels imported from the Allen Mouse Brain Atlas (AMBA). Using the atlas and dMRI tractography, we first reconstructed detailed node-to-node mouse brain corticothalamic structural connectivity matrices using different imaging and tractography parameters. We then investigated the effects of each condition for accurate reconstruction of the corticothalamic projections by quantifying the similarities between the tractography and the tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA). Our results suggest that these parameters significantly affect tractography outcomes and our atlas can be used to investigate macroscopic structural connectivity in the mouse brain. Furthermore, tractography in mouse brain gray matter still face challenges and need improved imaging and tractography methods.

Association of CNR1 gene and cannabinoid 1 receptor protein in the human brain

We aimed to integrate genomic mapping from brain mRNA atlas with the protein expression from positron emission tomography (PET) scans of type 1 cannabinoid (CB1) receptor and to compare the predictive power of CB1 receptor with those of other neuroreceptor/transporters using a meta-analysis. Volume of distribution (V_T ) from F18-FMPEP-d2 PET scans, CNR1 gene (Cannabinoid receptor 1) expression, and H3-CP55940 binding were calculated and correlation analysis was performed. Between V_T of F18-FMPEP-d2 PET scans and CNR1 mRNA expression, moderate strength of correlation was observed (rho = .5067, p = .0337). Strong positive correlation was also found between CNR1 mRNA expression and H3-CP55940 binding (r = .6336, p = .0364), validating the finding between F18-FMPEP-d2 PET scans and CNR1 mRNA. The correlation between V_T of F18-FMPEP-d2 PET scans and H3-CP55940 binding was marginally significant (r = .5025, p = .0563). From the meta-analysis, the correlation coefficient between mRNA expression and protein expressions ranged from -.10 to .99, with a pooled effect of .76. In conclusion, we observed the moderate to strong associations between gene and protein expression for CB1 receptor in the human brain, which was validated by autoradiography. We combined the autoradiographic finding with PET of CB1 receptor, producing the density atlas map of CB1 receptor. From the meta-analysis, the moderate to strong correlation was observed between mRNA expression and protein expressions across multiple genes. Further study is needed to investigate the relationship between multiple genes and in vivo proteins to improve and accelerate drug development.

Gene Expression Profile as a Predictor of Seizure Liability

Analysis platforms to predict drug-induced seizure liability at an early phase of drug development would improve safety and reduce attrition and the high cost of drug development. We hypothesized that a drug-induced in vitro transcriptomics signature predicts its ictogenicity. We exposed rat cortical neuronal cultures to non-toxic concentrations of 34 compounds for 24 h; 11 were known to be ictogenic (tool compounds), 13 were associated with a high number of seizure-related adverse event reports in the clinical FDA Adverse Event Reporting System (FAERS) database and systematic literature search (FAERS-positive compounds), and 10 were known to be non-ictogenic (FAERS-negative compounds). The drug-induced gene expression profile was assessed from RNA-sequencing data. Transcriptomics profiles induced by the tool, FAERS-positive and FAERS-negative compounds, were compared using bioinformatics and machine learning. Of the 13 FAERS-positive compounds, 11 induced significant differential gene expression; 10 of the 11 showed an overall high similarity to the profile of at least one tool compound, correctly predicting the ictogenicity. Alikeness-% based on the number of the same differentially expressed genes correctly categorized 85%, the Gene Set Enrichment Analysis score correctly categorized 73%, and the machine-learning approach correctly categorized 91% of the FAERS-positive compounds with reported seizure liability currently in clinical use. Our data suggest that the drug-induced gene expression profile could be used as a predictive biomarker for seizure liability.

Brain gene expression differences related to ethanol preference in the collaborative cross founder strains

The collaborative cross (CC) founder strains include five classical inbred laboratory strains [129S1/SvlmJ (S129), A/J (AJ), C57BL/6J (B6), NOD/ShiLtJ (NOD), and NZO/HILtJ (NZO)] and three wild-derived strains [CAST/EiJ (CAST), PWK/PhJ (PWK), and WSB/EiJ (WSB)]. These strains encompass 89% of the genetic diversity available in Mus musculus and ∼10-20 times more genetic diversity than found in Homo sapiens. For more than 60 years the B6 strain has been widely used as a genetic model for high ethanol preference and consumption. However, another of the CC founder strains, PWK, has been identified as a high ethanol preference/high consumption strain. The current study determined how the transcriptomes of the B6 and PWK strains differed from the 6 low preference CC strains across 3 nodes of the brain addiction circuit. RNA-Seq data were collected from the central nucleus of the amygdala (CeA), the nucleus accumbens core (NAcc) and the prelimbic cortex (PrL). Differential expression (DE) analysis was performed in each of these brain regions for all 28 possible pairwise comparisons of the CC founder strains. Unique genes for each strain were identified by selecting for genes that differed significantly [false discovery rate (FDR) < 0.05] from all other strains in the same direction. B6 was identified as the most distinct classical inbred laboratory strain, having the highest number of total differently expressed genes (DEGs) and DEGs with high log fold change, and unique genes compared to other CC strains. Less than 50 unique DEGs were identified in common between B6 and PWK within all three brain regions, indicating the strains potentially represent two distinct genetic signatures for risk for high ethanol-preference. 338 DEGs were found to be commonly different between B6, PWK and the average expression of the remaining CC strains within all three regions. The commonly different up-expressed genes were significantly enriched (FDR < 0.001) among genes associated with neuroimmune function. These data compliment findings showing that neuroimmune signaling is key to understanding alcohol use disorder (AUD) and support use of these 8 strains and the highly heterogeneous mouse populations derived from them to identify alcohol-related brain mechanisms and treatment targets.

Poly(A) RNA sequencing reveals age-related differences in the prefrontal cortex of dogs

Dogs may possess a unique translational potential to investigate neural aging and dementia because they are prone to age-related cognitive decline, including an Alzheimer’s disease–like pathological condition. Yet very little is known about the molecular mechanisms underlying canine cognitive decline. The goal of the current study was to explore the transcriptomic differences between young and old dogs’ frontal cortex, which is a brain region often affected by various forms of age-related dementia in humans. RNA isolates from the frontal cortical brain area of 13 pet dogs, which represented 7 different breeds and crossbreds, were analyzed. The dogs were euthanized for medical reasons, and their bodies had been donated by their owners for scientific purposes. The poly(A) tail RNA subfraction of the total transcriptome was targeted in the sequencing analysis. Cluster analyses, differential gene expression analyses, and gene ontology analyses were carried out to assess which genes and genetic regulatory mechanisms were mostly affected by aging. Age was the most prominent factor in the clustering of the animals, indicating the presence of distinct gene expression patterns related to aging in a genetically variable population. A total of 3436 genes were found to be differentially expressed between the age groups, many of which were linked to neural function, immune system, and protein synthesis. These findings are in accordance with previous human brain aging RNA sequencing studies. Some genes were found to behave more similarly to humans than to rodents, further supporting the applicability of dogs in translational aging research.

