Feeds:
Posts
Comments

Posts Tagged ‘Quality Control’

COVID-19-vaccine rollout risks and challenges

Reporter : Irina Robu, PhD

BioNTech and Pfizer and Moderna COVID-19 vaccines received Emergency Use Authorization in January 2021 in Canada, European Union, United Kingdom and United States. However, in certain places COVID-19 has hit a few hindrances such as stockpiles have accumulated, deployment to vulnerable countries and at-risk groups has been slower than expected.  Yet, experts can see the light at the end of the tunnel of the pandemic. In United States, hundred of organization take a vital role in vaccine deployment, adapting their operations to meet the demands for volume, speed and better technology. Tens of thousands of transporters, vaccine handlers, medical and pharmacy staff, and frontline workers have mandatory training on the specific characteristics of each manufacturer’s distinct vaccines.

The common operating model provides the details of end-to-end vaccine deployment. Possible areas of risk to the rapid delivery of COVID-19 vaccines in the United States include:

Raw-materials constraints in production scaling

Scaling access to material and boosting production levels can cause logistical, contractual and even diplomatic challenges, requiring new forms of collaboration. The top two US manufacturers, for example, can produce 280 million vials per year, capable of holding up to 2.8 billion doses.

Quality-assurance challenges in manufacturing

Generating yields to produce a new class of vaccines—such as those based on mRNA or viral vectors—at an unprecedented scale (1.8 billion to 2.3 billion doses by mid-2021), manufacturers have required massive volumes of inputs, a larger technical workforce.

Cold-chain logistics and storage-management challenges

Manufacturers and distributors are preparing to maintain cold-chain requirements for distribution and long-term storage of mRNA-based vaccines. Large amounts of dry ice may be needed at various locations before administration.

Increased labor requirements

Complex protocols for handling and preparing COVID-19 vaccines have the potential to strain labor capacities or divert workers from other critical roles.

Wastage at points of care

Errors in storing, preparing, or scheduling administration of doses at points of care will have significant consequences and proper on-site storage conditions are also of critical importance.

IT challenges

IT systems, including vaccine-tracking systems and immunization information systems will be vital for allocating, distributing, recording, and monitoring the deployment of vaccines.

There are several possible approaches to help mitigate each of the six risks discussed, each with practical steps for organization to take across the common operating model.

Building resilient raw-materials supplies

  • Resilience planning.Producers can partner with global suppliers of raw materials and ancillary-product manufacturers to create redundancies.
  • Collaboration between industry and government.Ongoing industry engagement with government is essential for ramping-up production and maintaining high levels of production.

 Scaling manufacturing within quality guidelines

  • Scale manufacturing in new and existing facilities.  Various digital and analytics tools can help expand capacity and scale more quickly.
  • Assure quality and yield in current facilities. By continuing to coordinate with regulators, manufacturers and authorities can certify that procedures and dosage quality meet both long-established and newly issued guidelines.
  • Establish predictable supplier plans. Each manufacturing stakeholder can follow a clearly defined plan and they can also conduct regular cross-functional risk reviews to ensure that quality.

Optimizing the cold chain

  • Build redundancy into distribution.Manufacturers, distributers should quickly identify points of failure and creating redundancies at each stage.
  • Leverage feedback loops.Reporting systems could be set up to capture supply-chain disruption events as soon as they happen, with data used to refine best practices and procedures and avoid further losses.
  • Use point-of-care stock management.Vaccine inventories can be redistributed to locations with greater demand. Strategies to avoid over stockpiling must confirm maintenance of the cold chain to prevent risks to the receiving administration site.

Addressing labor shortages

  • Use several types of point-of-care facilities.Rely on hospitals and primary-care locations for vaccine administration, in addition to retail pharmacies.
  • Streamline administration across sites.Deploying vaccines at larger, streamlined vaccination sites can be more efficient and improve patient safety, labor utilization, and speed of vaccination.

