14.4: Why It Matters- Gene Expression - Biology

14.4: Why It Matters- Gene Expression - Biology

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Why explain the regulation of gene expression?

Cancer is one of the top ten causes of death in the United States. Mutations can also alter the growth rate or the progression of the cell through the cell cycle.

Thus, cancer can be described as a disease of altered gene expression. Changes at every level of eukaryotic gene expression can be detected in some form of cancer at some point in time. In order to understand how changes to gene expression can cause cancer, it is critical to understand how each stage of gene regulation works in normal cells. By understanding the mechanisms of control in normal, non-diseased cells, it will be easier for scientists to understand what goes wrong in disease states including complex ones like cancer.

Systems metabolic engineering strategies for the production of amino acids

Systems metabolic engineering is a multidisciplinary area that integrates systems biology, synthetic biology and evolutionary engineering. It is an efficient approach for strain improvement and process optimization, and has been successfully applied in the microbial production of various chemicals including amino acids. In this review, systems metabolic engineering strategies including pathway-focused approaches, systems biology-based approaches, evolutionary approaches and their applications in two major amino acid producing microorganisms: Corynebacterium glutamicum and Escherichia coli, are summarized.


Plant hormones are considered as major determinants of plant’s overall growth and development. Multiple plant hormones such as auxin, cytokinin (CK), gibberellins (GA) and brassinosteroids (BR) have been shown to exert key functions in regulating various developmental processes such as, seed and fruit development, shoot and root architecture [1]. With the advancement in forward and reverse genetics, there is now a good understanding of how these hormones are perceived and the key players identified in their signalling pathways. For this purpose, plant hormones and their signalling transduction networks have been widely studied and employed to improve sustainable agriculture such as stem elongation, flowering time and processes like nitrogen use efficiency [2]. Interestingly, these effects on growth regulation are controlled by interacting signalling pathways among plant hormones. This interaction is either antagonistic, synergistic or occurs in parallel [3]. For example, processes such as seed germination, shoot and root growth and grain-filling are governed by antagonistic relationship between abscisic acid (ABA) and ethylene (ET) in maize [4]. Moreover, auxin and cytokinin exhibit antagonism during formation of root apical meristem but act in synergy during shoot apical meristem formation [5].

Melatonin (MT) is an indolic molecule ubiquitously present in all living organisms. Melatonin in plants is associated with growth and development, such as leaf and root organogenesis, senescence and flowering [6,7,8,9]. Moreover, melatonin also mitigates a variety of abiotic and biotic stresses in plants such as cold, drought, heat and infections by the fungal pathogen Diplocarpon mali, biotrophic and hemibiotrophic bacteria Xanthomonas oryzae and Pseudomonas syringae DC3000, respectively [10,11,12,13,14,15]. To date, melatonin is considered as a growth-regulating secondary metabolite in plants but recent studies suggest that it also has the potential to be a plant hormone [16]. In order to be considered as potential plant hormone, there are certain fundamental characteristics that a candidate molecule needs to exhibit. These include knowledge about biosynthetic pathway, receptor and physiological effects. Studies on biosynthetic pathway of melatonin in plants have made considerable progress. These show tryptophan (Trp) as the precursor followed by four sequential reactions with enzymes Tryptophan decarboxylase (TDC), Tryptophan-5-hydroxylase (T5H), Serotonin-N-acetyltransferase (SNAT) and Acetyl serotonin methyltransferase (ASMT) [17, 18]. This has been proposed to be the standard biosynthetic route in plants such as Arabidopsis thaliana and Rice (Zea mays) under normal growth conditions, however, an alternate pathway has also been proposed to exist under conditions such as senescence in which the key enzymes SNAT and ASMT switch in their order to produce melatonin. This has also been proposed to be the most prevailing biosynthetic route in Arabidopsis thaliana and Rice (Zea mays) as compared to the classic route [19]. Recently, a reverse biosynthetic reaction involving an enzyme N-acetylserotonin deacetylase (ASDAC) has been identified in Arabidopsis thaliana and rice in which melatonin intermediate N-acetylserotonin is rapidly converted to serotonin. This reaction restricts synthesis of melatonin thereby maintaining optimal levels of melatonin for balanced plant growth and development [20]. Like other plant hormones, melatonin exerts multiple physiological effects in plants such as regulation of stomatal opening/closing, photosynthesis, tropism, changes in metabolism of carbohydrates and nitrogen and cellular effects like changing the intracellular calcium (Ca 2+ ) content and membrane permeability [21,22,23,24]. Recently, the first melatonin receptor CAND2/PMTR1, a G-protein coupled receptor has been identified in Arabidopsis thaliana which was shown to regulate stomatal closure mediated by melatonin [25]. This was long-sought because a lack of melatonin receptor in plants was impeding the full understanding of melatonin-mediated signalling. Without an identified receptor, it was also a challenge to view melatonin as a potential plant hormone.