Limited power of dopamine transporter mRNA mapping for predicting dopamine transporter availability

Dopamine transporters (DAT) are transmembrane proteins that translocate dopamine from the extracellular space into presynaptic neurons. We aimed to investigate the predictive power of DAT mRNA for DAT protein expression, measured using positron emission tomography (PET). We performed ¹⁸ F-FP-CIT PET scans in 35 healthy individuals. Binding potentials (BP_ND ) from the ventral striatum, caudate nucleus, putamen, and middle frontal, orbitofrontal, cingulate, parietal, and temporal cortices were measured. DAT gene expression data were obtained from the freely available Allen Human Brain Atlas derived from six healthy donors. The auto-correlation of PET-derived BP_ND s for DAT was intermediate (mean ρ² = 0.66) with ρ² ranging from 0.0811 to 1. However, the auto-correlation of mRNA expression was weak across the probes with a mean ρ² of 0.09-0.23. Cross-correlations between PET-derived BP_ND s and mRNA expression were weak with a mean ρ² ranging from 0 to 0.22 across the probes. In conclusion, we observed weak associations between DAT mRNA expression and DAT availability in human brains. Therefore, DAT mRNA mapping may have only limited predictive power for DAT availability in humans. However, the difference in distribution of DAT mRNA and DAT protein may influence this limitation. This article is protected by copyright. All rights reserved.

Comparison of Gene Expression Between Resistant and Susceptible Families Against VPAHPND and Identification of Biomarkers Used for Resistance Evaluation in Litopenaeus vannamei

Acute hepatopancreatic necrosis disease (AHPND) has caused a heavy loss to shrimp aquaculture since its outbreak. Vibrio parahaemolyticus (VP_AHPND) is regarded as one of the main pathogens that caused AHPND in the Pacific white shrimp Litopenaeus vannamei. In order to learn more about the mechanism of resistance to AHPND, the resistant and susceptible shrimp families were obtained through genetic breeding, and comparative transcriptome approach was used to analyze the gene expression patterns between resistant and susceptible families. A total of 95 families were subjected to VP_AHPND challenge test, and significant variations in the resistance of these families were observed. Three pairs of resistant and susceptible families were selected for transcriptome sequencing. A total of 489 differentially expressed genes (DEGs) that presented in at least two pairwise comparisons were screened, including 196 DEGs highly expressed in the susceptible families and 293 DEGs in the resistant families. Among these DEGs, 16 genes demonstrated significant difference in all three pairwise comparisons. Gene set enrichment analysis (GSEA) of all 27,331 expressed genes indicated that some energy metabolism processes were enriched in the resistant families, while signal transduction and immune system were enriched in the susceptible families. A total of 32 DEGs were further confirmed in the offspring of the detected families, among which 19 genes were successfully verified. The identified genes in this study will be useful for clarifying the genetic mechanism of shrimp resistance against Vibrio and will further provide molecular markers for evaluating the disease resistance of shrimp in the breeding program.

On the Use of Heterogeneous Stock Mice to Map Transcriptomes Associated With Excessive Ethanol Consumption

We and many others have noted the advantages of using heterogeneous (HS) animals to map genes and gene networks associated with both behavioral and non-behavioral phenotypes. Importantly, genetically complex Mus musculus crosses provide substantially increased resolution to examine old and new relationships between gene expression and behavior. Here we report on data obtained from two HS populations: the HS/NPT derived from eight inbred laboratory mouse strains and the HS-CC derived from the eight collaborative cross inbred mouse strains that includes three wild-derived strains. Our work has focused on the genes and gene networks associated with risk for excessive ethanol consumption, individual variation in ethanol consumption and the consequences, including escalation, of long-term ethanol consumption. Background data on the development of HS mice is provided, including advantages for the detection of expression quantitative trait loci. Examples are also provided of using HS animals to probe the genes associated with ethanol preference and binge ethanol consumption.

Pairwise interactions in gene expression determine a hierarchical transcriptional profile in the human brain

The orchestrated expression of thousands of genes gives rise to the complexity of the human brain. However, the structures governing these myriad gene-gene interactions remain unclear. By analyzing transcription data from more than 2000 sites in six human brains, we found that pairwise interactions between genes, without considering any higher-order interactions, are sufficient to predict the transcriptional pattern of the genome for individual brain regions and the transcriptional profile of the entire brain consisting of more than 200 areas. These findings suggest a quadratic complexity of transcriptional patterns in the human brain, which is much simpler than expected. In addition, using a pairwise interaction model, we revealed that the strength of gene-gene interactions in the human brain gives rise to the nearly maximal number of transcriptional clusters, which may account for the functional and structural richness of the brain.

Melatonin and Melatonergic Influence on Neuronal Transcription Factors: Implications for the Development of Novel Therapies for Neurodegenerative Disorders

Melatonin is a multifunctional signalling molecule that is secreted by the mammalian pineal gland, and also found in a number of organisms including plants and bacteria. Research has continued to uncover an ever-increasing number of processes in which melatonin is known to play crucial roles in mammals. Amongst these functions is its contribution to cell multiplication, differentiation and survival in the brain. Experimental studies show that melatonin can achieve these functions by influencing transcription factors which control neuronal and glial gene expression. Since neuronal survival and differentiation are processes that are important determinants of the pathogenesis, course and outcome of neurodegenerative disorders; the known and potential influences of melatonin on neuronal and glial transcription factors are worthy of constant examination. In this review, relevant scientific literature on the role of melatonin in preventing or altering the course and outcome of neurodegenerative disorders, by focusing on melatonin's influence on transcription factors is examined. A number of transcription factors whose functions can be influenced by melatonin in neurodegenerative disease models have also been highlighted. Finally, the therapeutic implications of melatonin's influences have also been discussed and the potential limitations to its applications have been highlighted.

Spatial Transcriptomics: Molecular Maps of the Mammalian Brain

Maps of the nervous system inspire experiments and theories in neuroscience. Advances in molecular biology over the past decades have revolutionized the definition of cell and tissue identity. Spatial transcriptomics has opened up a new era in neuroanatomy, where the unsupervised and unbiased exploration of the molecular signatures of tissue organization will give rise to a new generation of brain maps. We propose that the molecular classification of brain regions on the basis of their gene expression profile can circumvent subjective neuroanatomical definitions and produce common reference frameworks that can incorporate cell types, connectivity, activity, and other modalities. Here we review the technological and conceptual advances made possible by spatial transcriptomics in the context of advancing neuroanatomy and discuss how molecular neuroanatomy can redefine mapping of the nervous system.