 Reducing spoilage at points of care

  • Track and monitor spoilage at points of care.Manufacturers and distributors can collaborate to establish the means to identify and trace instances of spoilage. They can learn from experience and refine guidance, training, certification, and allocation to optimize utilization of doses.
  • Pace first-dose allocation.Allocation of first doses to populations and locations where the need is greatest and the confidence in the availability of second doses is high (such as healthcare professionals and vulnerable populations in nursing homes).
  • Prioritize second doses.Authorities can help ensure that the recommended two-dose course schedule for such vaccines as the Pfizer-BioNTech, Moderna, and AstraZeneca vaccines are duly completed.
  • Establish recipient commitment.Vaccine recipients could be asked to commit to second-dose appointments at their point of care before first-dose administration.
  • Manage certification.National and local government institutions can collaborate to ensure that vaccination certifications are withheld until recipients receive their second dose.

Meeting IT challenges

  • Balance IT upgrades and resilience.Stakeholders should identify IT systems that can be relied upon in the deployment of COVID-19 vaccines and assess their ability to perform at scale.
  • Share cyberthreat intelligence.COVID-19-vaccine stakeholders should agree upon common requirements and processes for generating and sharing threat intelligence.
  • Establish means of demonstrating immunity.Manufacturers and distributers can commission systems to track and verify that vaccine recipients have demonstrated immunity. if it will release them from travel limits and other pandemic-related restrictions.

Although not one organization is involved for managing vaccine deployment, but the risks can be fully address if organizations align on lead organization to build scenarios to test responses to emerging crises. The groups could align on lead organizations to manage issues while building scenarios to test responses to emerging crises. The benefits in managing each of these risks could be demonstrated with compelling metrics and communications.  As COVID-19-vaccine rollouts commence, the steps mentioned above can be undertaken by manufactures, distributors and governments.

SOURCE

https://www.mckinsey.com/business-functions/risk/our-insights/the-risks-and-challenges-of-the-global-covid-19-vaccine-rollout?cid=other-eml-nsl-mip-mck&hlkid=19a51f848bee4d00806d2da81315f70d&hctky=2071733&hdpid=062f1841-f911-48f3-ab14-a9f92e30721f#

Read Full Post »

Biology, Physiology and Pathophysiology of Heat Shock Proteins

Curation: Larry H. Bernstein, MD, FCAP

 

 

Heat Shock Proteins (HSP)

  1. Exploring the association of molecular chaperones, heat shock proteins, and the heat shock response in physiological/pathological processes

Hsp70 chaperones: Cellular functions and molecular mechanism

M. P. MayerB. Bukau
Cell and Molec Life Sci  Mar 2005; 62:670  http://dx.doi.org:/10.1007/s00018-004-4464-6

Hsp70 proteins are central components of the cellular network of molecular chaperones and folding catalysts. They assist a large variety of protein folding processes in the cell by transient association of their substrate binding domain with short hydrophobic peptide segments within their substrate proteins. The substrate binding and release cycle is driven by the switching of Hsp70 between the low-affinity ATP bound state and the high-affinity ADP bound state. Thus, ATP binding and hydrolysis are essential in vitro and in vivo for the chaperone activity of Hsp70 proteins. This ATPase cycle is controlled by co-chaperones of the family of J-domain proteins, which target Hsp70s to their substrates, and by nucleotide exchange factors, which determine the lifetime of the Hsp70-substrate complex. Additional co-chaperones fine-tune this chaperone cycle. For specific tasks the Hsp70 cycle is coupled to the action of other chaperones, such as Hsp90 and Hsp100.

70-kDa heat shock proteins (Hsp70s) assist a wide range of folding processes, including the folding and assembly of newly synthesized proteins, refolding of misfolded and aggregated proteins, membrane translocation of organellar and secretory proteins, and control of the activity of regulatory proteins [17]. Hsp70s have thus housekeeping functions in the cell in which they are built-in components of folding and signal transduction pathways, and quality control functions in which they proofread the structure of proteins and repair misfolded conformers. All of these activities appear to be based on the property of Hsp70 to interact with hydrophobic peptide segments of proteins in an ATP-controlled fashion. The broad spectrum of cellular functions of Hsp70 proteins is achieved through

  • the amplification and diversification of hsp70genes in evolution, which has generated specialized Hsp70 chaperones,
  • co-chaperones which are selectively recruited by Hsp70 chaperones to fulfill specific cellular functions and
  • cooperation of Hsp70s with other chaperone systems to broaden their activity spectrum. Hsp70 proteins with their co-chaperones and cooperating chaperones thus constitute a complex network of folding machines.

