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12.6: Regulation of Gene Expression (Exercises) - Biology

12.6: Regulation of Gene Expression (Exercises) - Biology


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12.1 List all the mechanisms that can be used to regulate gene expression in eukaryotes.

12.2 With respect to the expression of β-galactosidase, what would be the phenotype of each of the following strains of E. coli?

a) I+, O+, Z+, Y+(no glucose, no lactose)

b) I+, O+, Z+, Y+ (no glucose, high lactose)

c) I+, O+, Z+, Y+ (high glucose, no lactose)

d) I+, O+, Z+, Y+(high glucose, high lactose)

e) I+, O+, Z-, Y+(no glucose, no lactose)

f) I+, O+, Z-, Y+ (high glucose, high lactose)

g) I+, O+, Z+, Y- (high glucose, high lactose)

h) I+, Oc, Z+, Y+ (no glucose, no lactose)

i) I+, Oc,Z+, Y+ (no glucose, high lactose)

j) I+, Oc, Z+, Y+ (high glucose, no lactose)

k) I+, Oc, Z+, Y+ (high glucose, high lactose)

l) I-, O+, Z+, Y+ (no glucose, no lactose)

m) I-, O+, Z+, Y+ (no glucose, high lactose)

n) I-, O+, Z+, Y+ (high glucose, no lactose)

o) I-, O+, Z+, Y+ (high glucose, high lactose)

p) Is, O+, Z+, Y+ (no glucose, no lactose)

q) Is, O+, Z+, Y+ (no glucose, high lactose)

r) Is, O+, Z+, Y+ (high glucose, no lactose)

s) Is, O+, Z+, Y+ (high glucose, high lactose)

12.3 In the E. coli strains listed below, some genes are present on both the chromosome, and the extrachromosomal F-factor episome. The genotypes of the chromosome and episome are separated by a slash. What will be the β-galactosidase phenotype of these strains? All of the strains are grown in media that lacks glucose.

a) I+, O+, Z+, Y+ / O-, Z-, Y- (high lactose)

b) I+, O+, Z+, Y+ / O-, Z-, Y- (no lactose)

c) I+, O+, Z-, Y+ / O-, Z+, Y+ (high lactose)

d) I+, O+, Z-, Y+ / O-, Z+, Y+ (no lactose)

e) I+, O+, Z-, Y+ / I-, O+, Z+, Y+ (high lactose)

f) I+, O+, Z-, Y+ / I-, O+, Z+, Y+ (no lactose)

g) I-, O+, Z+, Y+ / I+, O+, Z-, Y+ (high lactose)

h) I-, O+, Z+, Y+ / I+, O+, Z-, Y+ (no lactose)

i) I+, Oc, Z+, Y+ / I+, O+, Z-, Y+ (high lactose)

j) I+, Oc, Z+, Y+ / I+, O+, Z-, Y+ (no lactose)

k) I+, O+, Z-, Y+ / I+, Oc, Z+, Y+ (high lactose)

l) I+, O+, Z-, Y+ / I+, Oc, Z+, Y+ (no lactose)

m) I+, O+, Z-, Y+ / Is, O+, Z+, Y+ (high lactose)

n) I+, O+, Z-, Y+ / Is, O+, Z+, Y+ (no lactose)

o) Is, O+, Z+, Y+ / I+, O+, Z-, Y+ (high lactose)

p) Is, O+, Z+, Y+ / I+, O+, Z-, Y+ (no lactose)

12.1 Transcriptional: initiation, processing & splicing, degradation

Translational: initiation, processing, degradation

Post-translational: modifications (e.g. phosphorylation), localization

Others: histone modification, other chromatin remodeling, DNA methylation

12.2 Legend:

+++ Lots of β-galactosidase activity

+ Moderate β-galactosidase activity

-- No β-galactosidase activity

-- a) I+, O+, Z+, Y+(no glucose, no lactose)

+++ b) I+, O+, Z+, Y+ (no glucose, high lactose)