Turning strains into strengths for understanding psychiatric disorders

There is a paucity in the development of new mechanistic insights and therapeutic approaches for treating psychiatric disease. One of the major challenges is reflected in the growing consensus that risk for these diseases is not determined by a single gene, but rather is polygenic, arising from the action and interaction of multiple genes. Canonically, experimental models in mice have been designed to ascertain the relative contribution of a single gene to a disease by systematic manipulation (e.g., mutation or deletion) of a known candidate gene. Because these studies have been largely carried out using inbred isogenic mouse strains, in which there is no (or very little) genetic diversity among subjects, it is difficult to identify unique allelic variants, gene modifiers, and epigenetic factors that strongly affect the nature and severity of these diseases. Here, we review various methods that take advantage of existing genetic diversity or that increase genetic variance in mouse models to (1) strengthen conclusions of single-gene function; (2) model diversity among human populations; and (3) dissect complex phenotypes that arise from the actions of multiple genes.

GluD1 knockout mice with a pure C57BL/6N background show impaired fear memory, social interaction, and enhanced depressive-like behavior

The GluD1 gene is associated with susceptibility for schizophrenia, autism, depression, and bipolar disorder. However, the function of GluD1 and how it is involved in these conditions remain elusive. In this study, we generated a Grid1 gene-knockout (GluD1-KO) mouse line with a pure C57BL/6N genetic background and performed several behavioral analyses. Compared to a control group, GluD1-KO mice showed no significant anxiety-related behavioral differences, evaluated using behavior in an open field, elevated plus maze, a light-dark transition test, the resident-intruder test of aggression and sensorimotor gating evaluated by the prepulse inhibition test. However, GluD1-KO mice showed (1) higher locomotor activity in the open field, (2) decreased sociability and social novelty preference in the three-chambered social interaction test, (3) impaired memory in contextual, but not cued fear conditioning tests, and (4) enhanced depressive-like behavior in a forced swim test. Pharmacological studies revealed that enhanced depressive-like behavior in GluD1-KO mice was restored by the serotonin reuptake inhibitors imipramine and fluoxetine, but not the norepinephrine transporter inhibitor desipramine. In addition, biochemical analysis revealed no significant difference in protein expression levels, such as other glutamate receptors in the synaptosome and postsynaptic densities prepared from the frontal cortex and the hippocampus. These results suggest that GluD1 plays critical roles in fear memory, sociability, and depressive-like behavior.

Differential Sensitivity of Two Endothelial Cell Lines to Hydrogen Peroxide Toxicity: Relevance for In Vitro Studies of the Blood-Brain Barrier

Oxidative stress (OS) has been linked to blood–brain barrier (BBB) dysfunction which in turn has been implicated in the initiation and propagation of some neurological diseases. In this study, we profiled, for the first time, two endothelioma cell lines of mouse brain origin, commonly used as in vitro models of the blood–brain barrier, for their resistance against oxidative stress using viability measures and glutathione contents as markers. OS was induced by exposing cultured cells to varying concentrations of hydrogen peroxide and fluorescence microscopy/spectrometry was used to detect and estimate cellular glutathione contents. A colorimetric viability assay was used to determine changes in the viability of OS-exposed cells. Both the b.End5 and bEnd.3 cell lines investigated showed demonstrable content of glutathione with a statistically insignificant difference in glutathione quantity per unit cell, but with a statistically significant higher capacity for the b.End5 cell line for de novo glutathione synthesis. Furthermore, the b.End5 cells demonstrated greater oxidant buffering capacity to higher concentrations of hydrogen peroxide than the bEnd.3 cells. We concluded that mouse brain endothelial cells, derived from different types of cell lines, differ enormously in their antioxidant characteristics. We hereby recommend caution in making comparisons across BBB models utilizing distinctly different cell lines and require further prerequisites to ensure that in vitro BBB models involving these cell lines are reliable and reproducible.

Temporal Expression and Cellular Localization of PAPPA2 in the Developing Kidney of Rat

PAPPA2 is a metalloproteinase which cleaves insulin-like growth factor binding protein (IGFBP)-3 and IGFBP-5, and its role in pregnancy and postnatal growth is primarily studied. Using exclusion mapping, we reported a subcongenic (26-P) rat where a 0.71-Mbp region containing the pregnancy-associated plasma protein a2 (Pappa2) allele of salt-insensitive Brown Norway (BN) was introgressed into Dahl saltsensitive (SS) genetic background, resulting in the reduction of salt sensitivity. Pappa2 was differentially expressed in the adult kidney of 26-P and SS rats. Here, the expression and cellular localization of Pappa2 in embryonic and postnatal kidneys of 26-P and SS rats were examined. Pappa2 mRNA expression was 5-fold higher in the embryonic kidney (day 20.5) of the 26-P rat compared with the SS rat. Pappa2 mRNA expression progressively increased with the development of kidney, reaching a peak at postnatal day 5 before trending downward in subsequent stages of development in both strains. At all tested time points, Pappa2 remained higher in the 26-P compared with the SS rat kidney. Immunohistochemistry studies localized PAPPA2 in the ureteric bud (UB) and distal part of S-shaped body. PAPPA2 was colocalized with IGFBP-5 in the UB and Na⁺/K⁺/2Cl^- cotransporter-stained tubules, respectively. Future studies are needed to determine the role of Pappa2 in kidney development and mechanistic pathways involved in this process.

Maternal Experience Leads to Lasting Gene Expression Changes in Some Regions of the Mouse Brain

Rodent maternal behaviors are due to the coordinated effects of fluctuating hormones, with their onset triggered by interactions with newborn pups. Previous studies have shown that many genes have changes in expression during peripartum stages. However, it is unclear if there are long-lasting changes in gene expression, well after the performance of maternal behaviors, that could influence physiology and behavior throughout the remaining lifespan. Here, gene expression differences were examined in mouse between age-matched virgin and primiparous females, at least 4 weeks after weaning. Of the five brain regions examined-hypothalamus, hippocampus, cortex, cerebellum, and the amygdala-only the hypothalamus had thousands of genes with significant expression differences. The cerebellum had 130 genes with expression differences, and the other brain regions had no significant changes detected. The expression changes in the hypothalamus include an enrichment of genes that could mediate long-lasting behavioral and physiological changes, given their known roles in parental behavior, including galanin and prolactin receptor.