Protein folding processes assisted by Hsp70

The role of Hsp70s in the folding of non-native proteins can be divided into three related activities: prevention of aggregation, promotion of folding to the native state, and solubilization and refolding of aggregated proteins. In the cellular milieu, Hsp70s exert these activities in the quality control of misfolded proteins and the co- and posttranslational folding of newly synthesized proteins. Mechanistically related but less understood is the role of Hsp70s in the disassembly of protein complexes such as clathrin coats, viral capsids and the nucleoprotein complex, which initiates the replication of bacteriophage λ DNA. A more complex folding situation exists for the Hsp70-dependent control of regulatory proteins since several steps in the folding and activation process of these substrates are assisted by multiple chaperones.

Hsp70 proteins together with their co-chaperones of the J-domain protein (JDP) family prevent the aggregation of non-native proteins through association with hydrophobic patches of substrate molecules, which shields them from intermolecular interactions (‘holder’ activity). Some JDPs such as Escherichia coli DnaJ and Saccharomyces cerevisiae Ydj1 can prevent aggregation by themselves through ATP-independent transient and rapid association with the substrates. Only members of the Hsp70 family with general chaperone functions have such general holder activity.

Hsp70 chaperone systems assist non-native folding intermediates to fold to the native state (‘folder’ activity). The mechanism by which Hsp70-chaperones assist the folding of non-native substrates is still unclear. Hsp70-dependent protein folding in vitro occurs typically on the time scale of minutes or longer. Substrates cycle between chaperone-bound and free states until the ensemble of molecules has reached the native state. There are at least two alternative modes of action. In the first mechanism Hsp70s play a rather passive role. Through repetitive substrate binding and release cycles they keep the free concentration of the substrate sufficiently low to prevent aggregation, while allowing free molecules to fold to the native state (‘kinetic partitioning’). In the second mechanism, the binding and release cycles induce local unfolding in the substrate, e.g. the untangling of a misfolded β-sheet, which helps to overcome kinetic barriers for folding to the native state (‘local unfolding’) [8–11]. The energy of ATP may be used to induce such conformational changes or alternatively to drive the ATPase cycle in the right direction.

Hsp70 in cellular physiology and pathophysiology

Two Hsp70 functions are especially interesting, de novo folding of nascent polypeptides and interaction with signal transduction proteins, and therefore some aspects of these functions shall be discussed below in more detail. Hsp70 chaperones were estimated to assist the de novo folding of 10–20% of all bacterial proteins whereby the dependence on Hsp70 for efficient folding correlated with the size of the protein [12]. Since the average protein size in eukaryotic cells is increased (52 kDa in humans) as compared to bacteria (35 kDa in E. coli) [25], it is to be expected that an even larger percentage of eukaryotic proteins will be in need of Hsp70 during de novo folding. This reliance on Hsp70 chaperones increases even more under stress conditions. Interestingly, mutated proteins [for example mutant p53, cystis fibrosis transmembrane regulator (CFTR) variant ΔF508, mutant superoxid dismutase (SOD) 1] seem to require more attention by the Hsp70 chaperones than the corresponding wild-type protein [2629]. As a consequence of this interaction the function of the mutant protein can be preserved. Thereby Hsp70 functions as a capacitor, buffering destabilizing mutations [30], a function demonstrated earlier for Hsp90 [3132]. Such mutations are only uncovered when the overall need for Hsp70 action exceeds the chaperone capacity of the Hsp70 proteins, for example during stress conditions [30], at certain stages in development or during aging, when the magnitude of stress-induced increase in Hsp70 levels declines [3334]. Alternatively, the mutant protein can be targeted by Hsp70 and its co-chaperones to degradation as shown e.g. for CFTRΔF508 and some of the SOD1 mutant proteins [35,36]. Deleterious mutant proteins may then only accumulate when Hsp70 proteins are overwhelmed by other, stress-denatured proteins. Both mechanisms may contribute to pathological processes such as oncogenesis (mutant p53) and neurodegenerative diseases, including amyotrophic, lateral sclerosis (SOD1 mutations), Parkinsonism (α-synuclein mutations), Huntington’s chorea (huntingtin with polyglutamin expansions) and spinocerebellar ataxias (proteins with polyglutamin expansions).