-- c) I+, O+, Z+, Y+ (high glucose, no lactose)

+ d) I+, O+, Z+, Y+(high glucose, high lactose)

-- e) I+, O+, Z-, Y+(no glucose, no lactose)

-- f) I+, O+, Z-, Y+ (high glucose, high lactose)

+ g) I+, O+, Z+, Y- (high glucose, high lactose)

+++ h) I+, Oc, Z+, Y+ (no glucose, no lactose)

+++ i) I+, Oc,Z+, Y+ (no glucose, high lactose)

+ j) I+, Oc, Z+, Y+ (high glucose, no lactose)

+ k) I+, Oc, Z+, Y+ (high glucose, high lactose)

+++ l) I-, O+, Z+, Y+ (no glucose, no lactose)

+++ m) I-, O+, Z+, Y+ (no glucose, high lactose)

+ n) I-, O+, Z+, Y+ (high glucose, no lactose)

+ o) I-, O+, Z+, Y+ (high glucose, high lactose)

-- p) Is, O+, Z+, Y+ (no glucose, no lactose)

-- q) Is, O+, Z+, Y+ (no glucose, high lactose)

-- r) Is, O+, Z+, Y+ (high glucose, no lactose)

-- s) Is, O+, Z+, Y+ (high glucose, high lactose)

12.3 Legend:

+++ Lots of β-galactosidase activity

+ Moderate β-galactosidase activity

-- No β-galactosidase activity

+++ a) I+, O+, Z+, Y+ / O-, Z-, Y- (high lactose)

-- b) I+, O+, Z+, Y+ / O-, Z-, Y- (no lactose)

+++ c) I+, O+, Z-, Y+ / O-, Z+, Y+ (high lactose)

+ d) I+, O+, Z-, Y+ / O-, Z+, Y+ (no lactose)

+++ e) I+, O+, Z-, Y+ / I-, O+, Z+, Y+ (high lactose)

-- f) I+, O+, Z-, Y+ / I-, O+, Z+, Y+ (no lactose)

+++ g) I-, O+, Z+, Y+ / I+, O+, Z-, Y+ (high lactose)

-- h) I-, O+, Z+, Y+ / I+, O+, Z-, Y+ (no lactose)

+++ i) I+, Oc, Z+, Y+ / I+, O+, Z-, Y+ (high lactose)

+++ j) I+, Oc, Z+, Y+ / I+, O+, Z-, Y+ (no lactose)

+++ k) I+, O+, Z-, Y+ / I+, Oc, Z+, Y+ (high lactose)

+++ l) I+, O+, Z-, Y+ / I+, Oc, Z+, Y+ (no lactose)

-- m) I+, O+, Z-, Y+ / Is, O+, Z+, Y+ (high lactose)

-- n) I+, O+, Z-, Y+ / Is, O+, Z+, Y+ (no lactose)

-- o) Is, O+, Z+, Y+ / I+, O+, Z-, Y+ (high lactose)

-- p) Is, O+, Z+, Y+ / I+, O+, Z-, Y+ (no lactose)

12.4 You could demonstrate this with just I+OcZ-/I+O+Z+. The fact that this does not have constitutive lactose expression shows that the operator only acts on the same piece of DNA on which it is located. There are also other possible answers.

12.5 You could also demonstrate this with just I+O+Z-/I-O+Z+. The fact that this has the same lactose-inducible phenotype as wild-type hows that a functional lacI gene can act on operators on both the same piece of DNA from which it is transcribed, or on a different piece of DNA. There are also other possible answers.

12.6 For all of these, the answer is the same: The lacoperon would be inducible by lactose, but only moderate expression of the lac operon would be possible, even in the absence of glucose

a) loss-of-function of adenylate cyclase

b) loss of DNA binding ability of CAP

c) loss of cAMP binding ability of CAP

d) mutation of CAP binding site (CBS) cis-element so that CAP could not bind

12.7 Both involve trans-factors binding to corresponding cis-elements to regulate the initiation of transcription by recruiting or stabilizing the binding of RNApol and related transcriptional proteins at the promoter. In prokaryotes, genes may be regulated as a single operon. In eukaryotes, enhancers may be located much further from the promoter than in prokaryotes.