Pericyte loss leads to circulatory failure and pleiotrophin depletion causing neuron loss

Pericytes are positioned between brain capillary endothelial cells, astrocytes and neurons. They degenerate in multiple neurological disorders. However, their role in the pathogenesis of these disorders remains debatable. Here we generate an inducible pericyte-specific Cre line and cross pericyte-specific Cre mice with iDTR mice carrying Cre-dependent human diphtheria toxin receptor. After pericyte ablation with diphtheria toxin, mice showed acute blood-brain barrier breakdown, severe loss of blood flow, and a rapid neuron loss that was associated with loss of pericyte-derived pleiotrophin (PTN), a neurotrophic growth factor. Intracerebroventricular PTN infusions prevented neuron loss in pericyte-ablated mice despite persistent circulatory changes. Silencing of pericyte-derived Ptn rendered neurons vulnerable to ischemic and excitotoxic injury. Our data demonstrate a rapid neurodegeneration cascade that links pericyte loss to acute circulatory collapse and loss of PTN neurotrophic support. These findings may have implications for the pathogenesis and treatment of neurological disorders that are associated with pericyte loss and/or neurovascular dysfunction.

Biochemical and physiological properties of K+ channel-associated AKR6A (Kvβ) proteins

Voltage-gated potassium (Kv) channels play an essential role in the regulation of membrane excitability and thereby control physiological processes such as cardiac excitability, neural communication, muscle contraction, and hormone secretion. Members of the Kv1 and Kv4 families are known to associate with auxiliary intracellular Kvβ subunits, which belong to the aldo-keto reductase superfamily. Electrophysiological studies have shown that these proteins regulate the gating properties of Kv channels. Although the three gene products encoding Kvβ proteins are functional enzymes in that they catalyze the nicotinamide adenine dinucleotide phosphate (NAD[P]H)-dependent reduction of a wide range of aldehyde and ketone substrates, the physiological role for these proteins and how each subtype may perform unique roles in coupling membrane excitability with cellular metabolic processes remains unclear. Here, we discuss current knowledge of the enzymatic properties of Kvβ proteins from biochemical studies with their described and purported physiological and pathophysiological influences.

Molecular phenotyping of laboratory mouse strains using 500 multiple reaction monitoring mass spectrometry plasma assays

Mouse is the predominant experimental model for the study of human disease due, in part, to phylogenetic relationship, ease of breeding, and the availability of molecular tools for genetic manipulation. Advances in genome-editing methodologies, such as CRISPR-Cas9, enable the rapid production of new transgenic mouse strains, necessitating complementary high-throughput and systematic phenotyping technologies. In contrast to traditional protein phenotyping techniques, multiple reaction monitoring (MRM) mass spectrometry can be highly multiplexed without forgoing specificity or quantitative precision. Here we present MRM assays for the quantitation of 500 proteins and subsequently determine reference concentration values for plasma proteins across five laboratory mouse strains that are typically used in biomedical research, revealing inter-strain and intra-strain phenotypic differences. These 500 MRM assays will have a broad range of research applications including high-throughput phenotypic validation of novel transgenic mice, identification of candidate biomarkers, and general research applications requiring multiplexed and precise protein quantification.

The Effects of Normal Aging on Regional Accumulation of Hyaluronan and Chondroitin Sulfate Proteoglycans in the Mouse Brain

The brain changes in volume and composition with normal aging. Cellular components of the brain are supported by an extracellular matrix (ECM) comprised largely of hyaluronan (HA) and HA-associated members of the lectican family of chondroitin sulfate proteoglycans (CSPGs). We examined regional differences in microvascular density, neuronal and glial markers, and accumulation of HA and CSPGs in mouse brains during normal aging. The cortex, hippocampus, dentate gyrus, and cerebellum of young (4 months), middle-aged (14 months), and aged (24-26 months) brains were analyzed. Microvascular density decreased in cerebral cortex and cerebellum with age. There were no detectable differences in neuronal density. There was an increase in astrocytes in the hippocampus with aging. HA accumulation was higher in aged brain relative to young brain in the cerebral cortex and cerebellum, but not in other regions examined. In contrast, CSPGs did not change with aging in any of the brain regions examined. HA and CSPGs colocalized with a subset of neuronal cell bodies and astrocytes, and at the microvasculature. Differences in accumulation of ECM in the aging brain, in the setting of decreased microvascular density and/or increased glial activation, might contribute to age-related regional differences in vulnerability to injury and ischemia.

Assembling a Cellular User Manual for the Brain

For many years, efforts to decipher the various cellular components that comprise the CNS were stymied by a lack of technical strategies for isolating and profiling the brain's resident cell types. The advent of transcriptional profiling, combined with powerful new purification schemes, changed this reality and transformed our understanding of the macroglial populations within the brain. Here, we chronicle the historical context and scientific setting for our efforts to transcriptionally profile neurons, astrocytes, and oligodendrocytes, and highlight some of the profound discoveries that were cultivated by these data.Following a lengthy battle with pancreatic cancer, Ben Barres passed away during the writing of this Progression piece. Among Ben's innumerable contributions to the greater scientific community, his addition of publicly available transcriptome databases of CNS cell types will forever remain a relic of his generous spirit and boundless scientific curiosity. Although he had impressively committed a majority of these enormous gene lists to memory, Ben could oftentimes be spotted at meetings buried in his cell phone on the Barres RNAseq database. Perhaps the only thing he enjoyed more than exploring these data himself, was knowing how useful these contributions had been (and will hopefully continue to be) to his scientific peers.

Mapping Molecular Datasets Back to the Brain Regions They are Extracted from: Remembering the Native Countries of Hypothalamic Expatriates and Refugees

This article focuses on approaches to link transcriptomic, proteomic, and peptidomic datasets mined from brain tissue to the original locations within the brain that they are derived from using digital atlas mapping techniques. We use, as an example, the transcriptomic, proteomic and peptidomic analyses conducted in the mammalian hypothalamus. Following a brief historical overview, we highlight studies that have mined biochemical and molecular information from the hypothalamus and then lay out a strategy for how these data can be linked spatially to the mapped locations in a canonical brain atlas where the data come from, thereby allowing researchers to integrate these data with other datasets across multiple scales. A key methodology that enables atlas-based mapping of extracted datasets-laser-capture microdissection-is discussed in detail, with a view of how this technology is a bridge between systems biology and systems neuroscience.

Single-Cell Dissociation and Characterization in the Murine Retina and Optic Nerve

Recent technological advances have extended the range of analytic tools to very small samples. It is now possible to assay the transcriptome, and in some cases even the proteome, of single cells reliably. This allows addressing novel questions, such as the genotype/phenotype relationships of single neurons, heterogeneity within individual cells of the same type, or the basis of differential vulnerability to injury. An important prerequisite for these kinds of studies is the ability to isolate well-defined individual cells without contamination by adjacent tissue. In the retina and optic nerve, cells of different types and functions are closely intermingled, limiting the use of standard methods such as laser capture microdissection. Here, we describe a simple method to isolate morphologically intact cells from the retina and the optic nerve and discuss considerations in recognizing and isolating different cell types after dissociation.