Recent studies have investigated the crosstalk of melatonin with the well-known plant hormones such as salicylic acid (SA), abscisic acid (ABA), and ethylene [26]. Of particular interest has been the comparison between melatonin and the widely studied hormone, auxin, because of their common biosynthetic precursor, tryptophan, which leads to structural similarities such as having an indole core. These similarities have led to the hypothesis that melatonin could also share auxin-like activities, in terms of regulating growth in a concentration-dependent manner. However, the current understanding of the relationship between melatonin and auxin remains unclear. Previous studies using promoter-reporter constructs, gene expression and physiological responses both support and contradict similar modes of action or overlapping signalling pathway between auxin and melatonin. In support of similar functions, it has been reported that melatonin stimulated plant growth at low concentrations (10 − 4 M,10 − 7 M, 0.01 M) similar to auxin by increasing root growth, lateral and adventitious root formation in a variety of plant species [6, 27, 28]. Similarly, in roots of Brassica juncea, melatonin treatment (0.1 μM) increased the concentration of indole-acetic acid (IAA) and enhanced root growth [13, 29]. In, transgenic tomato plants over-expressing the ovine melatonin biosynthetic pathway gene, Serotonin-N-acetyltransferase (SNAT), led to a decrease in IAA levels and loss of apical dominance [15, 30]. Similarly, melatonin decreased the transcript abundance of YUCCA (YUC) (YUC1, YUC2, YUC5, YUC6 and TAR2) auxin biosynthetic genes upon 600 μM treatment in Arabidopsis roots [31]. Auxin-responsive marker lines such as Direct Repeat 5, DR5, have been used to investigate the response and distribution of auxin in many plant species such as Arabidopsis and soybean [32]. DR5 is a synthetic promoter containing auxin responsive elements (AuxREs) and is widely utilized as an indirect marker of endogenous auxin distribution, signalling and responses [33, 34]. Wang and colleagues showed that exogenous melatonin treatment at an inhibitory concentration (600 μM) to Arabidopsis roots enhanced GFP and GUS expression of DR5 lines [31]. Moreover, RNA-sequencing analysis from 10 and 20 μM melatonin-treated roots of 2-week old rice seedlings showed that auxin-signalling genes were significantly increased in abundance [35]. In contradiction to above studies, Kim et al. (2016) reported the inability of melatonin (10 − 7 M–10 − 4 M) to stimulate the plant responses in maize in the classical bioassays that are specifically used to demonstrate auxin responsiveness i.e. elongation of coleoptiles, inhibition of roots in young seedlings and induction of ethylene biosynthetic gene, 1-aminocyclopropane-1-carboxylate (ACC) synthase [36]. Gene expression studies in Arabidopsis plants treated with 100 pM and 1 mM melatonin revealed that auxin biosynthetic and related genes were not changed in transcript abundance except for one auxin-responsive gene IAA-amino synthase, that was increased in abundance upon melatonin treatment [37]. It has been shown that melatonin treatment (5, 100, 450 and 500 μM) was unable to induce expression of auxin-responsive marker line DR5:GUS in Arabidopsis seedlings [38, 39]. The data from these studies point to the contrasting findings between and within plant species. A common confounding factor especially for transcriptomic analysis, has been the lack of direct comparisons between melatonin and auxin treatments under identical set of experimental conditions.

Interplay of melatonin and mitochondria has been extensively studied in mammals but just recently begun to be investigated in plants [40, 41]. Mitochondria are the powerhouses of cells and play a key role in growth and development of plants by providing necessary metabolites, enzyme cofactors and energy (ATP). Recent studies have shown that mitochondria play integral role in cellular signalling. Mitochondrial signalling, or mitochondrial retrograde signalling results when mitochondria functioning is perturbed by stimuli and this leads to transmission of signals to alter nuclear gene expression [42]. This shows that mitochondria are not only crucial for plant’s growth and development but also for driving responses to biotic and abiotic stresses. It is thus not a surprise that there exists an interaction between mitochondrial and hormone signalling pathways as hormones are strongly linked with processes of growth and stress defence [43]. Nuclear genes encoding mitochondrial proteins have been shown to be responsive to a variety of hormone treatments based on a meta-analysis study. The main regulators of mitochondrial function identified were plant hormones auxin, cytokinin (CK), jasmonic acid (JA), and salicylic acid (SA) [43]. More direct targeted approaches have shown an interaction between these hormones and mitochondrial signalling. For example, ABA-induced signalling of guard cells in response to drought stress is negatively regulated by a pyruvate carrier of mitochondria termed as Negative Regulator of Guard Cell ABA Signalling 1, (NRGA1) in Arabidopsis thaliana [44]. Salicylic acid (SA) treatment has been shown to uncouple and inhibit mitochondrial electron transport in Nicotiana tabacum [45]. Auxin and mitochondrial respiration has been long hypothesized to have a connection [46]. Moreover, multiple studies have shown a link between auxin responses and mitochondrial function [47, 48]. The mutants of genes encoding proteins that are involved in the synthesis of the inner mitochondrial membrane such as Filamentation Temperature Sensitive H 4 (FTSH4) and PROHIBITIN3 were shown to inhibit auxin response [49, 50]. Additionally, auxin was oxidatively degraded in the ftsh4 mutant through hydrogen peroxide (H2O2)-mediation which is suggested to be a strategy to prioritize processes such as stress defence over growth-related processes [51]. Antimycin A is a chemical stimulus of mitochondrial stress that acts by blocking complex III of the mitochondrial respiratory chain. Treatment by antimycin A also led to decreased auxin (IAA) levels and down-regulation of auxin receptors and transporters such as auxin efflux transporters PIN1/3/4/7 in Arabidopsis thaliana [52, 53]. Additionally, auxin homeostasis was defective in mutants of the gene IAA-alanine Resistant 4 (IAR4) which encodes a putative mitochondrial pyruvate dehydrogenase E1 alpha-subunit suggesting its integral role in maintaining auxin homeostasis [54].