12.8 These fish would all have spiny tales like the deep-water population.

12.9 These could have arisen from loss-of-function mutation in FLC, or in the cis-element to which FLC normally binds.

12.10 If there was no deacetylation of FLC by HDAC, transcription of FLC might continue constantly, leading to constant suppression of flowering, even after winter.


12.6 Disorders of the Muscular System

Figure 12.6.1 Devices can be a pain in the neck – literally.

Spending hours each day looking down at hand-held devices is a pain in the neck — literally. The weight of the head bending forward can put a lot of strain on neck muscles, and muscle injuries can be very painful. Neck pain is one of the most common of all complaints that bring people to the doctor’s office. In any given year, about one in five adults will suffer from neck pain. That’s a lot of pains in the neck! Not all of them are due to muscular disorders, but many of them are. Muscular disorders, in turn, generally fall into two general categories: musculoskeletal disorders and neuromuscular disorders .


Aging-associated up-regulation of neuronal 5-lipoxygenase expression: putative role in neuronal vulnerability

Aging is associated with neurodegenerative processes. 5-Lipoxygenase (5-LO), which is also expressed in neurons, is the key enzyme in the synthesis of leukotrienes, inflammatory eicosanoids that are capable of promoting neurodegeneration. We hypothesized that neuronal 5-LO expression can be up-regulated in aging and that this may increase the brain's vulnerability to neurodegeneration. We observed differences in the distribution of 5-LO-like immunoreactivity in various brain areas of adult young (2-month-old) vs. old (24-month-old) male rats. Greater 5-LO-like immunoreactivity was found in old vs. young rats, in particular in the dendrites of pyramidal neurons in limbic structures, including the hippocampus, and in layer V pyramidal cells of the frontoparietal cortex and their apical dendrites. The aging-increased expression of neuronal 5-LO protein appears to be due to increased 5-LO gene expression. Using a quantitative reverse transcription/polymer-ase chain reaction assay and 5-LO-specific oligonucleotide primers and their mutated internal standards, we observed about a 2.5-fold greater hippocampal 5-LO mRNA content in old rats. 5-LO-like immunoreactivity was also observed in small, nonpyramidal cells, which were positive for glutamic acid decarboxylase or glial fibrillary acid protein. This type of 5-LO immunostaining did not increase in the old rats. Hippocampal excitotoxic injury induced by systemic injection of kainate was greater in old rats. Neuroprotection was observed with the 5-LO inhibitor, caffeic acid. Together, these results suggest that aging increases both neuronal 5-LO expression and neuronal vulnerability to 5-LO inhibitor-sensitive ex-citotoxicity, and indicate that the 5-LO system might play a significant role in the pathobiology of aging-associated neurodegenerative diseases.—Uz, T., Pesold, C., Longone, P., Manev, H. Aging-associated up-regulation of neuronal 5-lipoxygenase expression: putative role in neuronal vulnerability. FASEB J. 12, 439–449 (1998)


INTRODUCTION

Floral scent is a key modulating factor in plant–insect interactions and plays a central role in successful pollination, and thus in fruit development, of many crop species. Flower fragrances vary widely among species in terms of the number, identity, and relative amounts of constituent volatile compounds ( Knudsen and Tollsten, 1993 Knudsen et al., 1993). Closely related plant species, which rely on different insects for pollination, produce different odors ( Henderson, 1986 Raguso and Pichersky, 1995). Often, characteristic floral odors are correlated with the type of pollinators. Species pollinated by bees and flies tend to have scents that are defined (by humans) as sweet, whereas those pollinated by beetles have musty, spicy, or fruity odors ( Dobson, 1994).