The Genotype-Phenotype Relationships in the Light of Natural Selection

Although any genotype–phenotype relationships are a result of evolution, little is known about how natural selection and neutral drift, two distinct driving forces of evolution, operate to shape the relationships. By analyzing ∼500 yeast quantitative traits, we reveal a basic “supervisor–worker” gene architecture underlying a trait. Supervisors are often identified by “perturbational” approaches (such as gene deletion), whereas workers, which usually show small and statistically insignificant deletion effects, are tracked primarily by “observational” approaches that examine the correlation between gene activity and trait value across a number of conditions. Accordingly, supervisors provide most of the genetic understandings of the trait whereas workers provide rich mechanistic understandings. Further analyses suggest that most observed supervisor–worker interactions may evolve largely neutrally, resulting in pervasive between-worker epistasis that suppresses the tractability of workers. In contrast, a fraction of supervisors are recruited/maintained by natural selection to build worker co-expression, boosting the tractability of workers. Thus, by revealing a supervisor–worker gene architecture underlying complex traits, the opposite roles of natural selection versus neutral drift in shaping the gene architecture, and the complementary strengths of the perturbational and observational research strategies in characterizing the gene architecture, this study may lay a new conceptual foundation for understanding the molecular basis of complex traits.

Gene co-expression network connectivity is an important determinant of selective constraint

While several studies have investigated general properties of the genetic architecture of natural variation in gene expression, few of these have considered natural, outbreeding populations. In parallel, systems biology has established that a general feature of biological networks is that they are scale-free, rendering them buffered against random mutations. To date, few studies have attempted to examine the relationship between the selective processes acting to maintain natural variation of gene expression and the associated co-expression network structure. Here we utilised RNA-Sequencing to assay gene expression in winter buds undergoing bud flush in a natural population of Populus tremula, an outbreeding forest tree species. We performed expression Quantitative Trait Locus (eQTL) mapping and identified 164,290 significant eQTLs associating 6,241 unique genes (eGenes) with 147,419 unique SNPs (eSNPs). We found approximately four times as many local as distant eQTLs, with local eQTLs having significantly higher effect sizes. eQTLs were primarily located in regulatory regions of genes (UTRs or flanking regions), regardless of whether they were local or distant. We used the gene expression data to infer a co-expression network and investigated the relationship between network topology, the genetic architecture of gene expression and signatures of selection. Within the co-expression network, eGenes were underrepresented in network module cores (hubs) and overrepresented in the periphery of the network, with a negative correlation between eQTL effect size and network connectivity. We additionally found that module core genes have experienced stronger selective constraint on coding and non-coding sequence, with connectivity associated with signatures of selection. Our integrated genetics and genomics results suggest that purifying selection is the primary mechanism underlying the genetic architecture of natural variation in gene expression assayed in flushing leaf buds of P. tremula and that connectivity within the co-expression network is linked to the strength of purifying selection.

Hipposeq: a comprehensive RNA-seq database of gene expression in hippocampal principal neurons

Clarifying gene expression in narrowly defined neuronal populations can provide insight into cellular identity, computation, and functionality. Here, we used next-generation RNA sequencing (RNA-seq) to produce a quantitative, whole genome characterization of gene expression for the major excitatory neuronal classes of the hippocampus; namely, granule cells and mossy cells of the dentate gyrus, and pyramidal cells of areas CA3, CA2, and CA1. Moreover, for the canonical cell classes of the trisynaptic loop, we profiled transcriptomes at both dorsal and ventral poles, producing a cell-class- and region-specific transcriptional description for these populations. This dataset clarifies the transcriptional properties and identities of lesser-known cell classes, and moreover reveals unexpected variation in the trisynaptic loop across the dorsal-ventral axis. We have created a public resource, Hipposeq (http://hipposeq.janelia.org), which provides analysis and visualization of these data and will act as a roadmap relating molecules to cells, circuits, and computation in the hippocampus.

DOI:http://dx.doi.org/10.7554/eLife.14997.001

Both mouse and human brains are made up of many millions of cells called neurons that are interconnected to form circuits. These neurons are not all the same, because different classes of neurons express different complements of genes. Neurons that express similar genes tend to look and act alike, whereas neurons that express different genes tend to be dissimilar.

Cembrowski et al. have used a technique called next-generation RNA sequencing (RNA-seq) to determine which genes are expressed in groups of neurons that represent the main cell types found in a part of the brain called the hippocampus. This brain region is important for memory, and was chosen because the location and appearance of the main cell types in the hippocampus were already well understood.

The approach revealed that the main types of neurons in the mouse hippocampus are all very different from each other in terms of gene expression, and that even neurons of the same type can exhibit large differences across the hippocampus. Cembrowski et al. created a website that will allow other researchers to easily navigate, analyze, and visualize gene expression data in these populations of neurons.

Future work could next make use of recent technological advances to analyze gene expression in individual neurons, rather than groups of cells, to provide an even more detailed picture. It is also hoped that understanding the differences in gene expression will guide examination of how the hippocampus contributes to memory and what goes wrong in diseases that affect this region of the brain.

DOI:http://dx.doi.org/10.7554/eLife.14997.002

An Examination of Dynamic Gene Expression Changes in the Mouse Brain During Pregnancy and the Postpartum Period

The developmental transition to motherhood requires gene expression changes that alter the brain to drive the female to perform maternal behaviors. We broadly examined the global transcriptional response in the mouse maternal brain, by examining four brain regions: hypothalamus, hippocampus, neocortex, and cerebellum, in virgin females, two pregnancy time points, and three postpartum time points. We find that overall there are hundreds of differentially expressed genes, but each brain region and time point shows a unique molecular signature, with only 49 genes differentially expressed in all four regions. Interestingly, a set of “early-response genes” is repressed in all brain regions during pregnancy and postpartum stages. Several genes previously implicated in underlying postpartum depression change expression. This study serves as an atlas of gene expression changes in the maternal brain, with the results demonstrating that pregnancy, parturition, and postpartum maternal experience substantially impact diverse brain regions.