De novo folding is not necessarily accelerated by Hsp70 chaperones. In some cases folding is delayed for different reasons. First, folding of certain proteins can only proceed productively after synthesis of the polypeptide is completed as shown, e.g. for the reovirus lollipop-shaped protein sigma 1 [37]. Second, proteins destined for posttranslational insertion into organellar membranes are prevented from aggregation and transported to the translocation pore [38]. Third, in the case of the caspase-activated DNase (CAD), the active protein is dangerous for the cell and therefore can only complete folding in the presence of its specific inhibitor (ICAD). Hsp70 binds CAD cotranslationally and mediates folding only to an intermediate state. Folding is completed after addition of ICAD, which is assembled into a complex with CAD in an Hsp70-dependent manner [39]. Similar folding pathways may exist also for other potentially dangerous proteins.

As mentioned above Hsp70 interacts with key regulators of many signal transduction pathways controlling cell homeostasis, proliferation, differentiation and cell death. The interaction of Hsp70 with these regulatory proteins continues in activation cycles that also involve Hsp90 and a number of co-chaperones. The regulatory proteins, called clients, are thereby kept in an inactive state from which they are rapidly activated by the appropriate signals. Hsp70 and Hsp90 thus repress regulators in the absence of the upstream signal and guarantee full activation after the signal transduction pathway is switched on [6]. Hsp70 can be titrated away from these clients by other misfolded proteins that may arise from internal or external stresses. Consequently, through Hsp70 disturbances of the cellular system induced by environmental, developmental or pathological processes act on these signal transduction pathways.

In this way stress response and apoptosis are linked to each other. Hsp70 inhibits apoptosis acting on the caspase-dependent pathway at several steps both upstream and downstream of caspase activation and on the caspase-independent pathway. Overproduction of Hsp70 leads to increased resistance against apoptosis-inducing agents such as tumor necrosis factor-α(TNFα), staurosporin and doxorubicin, while downregulation of Hsp70 levels by antisense technology leads to increased sensitivity towards these agents [1840]. This observation relates to many pathological processes, such as oncogenesis, neurodegeneration and senescence. In many tumor cells increased Hsp70 levels are observed and correlate with increased malignancy and resistance to therapy. Downregulation of the Hsp70 levels in cancer cells induce differentiation and cell death [41]. Neurodegenerative diseases such as Alzheimer’s disease, Parkinson’s disease, Huntington’s corea and spinocerebellar ataxias are characterized by excessive apoptosis. In several different model systems overexpression of Hsp70 or one of its co-chaperones could overcome the neurodegenerative symptoms induced by expression of a disease-related gene (huntingtin, α-synuclein or ataxin) [20,42]. Senescence in cell culture as well as aging in vivo is correlated with a continuous decline in the ability to mount a stress response [3443]. Age-related symptoms and diseases reflect this decreased ability to cope with cellular stresses. Interestingly, centenarians seem to be an exception to the rule, as they show a significant induction of Hsp70 production after heat shock challenge [44].

ATPase domain and ATPase cycle

Substrate binding

The coupling mechanism: nucleotide-controlled opening and closing of the substrate binding cavity

The targeting activity of co-chaperones

J-domain proteins

Bag proteins

Hip, Hop and CHIP

Perspectives

The Hsp70 protein family and their co-chaperones constitute a complex network of folding machines which is utilized by cells in many ways. Despite considerable progress in the elucidation of the mechanistic basis of these folding machines, important aspects remain to be solved. With respect to the Hsp70 proteins it is still unclear whether their activity to assist protein folding relies on the ability to induce conformational changes in the bound substrates, how the coupling mechanism allows ATP to control substrate binding and to what extent sequence variations within the family translate into variations of the mechanism. With respect to the action of co-chaperones we lack a molecular understanding of the coupling function of JDPs and of how co-chaperones target their Hsp70 partner proteins to substrates. Furthermore, it can be expected that more cellular processes will be discovered that depend on the chaperone activity of Hsp70 chaperones.