Many volatile components of flowers have been identified however, the mechanism of flower fragrance formation is not well understood. Recent investigations of floral scent production in Clarkia breweri are the first examples of the isolation of enzymes and genes responsible for the biosynthesis of scent volatiles. The enzymes S-linalool synthase, S-adenosyl- l -methionine (SAM):(iso)eugenol O-methyl transferase, acetyl-CoA:benzyl alcohol acetyltransferase, and SAM:salicylic acid carboxyl methyl transferase, which catalyze the formation of linalool, methyl(iso)eugenol, benzylacetate, and methyl salicylate, respectively, and their corresponding genes have been isolated and characterized (Pichersky et al., 1994, 1995 Dudareva et al., 1996, 1998a, 1998b Wang et al., 1997 Wang and Pichersky, 1998 Ross et al., 1999 reviewed in Dudareva and Pichersky, 2000). It has been shown that in C. breweri, flowers synthesize their scent compounds de novo in the tissues from which they are emitted, and the emission levels, corresponding enzyme activities, and amounts of mRNA are all spatially and temporally correlated. In general, the expression of these genes is greatest in petals just before anthesis and is restricted to the epidermal cell layer of floral tissues.

Although production of volatile scent compounds appears to be widespread in the plant kingdom, information about their de novo biosynthesis (as distinct from their possible release from glucosides see Oka et al., 1999) and about regulation of the genes involved is limited and based to date on analysis of a single model system, moth-pollinated C. breweri. Whether similar molecular mechanisms are involved in regulation of floral scent production in other plant species is currently unclear. We have begun to address this question by studying bee-pollinated snapdragon flowers (Scrophulariaceae). The snapdragon model has several important advantages over the C. breweri system: a well-developed genetic map ( Stubbe, 1966), a transposon gene cloning system ( Martin et al., 1990), an available transformation protocol ( Heidmann et al., 1998), and rhythmic emission (see below). Several genes encoding flower pigment biosynthetic enzymes and genes controlling flower development have been isolated from snapdragon ( Coen et al., 1986 Sommer and Saedler, 1986 Coen and Meyerowitz, 1991 Irish and Yamamoto, 1995), but no information about enzymes and genes involved in the synthesis of flower scent compounds has been published.

In this study, we present a detailed analysis of the production of a volatile ester, methyl benzoate, in snapdragon flowers. We show that methyl benzoate is produced by enzymatic methylation of benzoic acid in the reaction catalyzed by SAM:benzoic acid carboxyl methyl transferase (BAMT). During the life span of the flower, the levels of methyl benzoate emission, BAMT activity, BAMT gene expression, the BAMT protein, and benzoic acid are developmentally and differentially regulated. Our results provide evidence that production of methyl benzoate is regulated by the amount of benzoic acid and the amount of the BAMT protein, which in turn is regulated at the transcriptional level. Overall, the data suggest that similar molecular mechanisms may be involved in the regulation of floral scent production in different plant species.


12.6: Regulation of Gene Expression (Exercises) - Biology

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Feature Papers represent the most advanced research with significant potential for high impact in the field. Feature Papers are submitted upon individual invitation or recommendation by the scientific editors and undergo peer review prior to publication.

The Feature Paper can be either an original research article, a substantial novel research study that often involves several techniques or approaches, or a comprehensive review paper with concise and precise updates on the latest progress in the field that systematically reviews the most exciting advances in scientific literature. This type of paper provides an outlook on future directions of research or possible applications.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to authors, or important in this field. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.