Alternative oxidase (AOX) is a terminal oxidase which is part of the plant mitochondrial electron transport chain and acts to uncouple respiration by bypassing proton-pumping complexes III and IV, thereby reducing excessive burst of reactive oxygen species (ROS). This activity is particularly dominant under stressful environmental conditions such as drought, low temperature and bacterial infection by Pseudomonas syringae where studies have shown a remarkable increase in AOX transcript and/or protein [55]. This indicates that a wide array of pathways can trigger AOX and hence it is considered as a marker for mitochondrial retrograde signalling. While a range of plant hormones can trigger/induce AOX such as SA and ET [45, 56] others such as auxin are antagonistic to the induction of AOX [52]. Auxin (4.5 μM NAA) application was shown to inhibit the Antimycin A-mediated induction of promoter-reporter Alternative oxidase1a (AOX1a::LUC) in Arabidopsis [52]. The antagonistic relationship of auxin and mitochondrial retrograde signalling plays a central role in balancing growth and stress responses. Mitochondria along with chloroplasts have been hypothesized to be the original sites of synthesis of melatonin. This relates to the endosymbiotic theory where these organelles are considered to be the descendants of endosymbiotic bacteria which produced melatonin [57]. Very recently, synthesis of melatonin in mitochondria, as well as chloroplasts has been reported in leaves of apple Malus zumi and Arabidopsis. Moreover, apple melatonin biosynthetic genes Serotonin N-acetyltransferase SNAT and Acetylserotonin O-methyltransferase ASMT were found to be localized to mitochondria and chloroplasts, respectively in both apple and Arabidopsis [41, 58]. However, there is lack of understanding in how melatonin functions with mitochondrial retrograde signalling and its relatedness with auxin. Thus, alternative oxidase is an ideal marker to test the interaction between auxin and melatonin.

In this study, the effects of melatonin were compared directly to auxin treatments. Two different transgenic Arabidopsis lines carrying inducible promoter-reporter constructs that are responsive to auxin were used to compare the response of plants to melatonin versus auxin. Direct repeat 5 (green fluorescent protein) DR5::GFP as a marker for auxin response and Alternative oxidase1a (luciferase) AOX1a::LUC as a marker for mitochondrial retrograde signalling was used [52, 59]. Furthermore, the potential molecular crosstalk between melatonin and auxin was investigated using global transcriptome analysis of Arabidopsis rosette leaves of seedlings whose roots were treated with either melatonin or auxin.


In bacteria, gene regulation is traditionally thought of as an adaptive or homeostatic mechanism that allows the cell to respond to changing metabolic conditions or to environmental stresses (e.g., Wall et al, 2004 Seshasayee et al, 2009 ). The underlying rationale is that proteins ‘should’ be made only when needed so as to conserve cellular resources or because the protein's activity is detrimental in other conditions. The classic example is the induction in Escherichia coli of the lac operon in response to lactose: the lac operon is required for growth on lactose, and the lac operon is very weakly expressed in the absence of lactose. If the lac operon is artificially induced in the absence of lactose by adding a non-metabolizable analog of lactose to the medium, then the expression of the lac operon reduces the growth rate. This reduction in the growth rate reflects the cost of producing useless proteins instead of useful ones ( Stoebel et al, 2008 ) and also the detrimental activity of the LacY permease in some conditions ( Eames and Kortemme, 2012 ). The relative reduction in E. coli's growth rate due to producing useless proteins seems to vary across growth conditions, but under low-cost conditions, the cost is approximately the fraction of total protein that is useless ( Shachrai et al, 2010 ).

Although many specific examples of gene regulation appear to be adaptive under laboratory conditions, it is not clear whether the regulation of the majority of genes is adaptive. Genome-wide studies in both bacteria and yeast have found little correlation between changes in expression and the importance of genes for fitness ( Birrell et al, 2002 Giaever et al, 2002 Smith et al, 2006 Deutschbauer et al, 2011 ). In other words, most genes are not downregulated when they are not needed for growth, and conversely, most genes that are upregulated do not seem to be important for fitness. This is surprising because under a cost-benefit model of optimal expression ( Dekel and Alon, 2005 ), the optimal expression level of a gene will be much lower if there is little or no benefit (or fitness advantage) than if there is a large benefit. Thus, there is a puzzle as to why adaptive regulation does not seem to be more widespread in bacteria.

There have been several proposals for why genes might be expressed when they are not needed for fitness or why they might not be induced when they are needed. More precisely, these theories try to explain why bacteria with apparently non-adaptive regulation have not been outcompeted by other bacteria with more optimal regulation. First, some genes might be expressed in ‘standby mode’ because they will help the bacterium survive if conditions change ( Fischer and Sauer, 2005 ). Standby control can be thought of as a way to reduce the delay inherent in adaptive control. If a gene is under adaptive control and is not expressed at all when it is not needed, then after conditions change and it becomes needed, there is a delay until enough of the protein is produced to adapt to this new condition. During this delay, the cell might stop growing or might even die. Thus, uncertainty about the near future implies some possibility of a benefit from expressing a gene that is not currently needed. If there is a significant chance of obtaining a benefit in the future, then the average future benefit will exceed the (certain) cost of expressing unneeded protein, so the optimal expression level will be above zero even though the gene currently confers no benefit. Conversely, if the gene is currently needed but conditions might change in the near future, this reduces the expected benefit of high expression, and hence reduces the optimal expression level. In other words, optimal standby control should dampen the dynamic range of expression without changing the pattern. (For a detailed example, see Supplementary Figure 1). Thus, optimal standby control cannot explain why there is so little correlation between relative expression (i.e., when genes are upregulated) and mutant fitness (i.e., when they are needed for optimal growth).

A second and related theory is that proteins that are only needed in small amounts might be expressed constitutively because the cost of adaptive control, such as the cost of making transcription factors, might exceed the benefit of making less of the protein when it is not needed ( Wessely et al, 2011 ). The cost of regulation seems small—e.g., the LacI repressor is present at only 20–50 copies per cell ( Milo et al, 2010 )—so this theory should only apply to weakly expressed genes that have a low cost of unnecessary expression.