Stereomicroscopic 3D-pattern profiling of murine and human intestinal inflammation reveals unique structural phenotypes

Histology is fundamental to assess two-dimensional intestinal inflammation; however, inflammatory bowel diseases (IBDs) are often indistinguishable microscopically on the basis of mucosal biopsies. Here, we use stereomicroscopy (SM) to rapidly profile the entire intestinal topography and assess inflammation. We examine the mucosal surface of >700 mice (encompassing >16 strains and various IBD-models), create a profiling catalogue of 3D-stereomicroscopic abnormalities and demonstrate that mice with comparable histological scores display unique sub-clusters of 3D-structure-patterns of IBD pathology, which we call 3D-stereoenterotypes, and which are otherwise indiscernible histologically. We show that two ileal IBD-stereoenterotypes ('cobblestones' versus 'villous mini-aggregation') cluster separately within two distinct mouse lines of spontaneous ileitis, suggesting that host genetics drive unique and divergent inflammatory 3D-structural patterns in the gut. In humans, stereomicroscopy reveals 'liquefaction' lesions and hierarchical fistulous complexes, enriched with clostridia/segmented filamentous bacteria, running under healthy mucosa in Crohn's disease. We suggest that stereomicroscopic (3D-SMAPgut) profiling can be easily implemented and enable the comprehensive study of inflammatory 3D structures, genetics and flora in IBD.

Involvement of cAMP-guanine nucleotide exchange factor II in hippocampal long-term depression and behavioral flexibility

Background

Guanine nucleotide exchange factors (GEFs) activate small GTPases that are involved in several cellular functions. cAMP-guanine nucleotide exchange factor II (cAMP-GEF II) acts as a target for cAMP independently of protein kinase A (PKA) and functions as a GEF for Rap1 and Rap2. Although cAMP-GEF II is expressed abundantly in several brain areas including the cortex, striatum, and hippocampus, its specific function and possible role in hippocampal synaptic plasticity and cognitive processes remain elusive. Here, we investigated how cAMP-GEF II affects synaptic function and animal behavior using cAMP-GEF II knockout mice.

Results

We found that deletion of cAMP-GEF II induced moderate decrease in long-term potentiation, although this decrease was not statistically significant. On the other hand, it produced a significant and clear impairment in NMDA receptor-dependent long-term depression at the Schaffer collateral-CA1 synapses of hippocampus, while microscopic morphology, basal synaptic transmission, and depotentiation were normal. Behavioral testing using the Morris water maze and automated IntelliCage system showed that cAMP-GEF II deficient mice had moderately reduced behavioral flexibility in spatial learning and memory.

Conclusions

We concluded that cAMP-GEF II plays a key role in hippocampal functions including behavioral flexibility in reversal learning and in mechanisms underlying induction of long-term depression.

Isolation of intact astrocytes from the optic nerve head of adult mice

The astrocytes of the optic nerve head are a specialized subtype of white matter astrocytes that form the direct cellular environment of the unmyelinated ganglion cell axons. Due to their potential involvement in glaucoma, these astrocytes have become a target of research. Due to the heterogeneity of the optic nerve tissue, which also contains other cell types, in some cases it may be desirable to conduct gene expression studies on small numbers of well-characterized astrocytes or even individual cells. Here, we describe a simple method to isolate individual astrocytes. This method permits obtaining astrocytes with intact morphology from the adult mouse optic nerve and reduces contamination of the isolated astrocytes by other cell types. Individual astrocytes can be recognized by their morphology and collected under microscopic control. The whole procedure can be completed in 2-3 h. We also discuss downstream applications like multiplex single-cell PCR and quantitative PCR (qPCR).

The mouse brain metabolome: region-specific signatures and response to excitotoxic neuronal injury

Neurodegeneration is a multistep process characterized by a multitude of molecular entities and their interactions. Systems analyses, or omics approaches, have become an important tool in characterizing this process. Although RNA and protein profiling made their entry into this field a couple of decades ago, metabolite profiling is a more recent addition. The metabolome represents a large part or all metabolites in a tissue, and gives a snapshot of its physiology. By using gas chromatography coupled to mass spectrometry, we analyzed the metabolic profile of brain regions of the mouse, and found that each region is characterized by its own metabolic signature. We then analyzed the metabolic profile of the mouse brain after excitotoxic injury, a mechanism of neurodegeneration implicated in numerous neurological diseases. More important, we validated our findings by measuring, histologically and molecularly, actual neurodegeneration and glial response. We found that a specific global metabolic signature, best revealed by machine learning algorithms, rather than individual metabolites, was the most robust correlate of neuronal injury and the accompanying gliosis, and this signature could serve as a global biomarker for neurodegeneration. We also observed that brain lesioning induced several metabolites with neuroprotective properties. Our results deepen the understanding of metabolic changes accompanying neurodegeneration in disease models, and could help rapidly evaluate these changes in preclinical drug studies.

Evolution and genetic architecture of chromatin accessibility and function in yeast

Chromatin accessibility is an important functional genomics phenotype that influences transcription factor binding and gene expression. Genome-scale technologies allow chromatin accessibility to be mapped with high-resolution, facilitating detailed analyses into the genetic architecture and evolution of chromatin structure within and between species. We performed Formaldehyde-Assisted Isolation of Regulatory Elements sequencing (FAIRE-Seq) to map chromatin accessibility in two parental haploid yeast species, Saccharomyces cerevisiae and Saccharomyces paradoxus and their diploid hybrid. We show that although broad-scale characteristics of the chromatin landscape are well conserved between these species, accessibility is significantly different for 947 regions upstream of genes that are enriched for GO terms such as intracellular transport and protein localization exhibit. We also develop new statistical methods to investigate the genetic architecture of variation in chromatin accessibility between species, and find that cis effects are more common and of greater magnitude than trans effects. Interestingly, we find that cis and trans effects at individual genes are often negatively correlated, suggesting widespread compensatory evolution to stabilize levels of chromatin accessibility. Finally, we demonstrate that the relationship between chromatin accessibility and gene expression levels is complex, and a significant proportion of differences in chromatin accessibility might be functionally benign.

Methods for quantifying gene expression in ecoimmunology: from qPCR to RNA-Seq

Historically, the use of cutting-edge molecular techniques to study immunological gene expression and related cellular pathways has been largely limited to model organisms. Few studies have been performed that quantify the molecular immunological responses of non-model species, especially in response to environmental factors, life-history events, or exposure to parasites. This dearth of information has largely occurred due to the lack of available non-model species-specific gene sequences and immunological reagents and also due to prohibitively expensive technology. However, with the rapid development of various sequencing and transcriptomic technologies, profiling the gene expression of non-model organisms has become possible. Technologies and concepts explored here include an overview of current technologies for quantifying gene expression, including: qPCR, multiplex branched DNA assays, microarrays, and profiling gene expression (RNA sequencing [RNA-Seq]) based on next-generation sequencing. Examples of the advancement of these technologies in non-model systems are discussed. Additionally, applications, limitations, and feasibility of the use of these methodologies in non-model systems to address questions in ecological immunology and disease-ecology are specifically addressed.