Conclusions

Using Solexa high-throughout sequencing technology, we sequenced the small RNA population of rice pollen at three sequential developmental stages from microspores to tricellular pollen, with sporophytic tissues-roots, leaves, and callus cells-as controls. We obtained millions of high-quality readouts from each sample and identified 292 kn-miRs and 75 nov-miRs. The miRNA composition and expression pattern of developing pollen were obviously different from those of sporophytes, with more nov-miRs enriched/specifically expressed in pollen while more kn-miRs were enriched/specifically expressed in sporophytes. Principal component analysis revealed that pollen could be differentiated from sporophytes with regard to miRNA expression profiles, with novel and non-conserved known miRNAs the main contributors to this. Furthermore, 1,068 targets were predicted for 292 known miRNAs although no obvious differences were found by GO abundance analysis of those targets between pollen-enriched and sporophyte-enriched kn-miRs, correlation of expression profiles of pollen-enriched kn-miRs with their targets significantly differs from that of sporophyte-enriched kn-miRs with their corresponding targets in terms of transcription, hormone signaling and chromatin remodeling. We identified 285 targets for the 75 nov-miRs, which appeared to be negatively regulated. GO terms for chromatin assembly and disassembly were statistically significantly associated with the targets of BCP-expressed nov-miRs, implying that BCP would be the key point of miRNA regulation. Our data reveal for the first time comprehensive and dynamic features of miRNAs in developing pollen.


12.6: Regulation of Gene Expression (Exercises) - Biology

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited.

Feature Papers represent the most advanced research with significant potential for high impact in the field. Feature Papers are submitted upon individual invitation or recommendation by the scientific editors and undergo peer review prior to publication.

The Feature Paper can be either an original research article, a substantial novel research study that often involves several techniques or approaches, or a comprehensive review paper with concise and precise updates on the latest progress in the field that systematically reviews the most exciting advances in scientific literature. This type of paper provides an outlook on future directions of research or possible applications.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to authors, or important in this field. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.


Biochemistry and Molecular Biology of Plants, 2nd Edition

Biochemistry and Molecular Biology of Plants, 2nd Edition has been hailed as a major contribution to the plant sciences literature and critical acclaim has been matched by global sales success. Maintaining the scope and focus of the first edition, the second will provide a major update, include much new material and reorganise some chapters to further improve the presentation.

This book is meticulously organised and richly illustrated, having over 1,000 full-colour illustrations and 500 photographs. It is divided into five parts covering: Compartments, Cell Reproduction, Energy Flow, Metabolic and Developmental Integration, and Plant Environment and Agriculture. Specific changes to this edition include:

  • Completely revised with over half of the chapters having a major rewrite.
  • Includes two new chapters on signal transduction and responses to pathogens.
  • Restructuring of section on cell reproduction for improved presentation.
  • Dedicated website to include all illustrative material.

Biochemistry and Molecular Biology of Plants holds a unique place in the plant sciences literature as it provides the only comprehensive, authoritative, integrated single volume book in this essential field of study.


Mental Health Benefits of Physical Exercise

Numerous studies suggest that regular aerobic exercise works as well as pharmaceutical antidepressants in treating mild-to-moderate depression . A possible reason for this effect is that exercise increases the biosynthesis of at least three neurochemicals that may act as euphoriants . The euphoric effect of exercise is well known. Distance runners may refer to it as “runner’s high,” and people who participate in crew (as in Figure 12.5.5) may refer to it as “rower’s high.” Because of these effects, health care providers often promote the use of aerobic exercise as a treatment for depression.

Figure 12.5.5 These rowing duos are competing in the 2016 Summer Olympics in Rio, in which Canada won a silver medal. They are clearly exerting themselves — and no doubt increasing their euphoriant neurochemicals in the process.

Additional mental health benefits of physical exercise include reducing stress, improving body image, and promoting positive self-esteem. Conversely, there is evidence to suggest that being sedentary is associated with increased risk of anxiety.


Results

FKBP1b overexpression improved spatial reference and reversal memory for both LT and ST aged groups (Fig. 2)

Both LT and ST FKBP1b overexpression countered age-related decline in spatial memory. A, Reference memory probe. FKBP1b treatments countered age-related deficits in reference memory probe performance. B, Reversal memory probe. FKBP1b treatments countered age-related deficits in the reversal memory probe trial. C, Cued testing. With visual cues prominently highlighting location of the escape platform, no group differences were found, indicating that memory test results were not due to differences in locomotor and/or visual abilities. *p ≤ 0.05 **p ≤ 0.01 ***p ≤ 0.001 ****p ≤ 0.0001 significant pairwise contrast versus AC.