A third theory related to changing conditions is that microorganisms might use one environmental signal to ‘anticipate’ another ( Tagkopoulos et al, 2008 Mitchell et al, 2009 ). Here, the change in environment is (somewhat) predictable, rather than being entirely random. For example, for a gut bacterium like E. coli, a rise in temperature might indicate that it has been ingested and will soon reach an anaerobic environment ( Tagkopoulos et al, 2008 ), so genes for anaerobic respiration might be induced even though they are not immediately useful. It is not clear whether anticipatory control of expression is widespread in bacteria.

Fourth, horizontally transferred genes, which are common in bacteria, might lack regulation because of insufficient time to evolve appropriate regulation in their current host ( Lercher and Pal, 2008 ). However, only the most recently transferred genes seem to lack regulation ( Lercher and Pal, 2008 ). Also, regulation can evolve quickly ( Stone and Wray, 2001 Berg et al, 2004 ), regulation can be conserved across transfer events ( Price et al, 2008 ), and many horizontally transferred genes are under complex control by multiple transcription factors ( Price et al, 2008 ). Thus, we doubt that horizontal gene transfer could explain why there is little correlation between relative expression (i.e., regulation) and mutant fitness genome-wide.

Fifth, the regulation of some genes might be suboptimal or maladaptive because the expression patterns of those genes are not under strong selection. More precisely, if altered regulation improves relative growth by less than 1/Ne per generation, where Ne is the effective size of the bacterial population and the effect on growth is averaged across natural environments, then this altered regulation is unlikely to take over the population. Selectively neutral evolution could also account for some of the complexity of gene regulation ( Lynch, 2007 ). However, both regulatory sites ( McCue et al, 2002 Rajewsky et al, 2002 ) and the coexpression of genes ( Price et al, 2007 ) are usually conserved between closely related bacteria, which implies that the regulation of most genes is under some selection. Furthermore, in E. coli, over half of all genes are present at above 0.1 mRNA per cell in a single condition, which corresponds to 30–60 proteins per cell ( Lu et al, 2007 ) or over 1 in 100 000 of all protein molecules in the cell ( Milo et al, 2010 ). Because the fitness cost of unnecessary expression of a gene is probably at least as great as its proportion of total protein, this implies that the fitness cost of unnecessary expression of the typical gene is at least 10 −5 per generation. This is about the same as the estimated fitness cost of mutations in codon usage that are under selection ( Bulmer, 1991 ). Thus, unnecessary expression of the typical protein should be under selection.

Finally, we propose that non-adaptive regulation is widespread in bacteria, at least in laboratory settings, because of two major factors. First, bacterial genomes encode far more operons than regulators. In the typical bacterium, only 4.2% of proteins are predicted to be transcription factors ( Charoensawan et al, 2010 ). With so few regulators, most genes are probably regulated by factors that are not directly related to their function. We call this mode of regulation indirect control. As an example, bacterial genes are often regulated by ‘global’ transcription factors that regulate diverse and sometimes functionally unrelated genes ( Martinez-Antonio and Collado-Vides, 2003 ). Second, bacterial regulatory systems have evolved under very different conditions than those being tested in the laboratory. If the utility of a gene's activity correlates with a functionally unrelated signal, then regulation by that signal will be selected for in the wild, but this correlation will probably not be maintained in artificial conditions. So we do not expect indirect control that evolved in the wild to be adaptive under artificial conditions. In contrast, if there is a direct regulatory link between an environmental signal and the physiological response, as with the lac operon, then the regulatory system can perform well outside of the conditions that it evolved under.

To test these various theories of bacterial gene regulation, we collected genome-wide mutant fitness data and gene expression data from the metal-reducing bacterium Shewanella oneidensis MR-1 across 15 matching conditions. We also examined large compendia of (unmatched) fitness and expression data for this bacterium. We found that 24% of genes are detrimental to fitness in some laboratory conditions, which shows that the regulation of many genes is maladaptive in the laboratory. We confirmed that the correlation between relative expression and mutant fitness is weak, as in our previous study with just four conditions ( Deutschbauer et al, 2011 ). We ruled out some technical explanations for the weak correlation, such as growth phase effects on expression, subtle variations in experimental conditions, or genetic redundancy due to paralogs, and we found little evidence of anticipatory control. As evidence of indirect control, we show that many genes are expressed constitutively instead of being controlled by transcription factors, or are regulated by growth rate. Furthermore, for many genes, this regulation seems to be suboptimal and cannot be explained by standby control. We also show that genes with closely related functions can have rather different expression patterns, which suggests that some of them are not under direct control.

To test the generality of our findings, we examined the expression and mutant fitness of biosynthetic genes in four diverse bacteria—S. oneidensis MR-1, E. coli K-12, the ethanol-producing bacterium Zymomonas mobilis ZM4, and the sulfate-reducing bacterium Desulfovibrio alaskensis G20. In E. coli, biosynthetic genes that were required for fitness in minimal media but not in rich media were almost all downregulated in minimal media, but in the other three bacteria, this was often not the case. We also compared fitness and expression data for Z. mobilis ZM4 across 18 matching conditions, and found little correlation between relative expression and mutant fitness in Z. mobilis ZM4. We conclude that suboptimal regulation is widespread in bacteria, at least under laboratory conditions.


During Drosophila oogenesis, mRNA localization presents an initial key step for the establishment of the body axes and embryonic patterning. Several mRNAs of maternal origin, including bicoid, gurken and osk, are transported from the nurse cells into the oocyte and localized to distinct positions within the oocyte. After fertilization, their locally translated protein products provide positional information and establish a tightly controlled transcriptional regulatory network for the segmentation of the embryo [61]. The localization of mRNPs containing bicoid or osk has been studied in great detail [11, 62] and involves microtubule-dependent motor proteins such as kinesin and dynein [11]. Especially in the case of osk, its localization process can be structured into several phases, including mRNA export from the nucleus, dynein-dependent transport of osk from the nurse cells into the oocyte, and kinesin-dependent trafficking to the posterior pole of the oocyte. A number of RNA-binding proteins have been described that act in trans to facilitate the transport from the nurse cells into and within the oocyte [11]. Some of them also serve as translational repressor during the transport process.