 

  1. The biochemistry and ultrastructure of molecular chaperones

Structure and Mechanism of the Hsp90 Molecular Chaperone Machinery

Laurence H. Pearl and Chrisostomos Prodromou
Ann Rev of Biochem July 2006;75:271-294
http://dx.doi.org:/10.1146/annurev.biochem.75.103004.142738

Heat shock protein 90 (Hsp90) is a molecular chaperone essential for activating many signaling proteins in the eukaryotic cell. Biochemical and structural analysis of Hsp90 has revealed a complex mechanism of ATPase-coupled conformational changes and interactions with cochaperone proteins, which facilitate activation of Hsp90’s diverse “clientele.” Despite recent progress, key aspects of the ATPase-coupled mechanism of Hsp90 remain controversial, and the nature of the changes, engendered by Hsp90 in client proteins, is largely unknown. Here, we discuss present knowledge of Hsp90 structure and function gleaned from crystallographic studies of individual domains and recent progress in obtaining a structure for the ATP-bound conformation of the intact dimeric chaperone. Additionally, we describe the roles of the plethora of cochaperones with which Hsp90 cooperates and growing insights into their biochemical mechanisms, which come from crystal structures of Hsp90 cochaperone complexes.

 

  1. Properties of heat shock proteins (HSPs) and heat shock factor (HSF)

Heat shock factors: integrators of cell stress, development and lifespan

Malin Åkerfelt,*‡ Richard I. Morimoto,§ and Lea Sistonen*‡
Nat Rev Mol Cell Biol. 2010 Aug; 11(8): 545–555.  doi:  10.1038/nrm2938

Heat shock factors (HSFs) are essential for all organisms to survive exposures to acute stress. They are best known as inducible transcriptional regulators of genes encoding molecular chaperones and other stress proteins. Four members of the HSF family are also important for normal development and lifespan-enhancing pathways, and the repertoire of HSF targets has thus expanded well beyond the heat shock genes. These unexpected observations have uncovered complex layers of post-translational regulation of HSFs that integrate the metabolic state of the cell with stress biology, and in doing so control fundamental aspects of the health of the proteome and ageing.

In the early 1960s, Ritossa made the seminal discovery of temperature-induced puffs in polytene chromosomes of Drosophila melanogaster larvae salivary glands1. A decade later, it was shown that the puffing pattern corresponded to a robust activation of genes encoding the heat shock proteins (HSPs), which function as molecular chaperones2. The heat shock response is a highly conserved mechanism in all organisms from yeast to humans that is induced by extreme proteotoxic insults such as heat, oxidative stress, heavy metals, toxins and bacterial infections. The conservation among different eukaryotes suggests that the heat shock response is essential for survival in a stressful environment.

The heat shock response is mediated at the transcriptional level by cis-acting sequences called heat shock elements (HSEs; BOX 1) that are present in multiple copies upstream of the HSP genes3. The first evidence for a specific transcriptional regulator, the heat shock factor (HSF) that can bind to the HSEs and induce HSP gene expression, was obtained through DNA–protein interaction studies on nuclei isolated from D. melanogaster cells4,5. Subsequent studies showed that, in contrast to a single HSF in invertebrates, multiple HSFs are expressed in plants and vertebrates68. The mammalian HSF family consists of four members: HSF1,HSF2, HSF3 and HSF4. Distinct HSFs possess unique and overlapping functions (FIG. 1), exhibit tissue-specific patterns of expression and have multiple post-translational modifications (PTMs) and interacting protein partners7,9,10. Functional crosstalk between HSF family members and PTMs facilitates the fine-tuning of HSF-mediated gene regulation. The identification of many targets has further extended the impact of HSFs beyond the heat shock response. Here, we present the recent discoveries of novel target genes and physiological functions of HSFs, which have changed the view that HSFs act solely in the heat shock response. Based on the current knowledge of small-molecule activators and inhibitors of HSFs, we also highlight the potential for pharmacologic modulation of HSF-mediated gene regulation.