A total of 52 rats in four groups completed our spatial memory testing protocol in the MWM (10 YC, 13 AC, 13 ST, and 16 LT rats). For the 3 groups of aged AAV-treated rats, behavioral testing began at 21 months of age, either 8 (LT) or 2 (ST) months after AAV injection. Training in the MWM reference memory task was performed over 4 d, with 3 training trials per day. Over the 4 d, all groups showed acquisition of the task as indicated by a distinct decline in latency and path-length to find the platform (F(11,528) = 11.58 p = 1.80e-10 F(11,528) = 7.41, p = 7.20e-12, respectively, repeated-measures ANOVA). Although there was a strong trend for young animals to outperform ACs during training, no significant effects of treatment were seen in latency and path length measures over this 4 d training phase, similar to results in Gant et al. (2015).

On the fifth day of the task, the platform was removed and recall of the platform location was probed with a single retention trial (Fig. 2A reference memory probe). There was a main effect of treatment group for both latency (F(3,48) = 3.57, p = 0.021, ANOVA) and path length (F(3,48) = 2.90, p = 0.044, ANOVA). As reported in multiple studies, the AC group exhibited significantly longer path lengths and higher latencies to find the platform compared with YC rats. In contrast, neither FKBP1b-treated aged group differed from YCs and both showed significantly reduced latency compared with ACs (vs ACs: LT, p = 0.05, ST, p = 0.05 YCs, p = 0.002, pLSD). The path length to platform results were highly similar to latency, although the differences between the FKBP1b groups and the ACs were only of borderline significance (vs ACs: LT, p = 0.078 ST, p = 0.08, YCs, p = 0.006, pLSD).

Training on the reversal memory task was conducted on the eighth day after 2 d of rest: The location of the platform was changed and the rats were given 3 trials in 1 d to learn the new location. Over the three reversal training trials, there were no significant differences in latency or path length among any of the groups, nor was there significant improvement (data not shown). On the ninth day, the platform was again removed and rats were tested for their retention of the new platform location on a single retention trial (Fig. 2B, reversal memory probe). There were substantial main effects of treatment on latency to platform (F(3,48) = 9.57, p = 0.000046, ANOVA) and path length to platform location (F(3,48) = 7.97, p = 0.0002, ANOVA). AC animals again exhibited highly significant deficits in path length and latency compared with YCs, but both ST and LT groups exhibited path length and latency scores that were highly similar to those of YCs and significantly reduced compared with AC animals (latency AC vs: LT, p < 0.0001, ST, p < 0.0001 YC, p < 0.0001 path length, AC vs: LT, p = 0.0001, ST, p = 0.0001 YC, p = 0.0008, pLSD Fig. 2B). There was also a main effect of treatment group for platform crossings during the reversal retention test (F(3,48) = 7.37, p = 0.0004 ANOVA), with the YC, ST, and LT groups showing significantly greater platform crossings compared with ACs (ACs vs: YCs, p < 0.0001 ST p < 0.025 LT, p < 0.01, pLSD).

On the 10 th day of the protocol, a cued retention test was given with visual cues highlighting the platform's location. All groups found the platform rapidly and no significant group differences were present in latency, path length, or swim speed in locating the platform (Fig. 2C, cued trial), indicating that aging-related changes in locomotor and visual acuity did not account for the differences in memory performance.

The ST and LT groups were statistically indistinguishable from each other on all latency and path length measures in the behavioral testing protocols. These results suggest that the reversal by ST and prevention by LT of aging-dependent memory impairment may be mediated by similar cellular mechanisms despite the differences in the duration of exposure.