The predictive power of brain mRNA mappings for in vivo protein density: a positron emission tomography correlation study

Substantial efforts are being spent on postmortem mRNA transcription mapping on the assumption that in vivo protein distribution can be predicted from such data. We tested this assumption by comparing mRNA transcription maps from the Allen Human Brain Atlas with reference protein concentration maps acquired with positron emission tomography (PET) in two representative systems of neurotransmission (opioid and serotoninergic). We found a tight correlation between mRNA expression and specific binding with 5-HT1A receptors measured with PET, but for opioid receptors, the correlation was weak. The discrepancy can be explained by differences in expression regulation between the two systems: transcriptional mechanisms dominate the regulation in the serotoninergic system, whereas in the opioid system proteins are further modulated after transcription. We conclude that mRNA information can be exploited for systems where translational mechanisms predominantly regulate expression. Where posttranscriptional mechanisms are important, mRNA data have to be interpreted with caution. The methodology developed here can be used for probing assumptions about the relationship of mRNA and protein in multiple neurotransmission systems.

Optic nerve crush induces spatial and temporal gene expression patterns in retina and optic nerve of BALB/cJ mice

Background

Central nervous system (CNS) trauma and neurodegenerative disorders trigger a cascade of cellular and molecular events resulting in neuronal apoptosis and regenerative failure. The pathogenic mechanisms and gene expression changes associated with these detrimental events can be effectively studied using a rodent optic nerve crush (ONC) model. The purpose of this study was to use a mouse ONC model to: (a) evaluate changes in retina and optic nerve (ON) gene expression, (b) identify neurodegenerative pathogenic pathways and (c) discover potential new therapeutic targets.

Results

Only 54% of total neurons survived in the ganglion cell layer (GCL) 28 days post crush. Using Bayesian Estimation of Temporal Regulation (BETR) gene expression analysis, we identified significantly altered expression of 1,723 and 2,110 genes in the retina and ON, respectively. Meta-analysis of altered gene expression (≥1.5, ≤-1.5, p < 0.05) using Partek and DAVID demonstrated 28 up and 20 down-regulated retinal gene clusters and 57 up and 41 down-regulated optic nerve clusters. Regulated gene clusters included regenerative change, synaptic plasticity, axonogenesis, neuron projection, and neuron differentiation. Expression of selected genes (Vsnl1, Syt1, Synpr and Nrn1) from retinal and ON neuronal clusters were quantitatively and qualitatively examined for their relation to axonal neurodegeneration by immunohistochemistry and qRT-PCR.

Conclusion

A number of detrimental gene expression changes occur that contribute to trauma-induced neurodegeneration after injury to ON axons. Nrn1 (synaptic plasticity gene), Synpr and Syt1 (synaptic vesicle fusion genes), and Vsnl1 (neuron differentiation associated gene) were a few of the potentially unique genes identified that were down-regulated spatially and temporally in our rodent ONC model. Bioinformatic meta-analysis identified significant tissue-specific and time-dependent gene clusters associated with regenerative changes, synaptic plasticity, axonogenesis, neuron projection, and neuron differentiation. These ONC induced neuronal loss and regenerative failure associated clusters can be extrapolated to changes occurring in other forms of CNS trauma or in clinical neurodegenerative pathological settings. In conclusion, this study identified potential therapeutic targets to address two key mechanisms of CNS trauma and neurodegeneration: neuronal loss and regenerative failure.

Introduction to sequencing the brain transcriptome

High-throughput next-generation sequencing is now entering its second decade. However, it was not until 2008 that the first report of sequencing the brain transcriptome appeared (Mortazavi, Williams, Mccue, Schaeffer, & Wold, 2008). These authors compared short-read RNA-Seq data for mouse whole brain with microarray results for the same sample and noted both the advantages and disadvantages of the RNA-Seq approach. While RNA-Seq provided exon level resolution, the majority of the reads were provided by a small proportion of highly expressed genes and the data analysis was exceedingly complex. Over the past 6 years, there have been substantial improvements in both RNA-Seq technology and data analysis. This volume contains 11 chapters that detail various aspects of sequencing the brain transcriptome. Some of the chapters are very methods driven, while others focus on the use of RNA-Seq to study such diverse areas as development, schizophrenia, and drug abuse. This chapter briefly reviews the transition from microarrays to RNA-Seq as the preferred method for analyzing the brain transcriptome. Compared with microarrays, RNA-Seq has a greater dynamic range, detects both coding and noncoding RNAs, is superior for gene network construction, detects alternative spliced transcripts, and can be used to extract genotype information, e.g., nonsynonymous coding single nucleotide polymorphisms. RNA-Seq embraces the complexity of the brain transcriptome and provides a mechanism to understand the underlying regulatory code; the potential to inform the brain-behavior-disease relationships is substantial.

The genetics of gene expression in complex mouse crosses as a tool to study the molecular underpinnings of behavior traits

Complex Mus musculus crosses provide increased resolution to examine the relationships between gene expression and behavior. While the advantages are clear, there are numerous analytical and technological concerns that arise from the increased genetic complexity that must be considered. Each of these issues is discussed, providing an initial framework for complex cross study design and planning.

Accuracy of allele frequency estimation using pooled RNA-Seq

For nonmodel organisms, genome-wide information that describes functionally relevant variation may be obtained by RNA-Seq following de novo transcriptome assembly. While sequencing has become relatively inexpensive, the preparation of a large number of sequencing libraries remains prohibitively expensive for population genetic analyses of nonmodel species. Pooling samples may be then an attractive alternative. To test whether pooled RNA-Seq accurately predicts true allele frequencies, we analysed the liver transcriptomes of 10 bank voles. Each sample was sequenced both as an individually barcoded library and as a part of a pool. Equal amounts of total RNA from each vole were pooled prior to mRNA selection and library construction. Reads were mapped onto the de novo assembled reference transcriptome. High-quality genotypes for individual voles, determined for 23,682 SNPs, provided information on 'true' allele frequencies; allele frequencies estimated from the pool were then compared with these values. 'True' frequencies and those estimated from the pool were highly correlated. Mean relative estimation error was 21% and did not depend on expression level. However, we also observed a minor effect of interindividual variation in gene expression and allele-specific gene expression influencing allele frequency estimation accuracy. Moreover, we observed strong negative relationship between minor allele frequency and relative estimation error. Our results indicate that pooled RNA-Seq exhibits accuracy comparable with pooled genome resequencing, but variation in expression level between individuals should be assessed and accounted for. This should help in taking account the difference in accuracy between conservatively expressed transcripts and these which are variable in expression level.