Throughout its lifetime from synthesis in the nuclei of nurse cells to degradation at the posterior pole of the oocyte, the osk transcript is associated with a dynamic collection of proteins. These factors orchestrate the synthesis, processing, export, translational control, localization, and degradation of osk mRNA. More functionally relevant trans-acting factors are known for osk RNA than for any other localized transcript.

During splicing in multicellular eukaryotes, a large multisubunit complex called the exon junction complex (EJC) is deposited upstream the exon-exon junction. Whereas this complex generally serves as a hallmark for the nonsense-mediated decay of mRNAs with premature stop codons [63], its assembly upstream of the first exon-exon junction is essential for osk mRNA localization [64]. This requirement nicely fits genetic data showing that osk reporter mRNAs derived from cDNA are incompetent of localization in the absence of endogenous osk mRNA. In addition to EJC components, several other RNA-binding proteins whose loss of function result in defects in osk localization, shuttle between nucleus and cytoplasm. These include proteins of the heterologous nuclear RNP (hnRNP) family such as Hrp48 and Squid/Hrp40. Whereas Hrp48 directly binds to osk 5’- and 3’-UTR [65, 66], Hrp40 interacts only with osk 3’-UTR where it also binds to Hrp48 [67]. Since many hnRNP proteins bind RNA co-transcriptionally [68], Hrp40 and Hrp48 likely also assemble with osk in the nucleus. Unlike other hnRNPs binding to osk, the polypyrimidine tract-binding protein (PTB)/hnRNP I does not need to bind its target mRNA inside the nucleus. This conclusion was drawn based on the observation that an exclusively cytoplasmic variant of PTB is able to associate with osk and functionally replace endogenous PTB [69]. In contrast to the Drosophila protein, the nuclear association of the Xenopus laevis PTB homolog with its target Vg1 mRNA has been proposed to be a crucial step during localization [70]. Drosophila PTB binds to multiple sites within the osk 3’-UTR and mediates the formation of large complexes containing multiple osk molecules [69]. This assembly might serve at least two functions, packaging multiple mRNA molecules into mRNPs for efficient transport and repression of osk translation by masking the mRNA from the translation machinery.

Interestingly, formation of large osk RNA protein particles also involves a second translational repressor, Bruno [71]. Bruno contains three RNA-Recognition Motifs (RRM), binds to several sites within osk 3’-UTR (Bruno response elements, BRE) and appears to repress translation via two different mechanisms. On one hand, Bruno recruits Cup, an inhibitor of cap-dependent translation initiation that interferes with the interaction of the translation initiation factors eIF4E and eIF4G [72]. On the other hand, in vitro observations suggest that by binding to its cognate sites within osk mRNA the protein incorporates the transcripts into large 50 - 80S translation silencing particles [73]. This packaging of mRNA renders it inaccessible for the translation apparatus. Similar to Bruno, Hrp48 binds to BREs [66]. Life imaging of osk RNP particles has recently revealed that Hrp48 is also required for formation of large osk particles [74]. Additional evidence for a function of BRE in multimerization of osk mRNA comes from observations that BRE elements can act in trans and establish translational control on co-expressed osk mRNA mutants that lack BREs [75]. This finding is consistent with the idea that osk RNP particles contain multiple osk RNA molecules with their corresponding RNA-binding proteins. Besides protein-driven multimerization, new data also suggest that RNA-RNA interaction between individual osk molecules could contribute to the formation of these large particles. A stem-loop region within osk 3’-UTR that does not encompass any known binding site for the above mentioned proteins is sufficient to drive homodimerization of two osk messages [76]. Together these data suggest that protein- as well as RNA-mediated formation of large particles is crucial, both for translational repression and transport of osk mRNA.

The large osk particles described above contain additional RNA binding proteins like Exuperantia (Exu) and Staufen (Stau). Exu lacks canonical RNA binding motifs but associates with osk mRNA and with many proteins involved in translational repression of osk [77]. Exu is required for proper osk mRNA localization and found in RNPs that display dynamic movements consistent with active transport. Staufen contains several double-stranded RNA binding domains (dsRBDs) and is involved in RNA localization in a number of organisms [78]. On one hand, genetic data provide evidence that it is involved in anchoring at the end of transport. On the other hand, Stau is a component of the large osk mRNPs already early on during microtubule-dependent transport [79]. Although in mammalian cells, at least a subfraction of both Staufen homologs, Stau1 and Stau2, shuttle between nucleus and cytoplasm [80], it has been demonstrated that XStau, the Xenopus homolog that participates in localization of Vg1 mRNA in oocytes, assembles with the RNA in the cytoplasm after nuclear export [70]. Similarly, Drosophila Stau associates with the mature osk particle in the cytoplasm, presumably after the transport of osk particles from the nurse cell to the oocyte [74]. In the oocyte, osk-containing mRNPs are localized to the posterior pole via active transport by microtubule-dependent motor proteins [74, 81]. The transport is at least in part mediated by the plus end directed motor kinesin-1. Interestingly, tracking of osk mRNPs in living oocytes has revealed that this transport corresponds to a random walk [81] and might actually reflect directed, motor-dependent movement along a weakly polarized microtubule network [82]. Transport is followed by anchoring or local entrapment at the oocyte’s posterior pole. This entrapment depends on components of the actomyosin system [83] but also on RNA binding proteins like Staufen.