Box 1

The heat shock element

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3402356/bin/nihms281610u1.jpg

Heat shock factors (HSFs) act through a regulatory upstream promoter element, called the heat shock element (HSE). In the DNA-bound form of a HSF, each DNA-binding domain (DBD) recognizes the HSE in the major groove of the double helix6. The HSE was originally identified using S1 mapping of transcripts of the Drosophila melanogaster heat shock protein (HSP) genes3 (see the figure; part a). Residues –47 to –66 are necessary for heat inducibility. HSEs in HSP gene promoters are highly conserved and consist of inverted repeats of the pentameric sequence nGAAn132. The type of HSEs that can be found in the proximal promoter regions of HSP genes is composed of at least three contiguous inverted repeats: nTTCnnGAAnnTTCn132134. The promoters of HSF target genes can also contain more than one HSE, thereby allowing the simultaneous binding of multiple HSFs. The binding of an HSF to an HSE occurs in a cooperative manner, whereby binding of an HSF trimer facilitates binding of the next one135. More recently, Trinklein and colleagues used chromatin immunoprecipitation to enrich sequences bound by HSF1 in heat-shocked human cells to define the HSE consensus sequence. They confirmed the original finding of Xiao and Lis, who identified guanines as the most conserved nucleotides in HSEs87,133 (see the figure; part b). Moreover, in a pair of inverted repeats, a TTC triplet 5′ of a GAA triplet is separated by a pyrimidine–purine dinucleotide, whereas the two nucleotides separating a GAA triplet 5′ from a TTC triplet is unconstrained87. The discovery of novel HSF target genes that are not involved in the heat shock response has rendered it possible that there may be HSEs in many genes other than the HSP genes. Although there are variations in these HSEs, the spacing and position of the guanines are invariable7. Therefore, both the nucleotides and the exact spacing of the repeated units are considered as key determinants for recognition by HSFs and transcriptional activation. Part b of the figure is modified, with permission, from REF. 87 © (2004) The American Society for Cell Biology.

Figure 1     http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3402356/bin/nihms281610f1.gif

The mammalian HSF machinery

HSFs as stress integrators

A hallmark of stressed cells and organisms is the increased synthesis of HSPs, which function as molecular chaperones to prevent protein misfolding and aggregation to maintain protein homeostasis, also called proteostasis11. The transcriptional activation of HSP genes is mediated by HSFs (FIG. 2a), of which HSF1 is the master regulator in vertebrates. Hsf1-knockout mouse and cell models have revealed that HSF1 is a prerequisite for the transactivation of HSP genes, maintenance of cellular integrity during stress and development of thermotolerance1215. HSF1 is constitutively expressed in most tissues and cell types16, where it is kept inactive in the absence of stress stimuli. Thus, the DNA-binding and transactivation capacity of HSF1 are coordinately regulated through multiple PTMs, protein–protein interactions and subcellular localization. HSF1 also has an intrinsic stress-sensing capacity, as both D. melanogaster and mammalian HSF1 can be converted from a monomer to a homotrimer in vitro in response to thermal or oxidative stress1719.

Figure 2    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3402356/bin/nihms281610f2.gif

Members of the mammalian HSF family

Functional domains

HSFs, like other transcription factors, are composed of functional domains. These have been most thoroughly characterized for HSF1 and are schematically presented in FIG. 2b. The DNA-binding domain (DBD) is the best preserved domain in evolution and belongs to the family of winged helix-turn-helix DBDs2022. The DBD forms a compact globular structure, except for a flexible wing or loop that is located between β-strands 3 and 4 (REF. 6). This loop generates a protein– protein interface between adjacent subunits of the HSF trimer that enhances high-affinity binding to DNA by cooperativity between different HSFs23. The DBD can also mediate interactions with other factors to modulate the transactivating capacity of HSFs24. Consequently, the DBD is considered as the signature domain of HSFs for target-gene recognition.