Combining animal personalities with transcriptomics resolves individual variation within a wild-type zebrafish population and identifies underpinning molecular differences in brain function

Resolving phenotype variation within a population in response to environmental perturbation is central to understanding biological adaptation. Relating meaningful adaptive changes at the level of the transcriptome requires the identification of processes that have a functional significance for the individual. This remains a major objective towards understanding the complex interactions between environmental demand and an individual's capacity to respond to such demands. The interpretation of such interactions and the significance of biological variation between individuals from the same or different populations remain a difficult and under-addressed question. Here, we provide evidence that variation in gene expression between individuals in a zebrafish population can be partially resolved by a priori screening for animal personality and accounts for >9% of observed variation in the brain transcriptome. Proactive and reactive individuals within a wild-type population exhibit consistent behavioural responses over time and context that relates to underlying differences in regulated gene networks and predicted protein-protein interactions. These differences can be mapped to distinct regions of the brain and provide a foundation towards understanding the coordination of underpinning adaptive molecular events within populations.

From genes to milk: genomic organization and epigenetic regulation of the mammary transcriptome

Even in genomes lacking operons, a gene's position in the genome influences its potential for expression. The mechanisms by which adjacent genes are co-expressed are still not completely understood. Using lactation and the mammary gland as a model system, we explore the hypothesis that chromatin state contributes to the co-regulation of gene neighborhoods. The mammary gland represents a unique evolutionary model, due to its recent appearance, in the context of vertebrate genomes. An understanding of how the mammary gland is regulated to produce milk is also of biomedical and agricultural importance for human lactation and dairying. Here, we integrate epigenomic and transcriptomic data to develop a comprehensive regulatory model. Neighborhoods of mammary-expressed genes were determined using expression data derived from pregnant and lactating mice and a neighborhood scoring tool, G-NEST. Regions of open and closed chromatin were identified by ChIP-Seq of histone modifications H3K36me3, H3K4me2, and H3K27me3 in the mouse mammary gland and liver tissue during lactation. We found that neighborhoods of genes in regions of uniquely active chromatin in the lactating mammary gland, compared with liver tissue, were extremely rare. Rather, genes in most neighborhoods were suppressed during lactation as reflected in their expression levels and their location in regions of silenced chromatin. Chromatin silencing was largely shared between the liver and mammary gland during lactation, and what distinguished the mammary gland was mainly a small tissue-specific repertoire of isolated, expressed genes. These findings suggest that an advantage of the neighborhood organization is in the collective repression of groups of genes via a shared mechanism of chromatin repression. Genes essential to the mammary gland's uniqueness are isolated from neighbors, and likely have less tolerance for variation in expression, properties they share with genes responsible for an organism's survival.

Specialization of gene expression during mouse brain development

The transcriptome of the brain changes during development, reflecting processes that determine functional specialization of brain regions. We analyzed gene expression, measured using in situ hybridization across the full developing mouse brain, to quantify functional specialization of brain regions. Surprisingly, we found that during the time that the brain becomes anatomically regionalized in early development, transcription specialization actually decreases reaching a low, “neurotypic”, point around birth. This decrease of specialization is brain-wide, and mainly due to biological processes involved in constructing brain circuitry. Regional specialization rises again during post-natal development. This effect is largely due to specialization of plasticity and neural activity processes. Post-natal specialization is particularly significant in the cerebellum, whose expression signature becomes increasingly different from other brain regions. When comparing mouse and human expression patterns, the cerebellar post-natal specialization is also observed in human, but the regionalization of expression in the human Thalamus and Cortex follows a strikingly different profile than in mouse.

Brain development is one of the most complex biological processes, orchestrated by the precisely timed and coordinated expression of thousands of genes. As the brain develops, specific regions are formed, their structure and function reflected in unique sets of expressed genes. Regional gene expression profiles determine the basic properties of neural systems: controlling how the brain develops from embryos to adults, maintaining the well being of the system, adapting the brain following experience and carrying out specific regional functions. Here we investigate the temporal dynamics of changes in regional gene expression patterns throughout mouse brain development. We identify a neurotypic phase around the time of birth, in which patterns of gene expression become more homogeneous across the brain, creating an ‘hourglass’ shaped expression divergence profile. We characterize the biological processes, genes and brain regions responsible for this pattern, and also compare mouse neurodevelopmental expression patterns with parallel data from human, finding striking similarities and differences between the two species.

Nonparametric Bayesian evaluation of differential protein quantification

Arbitrary cutoffs are ubiquitous in quantitative computational proteomics: maximum acceptable MS/MS PSM or peptide q value, minimum ion intensity to calculate a fold change, the minimum number of peptides that must be available to trust the estimated protein fold change (or the minimum number of PSMs that must be available to trust the estimated peptide fold change), and the "significant" fold change cutoff. Here we introduce a novel experimental setup and nonparametric Bayesian algorithm for determining the statistical quality of a proposed differential set of proteins or peptides. By comparing putatively nonchanging case-control evidence to an empirical null distribution derived from a control-control experiment, we successfully avoid some of these common parameters. We then apply our method to evaluating different fold-change rules and find that for our data a 1.2-fold change is the most permissive of the plausible fold-change rules.

On the reproducibility of science: unique identification of research resources in the biomedical literature

Scientific reproducibility has been at the forefront of many news stories and there exist numerous initiatives to help address this problem. We posit that a contributor is simply a lack of specificity that is required to enable adequate research reproducibility. In particular, the inability to uniquely identify research resources, such as antibodies and model organisms, makes it difficult or impossible to reproduce experiments even where the science is otherwise sound. In order to better understand the magnitude of this problem, we designed an experiment to ascertain the “identifiability” of research resources in the biomedical literature. We evaluated recent journal articles in the fields of Neuroscience, Developmental Biology, Immunology, Cell and Molecular Biology and General Biology, selected randomly based on a diversity of impact factors for the journals, publishers, and experimental method reporting guidelines. We attempted to uniquely identify model organisms (mouse, rat, zebrafish, worm, fly and yeast), antibodies, knockdown reagents (morpholinos or RNAi), constructs, and cell lines. Specific criteria were developed to determine if a resource was uniquely identifiable, and included examining relevant repositories (such as model organism databases, and the Antibody Registry), as well as vendor sites. The results of this experiment show that 54% of resources are not uniquely identifiable in publications, regardless of domain, journal impact factor, or reporting requirements. For example, in many cases the organism strain in which the experiment was performed or antibody that was used could not be identified. Our results show that identifiability is a serious problem for reproducibility. Based on these results, we provide recommendations to authors, reviewers, journal editors, vendors, and publishers. Scientific efficiency and reproducibility depend upon a research-wide improvement of this substantial problem in science today.