AAV-FKBP1b injection increased hippocampal FKBP1b protein and gene expression, particularly in the LT group (Fig. 3)

Hippocampal FKBP1b mRNA and protein levels were increased substantially by LT AAV-FKBP1b overexpression. Top, qRT-PCR quantification of hippocampal FKBP1b mRNA expression (FKBP1b/Gapdh) for each treatment group (one-way ANOVA on ranks, p = 0.000050 for pairwise contrast vs AC *p ≤ 0.05 ***p ≤ 0.001). Bottom, Immunostaining for hippocampal FKBP1b expression. Representative photomicrographs are shown from YC (A), AC (B), aged ST FKBP1b (C), and aged LT FKBP1b (D). Note the substantial increase in FKBP1b expression at both the mRNA and protein levels, particularly in the LT-FKBP1b group. sp, Stratum pyramidale DG, dentate gyrus. Scale bar, 500 μ m .

Because the Affymetrix Rat Gene 1.0 ST microarray used in the present study does not include the probe set for FKBP1b, we used qRT-PCR to evaluate the effectiveness of AAV-FKBP1b injection for inducing FKBP1b expression in hippocampus. FKBP1b/Gapdh expression is plotted as a function of treatment group in Figure 3. There was a highly significant increase in FKBP1b expression (F(3,47) = 18.449, p = 0.000050, ANOVA on ranks, Kruskal–Wallis). By pairwise contrast (Fisher's LSD on ranks), the increased expression was significant in ST (p = 0.012) and highly significant in LT (p < 0.001). IHC in representative animals indicated that hippocampal FKBP1b protein upregulation paralleled FKBP1b mRNA increases in AAV-FKBP1b-treated rats and was particularly intense in LT animals (Fig. 3, bottom).

In contrast to our prior findings (Blalock et al., 2004 Kadish et al., 2009 Gant et al., 2015), we did not observe differences in endogenous FKBP1b expression between the AC and YC groups (Fig. 3, top). This may be due to differences between studies in tissue dissection. In the present study, we collected tissue from whole dorsal hippocampus, whereas in prior work, we measured expression primarily in the CA1 region (Blalock et al., 2004 Kadish et al., 2009 Gant et al., 2015). Therefore, the aging effect on FKBP1b in CA1 might have been obscured in the present work because of dilution from less age-sensitive hippocampal regions. Further studies will be needed to fully elucidate the topographic distribution of aging changes in FKBP1b protein and gene expression, as well as the role of potential functional changes (Lehnart et al., 2008).

Transcriptional profiling (Fig. 4)

Microarray analysis flowchart. Shown are the effects of aging and FKBP1b on hippocampal gene transcription. Left, Total gene probe sets (29,218) were filtered to remove absent (low signal intensity) and incompletely annotated probe sets. The remaining genes (14,828) were tested by ANOVA (p ≤ 0.05) followed by pairwise comparison (Fisher's pLSD, ≤ 0.05 between YC and AC) to define aging-dependent genes. Right, Statistical template algorithm. Aging-dependent genes were categorized based on whether FKBP1b had no effect (templates I and III) or significantly countered aging's effect (templates II and IV). A total of 99.8% of aging-dependent genes were assigned to a template based on criteria described in the text. A Monte Carlo simulation (1000 iterations, see Results) was used to estimate the number of genes expected in each template by chance. The number of genes assigned to each template in the observed data was significantly greater than the number expected by chance (p ≤ 0.0001 binomial test >11-fold increase for all templates). See also Figure 4-1.