The trimerization of HSFs is mediated by arrays of hydrophobic heptad repeats (HR-A and HR-B) that form a coiled coil, which is characteristic for many Leu zippers6,25 (FIG. 2b). The trimeric assembly is unusual, as Leu zippers typically facilitate the formation of homodimers or heterodimers. Suppression of spontaneous HSF trimerization is mediated by yet another hydrophobic repeat, HR-C2628. Human HSF4 lacks the HR-C, which could explain its constitutive trimerization and DNA-binding activity29. Positioned at the extreme carboxyl terminus of HSFs is the transactivation domain, which is shared among all HSFs6except for yeast Hsf, which has transactivation domains in both the amino and C termini, and HSF4A, which completely lacks a transactivation domain2931. In HSF1, the transactivation domain is composed of two modules — AD1 and AD2, which are rich in hydrophobic and acidic residues (FIG. 3a) — that together ensures a rapid and prolonged response to stress32,33. The transactivation domain was originally proposed to provide stress inducibility to HSF1 (REFS 34,35), but it soon became evident that an intact regulatory domain, located between the HR-A and HR-B and the transactivation domain, is essential for the responsiveness to stress stimuli32,33,36,37. Because several amino acids that are known targets for different PTMs reside in the regulatory domain33,3842, the structure and function of this domain are under intensive investigation.

Figure 3    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3402356/bin/nihms281610f3.gif

HSF1 undergoes multiple PTMs on activation

Regulation of the HSF1 activation–attenuation cycle

The conversion of the inactive monomeric HSF1 to high-affinity DNA-binding trimers is the initial step in the multistep activation process and is a common feature of all eukaryotic HSFs43,44 (FIG. 3b). There is compelling evidence for HSF1 interacting with multiple HSPs at different phases of its activation cycle. For example, monomeric HSF1 interacts weakly with HSP90 and, on stress, HSF1 dissociates from the complex, allowing HSF1 trimerization45,46 (FIG. 3b). Trimeric HSF1 can be kept inactive when its regulatory domain is bound by a multi-chaperone complex of HSP90, co-chaperone p23 (also known as PTGES3) and immunophilin FK506-binding protein 5 (FKBP52; also known as FKBP4)4651. Elevated levels of both HSP90 and HSP70 negatively regulate HSF1 and prevent trimer formation on heat shock52. Activated HSF1 trimers also interact with HSP70 and the co-chaperone HSP40 (also known as DNAJB1), but instead of suppressing the DNA-binding activity of HSF1, this interaction inhibits its transactivation capacity5254. Although the inhibitory mechanism is still unknown, the negative feedback from the end products of HSF1-dependent transcription (the HSPs) provides an important control step in adjusting the duration and intensity of HSF1 activation according to the levels of chaperones and presumably the levels of nascent and misfolded peptides.

A ribonucleoprotein complex containing eukaryotic elongation factor 1A (eEF1A) and a non-coding RNA, heat shock RNA-1 (HSR-1), has been reported to possess a thermosensing capacity. According to the proposed model, HSR-1 undergoes a conformational change in response to heat stress and together with eEF1A facilitates trimerization of HSF1 (REF. 55). How this activation mode relates to the other regulatory mechanisms associated with HSFs remains to be elucidated.

Throughout the activation–attenuation cycle, HSF1 undergoes extensive PTMs, including acetylation, phosphorylation and sumoylation (FIG. 3). HSF1 is also a phosphoprotein under non-stress conditions, and the results from mass spectrometry (MS) analyses combined with phosphopeptide mapping experiments indicate that at least 12 Ser residues are phosphorylated41,5659. Among these sites, stress-inducible phosphorylation of Ser230 and Ser326 in the regulatory domain contributes to the transactivation function of HSF1 (REFS 38,41). Phosphorylation-mediated sumoylation on a single Lys residue in the regulatory domain occurs rapidly and transiently on exposure to heat shock; Ser303 needs to be phosphorylated before a small ubiquitin-related modifier (SUMO) can be conjugated to Lys298 (REF. 39). The extended consensus sequence ΨKxExxSP has been named the phosphorylation-dependent sumoylation motif (PDSM; FIG. 3)40. The PDSM was initially discovered in HSF1 and subsequently found in many other proteins, especially transcriptional regulators such as HSF4, GATA1, myocyte-specific enhancer factor 2A (MEF2A) and SP3, which are substrates for both SUMO conjugation and Pro-directed kinases40,6062.