In summary, the detailed analysis of osk mRNA localization has revealed that also in multicellular organisms multiple RNA-binding proteins participate in the localization of an mRNA and that nuclear events are important to guide cytoplasmic localization of transcripts. Some of the described proteins like Stau, Hrp48, or EJC components such as Barentz might have a more direct role in mRNA transport by e.g. recruiting different motor proteins (dynein or kinesin I) at various stages of localization. The involved RNA-binding proteins can have diverse but also overlapping functions during localization, ranging from translational control to particle formation and anchoring at the target site.


Expression microarrays are used to analyze molecular profiles of cancer in order to better understand the biological background of the disease. Another aim is to find new molecular markers, therapeutic targets, and/or new classification approaches that will enable better treatment of patients. Our study was intended to achieve both goals. We searched for gene expression patterns that may characterize histological types of ovarian cancer and are related to its histological grade, FIGO stage, response to chemotherapy, and survival times.

From the broad spectrum of features that we analyzed in our study, only histological type of the tumor was a factor, which showed a very strong impact on the gene expression pattern. Interestingly, there was one exception: six undifferentiated tumors that were available for this analysis, showed practically no difference in gene expression pattern from serous cancers. If confirmed in other studies, this may be an indication for evaluating these two groups together in microarray analyses.

On the contrary, the differences between serous/undifferentiated, endometrioid, and clear cell cancers were statistically highly significant. Moreover, the gene expression signature selected in respect to tumor histology allowed for a very precise sample classification, with the sensitivity and specificity not achieved in any other comparisons. Also unsupervised analysis, performed using the singular value decomposition (SVD) showed that histological type of the tumor is a major source of variability in the gene expression pattern in ovarian cancer (not shown). This large difference in gene expression pattern may be not surprising when we take into account that histological differences are clearly manifested at the morphological level and are easily distinguishable by light microscopy. On the other hand, these results, indicating deep molecular divergence, may support the current knowledge that ovarian cancer has a heterogeneous histological origin (e.g., fallopian, endometrioid, or endocervical) (28�).

The histology of ovarian cancer was already analyzed in many previous microarray studies (33�), however, it has not been regarded as a confounding factor in gene expression analysis in respect to other features. Conversely, different factors have been analyzed across various histological types. This may be one of the reasons for discrepancies and low reproducibility of the findings. Thus, a practical conclusion may be drawn that when searching for the genes related to other features of ovarian cancer, the analyses should be carried out on histologically homogenous groups of samples. Alternatively, the influence of the histological type on gene expression may be controlled by multivariate approach.

Except for evaluation of histological type, no other comparison gave such a huge number of statistically significant genes. This was the reason why we decided to use less stringent criteria for gene selection (uncorrected p-value π.001 and FDR 㰐%). Analyzing gene expression patterns in tumor samples of different grades, we focused mostly on the difference between grade 3 and 4, as the usage of the latter grade was abandoned in ovarian cancer diagnostics. A study performed by members of our group showed that the recognition of grade 4 might be important from the clinical viewpoint, since patients with grade 4 ovarian cancer had worse response to taxanes than to DNA-damaging agents (23). Thus, we expected that we would find differences between grade 3 and 4 also at the molecular level. However, samples classification was poor and in pairwise comparison we found only one gene with significantly changed expression (FDR 㰐%). It was surprising, as in ANOVA we found 152 probe sets (FDR 㰐%) differentiating between three grades (G2, G3, and G4), and this difference was also significant in the global test. In our opinion, this discrepancy may suggest that although tumor grade is generally associated with significant changes in gene expression pattern, the subjectively defined grades 3 and 4 may not reflect these differences. An additional factor influencing the results of this analysis may be the small and unequal size of the groups evaluated.

The problem also occurred when analyzing gene expression profiles in relation to FIGO stages and residual tumor size. These features were significant in the ANOVA and global tests, but the number of genes with different expression found in pairwise comparisons was low and the quality of classification was poor. The difference between FIGO II and FIGO III/IV was statistically significant, however, this result may be an artifact related to uneven samples distribution in the groups being compared.

As far as the residual tumor size is concerned, poor classification of tumor samples may be due to the fact that debulking status did not solely depend on the biological tumor profile, but also on the changing attitude to optimal debulking over several years during which our material was collected. Other factors influencing the results might be technical issues, such as skills of surgeons and the equipment available. Our samples came from mid 1990s (patients treated with platinum𠄼yclophosphamide, PC), and from early 2000s (patients treated with taxane–platinum, TP). The group treated with PC had been generally less radically operated than the group treated with TP (23). Thus, this may be the major reason why it was hard to obtain reliable results in gene expression analysis in respect to this parameter. In addition, the arbitrarily outlined classes (R0𠄲) may not reflect intrinsic biological differences.

The most important, from the clinical point of view, is the search for molecular markers suitable for prediction of tumor response to the therapy. In the presented analysis, we were not able to find a gene signature that would allow for good classification of samples sensitive and resistant to chemotherapy. It seems that chemosensitivity/resistance, in contrast to, e.g., histological type, is a feature that may depend on subtle molecular changes, possibly in many alternative pathways. Such differences may be hard to detect by the methods applied. It has been shown recently, by comparing the data from Cancer Cell Line Encyclopedia and Cancer Genome Project, that discrepancies in drug sensitivity testing are common even when performed on cell lines (41). Another reason for the failure of this analysis may be again the fact that we analyzed two cohorts of patients treated with different CHT regimens. Probably, different molecular pathways were engaged in tumor response to the two regimens and this could affect the results of our analyses. It was not advisable, however, to divide patients into two groups according to the CHT regimen, because this would result in biased results due to small classes of samples.