Figure 4-1

To identify genes with expression that paralleled aging and FKBP1b's cognitive effects in the same animals, we first distinguished genes that changed expression with aging (i.e., differed between ACs and YCs, the aging effect). We then identified those genes among the aging-dependent genes that were also altered by FKBP1b overexpression (i.e., differed between LT/ST FKBP1b and AC, the FKBP1b effect). RNA from six subjects per treatment group was prepared and hybridized to Affymetrix Rat Gene 1.0 arrays. Of the ∼30,000 probe sets on the array, we filtered to retain 14,828 annotated, present genes (see Materials and Methods) for statistical analysis. One-way ANOVA (p ≤ 0.05) showed that 24% (3502) differed significantly across the four groups, yielding an FDR of 0.12 (see Materials and Methods). An FDR of 0.12 is quite low for a microarray study of brain aging and provides considerable confidence in these results. Reliability in microarray studies is also strengthened when functional categories are overrepresented by coregulated genes (Blalock et al., 2005 Galvin and Ginsberg, 2005 Ginsberg and Mirnics, 2006).

Among ANOVA-significant genes (3502), 2342 (67%) also differed significantly in pairwise contrast (Fisher's protected LSD p ≤ 0.05) between the YC and AC groups (aging-dependent genes, “the aging effect”). Among these, the expression levels of 37% (876/2342) genes were also altered by FKBP1b overexpression (517 by LT, 193 by ST, 166 by both ST and LT, “the FKBP1b effect”) and were defined as aging and FKBP1b-sensitive genes (for a complete list of aging- and FKBP1b-sensitive genes, see Fig. 4-1). Many more of these were altered by both ST and LT (166) than would be expected by chance if ST and LT treatments acted through independent mechanisms (p = 4.8E-9, binomial test). These results suggest that LT and ST FKBP1b treatments exerted similar transcriptional effects. To determine whether this agreement between ST and LT was limited primarily to the 166 genes in the overlap or instead reflected widespread similarity among most FKBP1b-sensitive genes, we tested the correlation between LT and ST effects across all 876 FKBP1b-sensitive genes. A highly significant proportion of FKBP1b-sensitive genes (822/876 93.8%) were changed in the same direction by both ST and LT (p ≤ 1E-12, binomial test). Further, the effect sizes of ST and LT treatment genes (expressed as log2-fold change vs AC) were strongly correlated (r = 0.85, p = 1.3E-24 Pearson's test, data not shown). These results indicate that ST and LT FKBP1b treatments influenced gene expression similarly. The ST and LT groups also performed nearly identically on all behavioral measures. Based on these similarities and because the genome ontology functional category analysis (see below) provides greater statistical confidence in overrepresentation of a given category with increasing numbers of genes assigned to that functional category, we combined the ST and LT lists of FKBP1b-sensitive genes into a single list, such that a gene was considered an FKBP1b-sensitive gene if it differed from ACs with ST and/or LT treatment.

Remarkably, only four of the 876 aging-dependent and FKBP1b-sensitive genes (Eif3g, Pla2g7, S100β, and Snapc2) exhibited exacerbation of aging effects by FKBP1b, whereas the other 872 changed in opposite directions with aging and FKBP1b treatment. Accordingly, the anomalous four genes were excluded from functional category analyses and the remaining aging-dependent genes (2338) were parsed into one of four gene expression templates (Fig. 4, right, templates I–IV), which reflected direction of a gene's expression change with aging (up or down) and whether the gene did (Fig. 4, templates II and IV), or did not (Fig. 4, templates I and III) show the counteracting effect of FKBP1b treatment (LT or ST vs AC p ≤ 0.05, Fisher's post hoc LSD).

To determine whether the number of genes identified in each template was greater than expected by chance, we ran a Monte Carlo simulation using the same statistical analysis and template assignment strategy, but with randomly generated numbers substituted for signal intensity values (see Materials and Methods). The numbers of genes actually observed for each reported pattern exceeded by >11-fold the number expected by chance based on the simulation (Fig. 4 ≤ 0.00001, binomial test for each pattern), indicating a strong biological effect.

GO functional categories associated with genes matching each of the four template patterns (Fig. 4) of aging- and FKBP1b-sensitive genes (Table 1)

Functional categories overrepresented by genes assigned to the four expression templates reflecting aging ± FKBP1b sensitivity


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