We also searched for genes that may be related to patients’ prognosis, i.e., DFS and OS. Only 4 out of 15 genes, selected in microarray analysis as associated with OS, were positively validated by qRT-PCR, and none were validated for DFS. Our further attempts to validate these four genes in the independent set of samples were unsuccessful. There may be several reasons for this result. First, all genes selected in respect to survival time were of low statistical significance in the microarray analysis. Second, contrarily to the initial group, the independent set of patients used for validation was uniformly treated with TP regimen only. Therefore, it might show results different from those obtained in the initial, mixed group. Indeed, we observed that the initial group of patients had different OS statistics than the test group (Table 6).

Table 6. Characteristics of the two groups of patients according to OS statistics (days).

In general, the results of qRT-PCR validation were surprising. Several genes that were selected as related to one feature appeared to correlate with another factor(s). In our opinion, this observation confirms that many clinical and biological features of the tumor are difficult to define and that arbitrarily assigned groups of samples used in gene expression analyses not always reflect biologically significant differences.

Our attempts to validate selected genes were rather unsuccessful. It should be noted, however, that we performed an external validation on the independent group of tumor samples, while many other studies that claim finding potential biomarkers, were confined just to the internal, technical validation [reviewed, e.g., in Ref. (2, 42)].

One of the most interesting genes selected in our study is CLASP1 (cytoplasmic linker associated protein 1). It was associated with both OS and DFS in the microarray analysis, although validation results were mixed. In the initial group of samples, it was validated in respect to OS and showed significant association with response to chemotherapy and with the presence of hereditary BRCA1 mutation. Surprisingly, when we tried to validate CLASP1 in the independent set of samples it was statistically insignificant in respect to OS, but it proved to be associated again with DFS. CLASP1 is thought to play a role in the regulation of microtubule dynamics in interphase and during cell division (43, 44). Thus, the protein may be important in tumor cell response to taxanes. Possibly, it may also be somehow engaged in differential response to CHT in patients with hereditary, BRCA1 mutation-linked ovarian cancer. Regardless of the inconsistent results of validation, we think that CLASP1 may be worth further investigation as a potential prognostic and predictive marker.

Why narcoleptics get fat

People with narcolepsy are not only excessively sleepy, but they are also prone to gaining weight. In fact, narcoleptic patients will often pack on pounds even as they eat considerably less than your average person.

Now researchers reporting in the October issue of Cell Metabolism, a Cell Press publication, appear to have an answer as to why. It seems a deficiency of the neuropeptide hormone orexin, an ingredient that encourages hunger and wakefulness, may leave them with a lack of energy-burning brown fat.

The findings may lead to orexin-based weight loss therapies for those with narcolepsy and for the rest of us, too, according to the researchers.

Orexins are rather unique in that they allow one to eat more and lose more at the same time, explained Devanjan Sikder of the Sanford-Burnham Research Institute. "It is a couch potato's dream."

Fat comes in one of two types: white or brown. White fat stores calories while brown fat burns them, generating heat in the process. There had been hints that orexins might influence body temperature, but it wasn't clear exactly how.

The new evidence in mice shows that orexins are critical for the formation of mature brown fat from its precursors. With too little orexin, animals' brown fat activity drops along with their energy expenditure. Likewise, mice injected with orexin show a substantial loss of fat.

The findings bolster the emerging concept that those with less active brown fat may be destined from birth, or even before, to be fatter. "They are somehow predisposed," Sikder said.

There are already ways of stimulating brown fat's production, but it isn't easy to do. For instance, more brown fat is produced when you spend a lot of time in the cold. The new findings suggest that orexin therapies might be useful for increasing brown fat and literally melting extra calories away.

"One caveat is that orexin might increase arousal," the researchers wrote, "although this is expected only under sleep deprived conditions."

Sikder says it will now also be worthwhile to examine orexin-deficient people with narcolepsy to find out whether their brown fat activity is indeed compromised.


Transcript quantification has been a key component of RNA-seq analysis pipelines, and the most popular approaches (such as RSEM [1], kallisto [2], and Salmon [3]) estimate the abundance of individual transcripts by inference over a generative model from transcripts to observed reads. To generate a read in the model, a transcript is first sampled proportional to its relative abundance multiplied by length, then a fragment is sampled as a subsequence of the transcript according to bias correction models. The quantification algorithm thus takes the reference transcriptome and the set of reads as input and outputs a most probable set of relative abundances under the model. We focus on a generalization of the problem, called graph quantification, that allows for better handling of uncertainty in the reference transcriptome.

The concept of graph quantification was first proposed by Bernard et al. [4], which introduced a method called FlipFlop. Instead of a set of linear transcripts, a splice graph is given and every transcript compatible with the splice graph (a path from transcript start to termination in the splice graph) is assumed to be able to express reads. The goal is to infer the abundance of edges of the splice graph (or its extensions) under flow balance constraints. Transcript abundances are obtained by flow decomposition under this setup. FlipFlop infers network flow on its extension of splice graphs, called fragment graphs, and uses the model to further assemble transcripts. However, the proposed fragment graph model only retains its theoretical guarantee when the lengths of single-end reads or paired-end fragments are fixed. In this work, we propose an alternative approach to graph quantification that correctly addresses the variable-length reads and corrects for sequencing biases. Our method is based on flow inference on a different extension of the splice graph.

Modeling RNA-seq reads directly by network flow on splice graphs (or variants) is advantageous when the set of transcript sequences is uncertain or incomplete. It is unlikely that the set of reference transcripts is correct and complete for all genes in all tissues, and therefore, many transcriptome assembly methods have been developed for reconstructing a set of expressed transcripts from RNA-seq data [5,6,7,8], including FlipFlop [4]. Recent long-read sequencing confirms the expression of unannotated transcripts [9], but it also shows that the individual exons and splice junctions are relatively accurate. With incomplete reference transcripts but correct splice graphs, it is more appropriate to model RNA-seq reads directly by splice graph network flows compared to modeling using the abundances of an incomplete set of transcripts.

The network flow of graph quantification may be incorporated into other transcriptome assembly methods in addition to FlipFlop. StringTie [6] iteratively finds the heaviest path of a flow network constructed from splice graphs. A theoretical work by Shao et al. [10] studies the minimum path decomposition of splice graphs when the edge abundances satisfy flow balance constraints. Better network flow estimation on splice graphs inspires improvement of transcriptome assembly methods.

The splice graph flow itself is biologically meaningful as it indicates the relative usage of splice junctions. Estimates of these quantities can be used to study alternative splicing patterns under the incomplete reference assumption. PSG [11] pioneered this line of work but with a different abundance representation. It models splice junction usage by fixed-order Markov transition probabilities from one exon (or fixed number of predecessor exons) to its successor exon in the splice graph. It develops a statistical model to detect the difference in transition probability between two groups of samples. However, a fixed-order Markov chain has limitations: a small order cannot capture long-range phasing relationships, and a large order requires inferring a number of transition probabilities that are likely to lack sufficient read support. Markov models set the abundance of a transcript to the product of transition probabilities of its splice junctions, which implicitly places a strong constraint on the resulting transcriptome. Many other previous studies of splice junction usage depend on a list of reference transcripts and compute the widely used metric Percentage Spliced In (PSI) [12,13,14]. Under an incomplete reference assumption, the estimated network flow is a potential candidate to compute PSI and study alternative splicing usage.

A key challenge of graph quantification, especially for paired-end reads, is to incorporate the co-existence relationship among exons in transcripts. When a read spans multiple exons, the exons must co-exist in the transcript that generates this read. Such a co-existence relationship is called phasing, and the corresponding read is said to contain phasing information. For these reads, the flows of the spanned splice edges may be different from each other, and in this case, the probability of the read cannot be uniquely inferred from the original splice graph flow. FlipFlop solves this problem by expanding the splice graph into a fragment graph, assuming all reads are fixed-length. In a fragment graph, every vertex represents a phasing path, two vertices are connected if the phasing paths represented by the vertices differ by one exon, and every transcript on the splice graph maps to a path on the fragment graph. The mapped path in the fragment graph contains every possible phasing path from a read in the transcript, in ascending order of genomic location. However, it is not possible to construct this expansion of splice graphs when the reads or fragments are of variable lengths. There is no longer a clear total order over all phasing paths possible from a given transcript, and it is unclear how to order the phasing paths in a fragment graph. We detail the FlipFlop model in Additional file 1: Section 1.1.

To incorporate the phasing information from variable-length reads or fragments, we develop a dynamic unrolling technique over the splice graph with an Aho-Corasick automaton. The resulting graph is called a prefix graph. We prove that optimizing a network flow on the prefix graph is equivalent to optimizing abundances of reference transcripts using the state-of-the-art transcript expression quantification formulation when all full paths of splice graphs are provided as reference transcripts, assuming modeled biases of generating a fragment are determined by the fragment sequence itself regardless of which transcript it is from. In other words, quantification on prefix graphs generates exact quantification for the whole set of full splice graph paths. The proof is done by reparameterizing the sequencing read generation model from transcript abundances to edge abundances in the prefix graph. We also propose a specialized EM algorithm to efficiently infer a prefix graph flow that solves the graph quantification problem.

As a case study, we apply our method to paired-end RNA-seq data of bipolar disease samples and estimate flows for neurogenesis-related genes, which are known to have complex alternative splicing patterns and unannotated isoforms. We use this case study to demonstrate the applicability of our method to handle variable-length fragments. Additionally, the network flow leads to different PSI compared to the one computed with reference transcripts, suggesting reference completeness should be considered in alternative splicing analysis.

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Can we test the hypothesis that mitochondrial health = immune health and enhanced resistance to the virus?

In terms of testing and/or looking for evidence that mitochondria could help explain some of the pathophysiology of this virus, there are several potential ways to look for this relationship, ranging from laboratory based to population studies.

Direct evidence that SARs-CoV-2 modulates mitochondrial function

This work could be carried out in vitro with cultured cells and/or isolated mitochondria prepared from control and infected individuals. In particular, using imaging to look for co-localisation in “virus factories”.

Lifestyle and mitochondrial function

It could be predicted that those populations exhibiting the lowest levels of optimal health and the highest levels of the metabolic syndrome and “diabesity”, will show the highest susceptibility. For example, it might be revealing to map the case fatality rate to the latest trends in obesity/ diabetes after the necessary confounders are taken into account [284]. In support of this, the emerging data from New York in relation to SARS-CoV-2 infection is that obesity is strongly correlated with critical illness [9]. In contrast, would those populations showing the highest fitness levels and functioning be more resistant? For instance, would measured VO2 max show an inverse relationship with morbidity?

Inherited mitochondrial dysfunction

Individuals with known mitochondrial dysfunction are well known to show abnormal susceptibility to infections [25]. Is there a link between mitochondrial haplotype and resistance? There is certainly evidence for different mtDNA haplotypes amongst different populations [285]. Although there is an emerging disparity in morbidity between Black people and other minorities in the USA, it is thought it may be more to do with socio-economic imbalances and higher rates of lifestyle induced co-morbidities [286].

Markers of mitochondrial health in the blood

Blood-derived mitochondrial markers of reduced function may correlate with disease severity before, during and after infection.

Epigenetic age and mitochondrial function

Data now show it is possible to determine someone’s epigenetic age and compare it with their chronological age. There is a close correlation with this ratio and co-existing morbidity [287]. Thus, would blood-derived epigenetic markers of metabolic age correlate with disease severity before, during and after infection?

Watch the video: iOS 15 vs iOS BATTERY Test on iPhone 12, 11, XR, 8, 7, u0026 6s (September 2022).


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