Recently, Mohideen and colleagues showed that a conserved basic patch on the surface of the SUMO-conjugating enzyme ubiquitin carrier protein 9 (UBC9; also known as UBE2I) discriminates between the phosphorylated and non-phosphorylated PDSM of HSF1 (REF. 63). Future studies will be directed at elucidating the molecular mechanisms for dynamic phosphorylation and UBC9-dependent SUMO conjugation in response to stress stimuli and establishing the roles of kinases, phosphatases and desumoylating enzymes in the heat shock response. The kinetics of phosphorylation-dependent sumoylation of HSF1 correlates inversely with the severity of heat stress, and, as the transactivation capacity of HSF1 is impaired by sumoylation and this PTM is removed when maximal HSF1 activity is required40, sumoylation could modulate HSF1 activity under moderate stress conditions. The mechanisms by which SUMO modification represses the transactivating capacity of HSF1, and the functional relationship of this PTM with other modifications that HSF1 is subjected to, will be investigated with endogenous substrate proteins.

Phosphorylation and sumoylation of HSF1 occur rapidly on heat shock, whereas the kinetics of acetylation are delayed and coincide with the attenuation phase of the HSF1 activation cycle. Stress-inducible acetylation of HSF1 is regulated by the balance of acetylation by p300–CBP (CREB-binding protein) and deacetylation by the NAD+-dependent sirtuin, SIRT1. Increased expression and activity of SIRT1 enhances and prolongs the DNA-binding activity of HSF1 at the human HSP70.1promoter, whereas downregulation of SIRT1 enhances the acetylation of HSF1 and the attenuation of DNA-binding without affecting the formation of HSF1 trimers42. This finding led to the discovery of a novel regulatory mechanism of HSF1 activity, whereby SIRT1 maintains HSF1 in a state that is competent for DNA binding by counteracting acetylation (FIG. 3). In the light of current knowledge, the attenuation phase of the HSF1 cycle is regulated by a dual mechanism: a dependency on the levels of HSPs that feed back directly by weak interactions with HSF1, and a parallel step that involves the SIRT1-dependent control of the DNA-binding activity of HSF1. Because SIRT1 has been implicated in caloric restriction and ageing, the age-dependent loss of SIRT1 and impaired HSF1 activity correlate with an impairment of the heat shock response and proteostasis in senescent cells, connecting the heat shock response to nutrition and ageing (see below).

HSF dynamics on the HSP70 promoter

For decades, the binding of HSF to the HSP70.1 gene has served as a model system for inducible transcription in eukaryotes. In D. melanogaster, HSF is constitutively nuclear and low levels of HSF are associated with the HSP70promoter before heat shock6466. The uninduced HSP70 promoter is primed for transcription by a transcriptionally engaged paused RNA polymerase II (RNAP II)67,68. RNAP II pausing is greatly enhanced by nucleosome formation in vitro, implying that chromatin remodelling is crucial for the release of paused RNAP II69. It has been proposed that distinct hydrophobic residues in the transactivation domain of human HSF1 can stimulate RNAP II release and directly interact withBRG1, the ATPase subunit of the chromatin remodelling complex SWI/SNF70,71. Upon heat shock, RNAP II is released from its paused state, leading to the synthesis of a full-length transcript. Rapid disruption of nucleosomes occurs across the entire HSP70 gene, at a rate that is faster than RNAP II-mediated transcription72. The nucleosome displacement occurs simultaneously with HSF recruitment to the promoter in D. melanogaster. Downregulation of HSF abrogates the loss of nucleosomes, indicating that HSF provides a signal for chromatin rearrangement, which is required for HSP70 nucleosome displacement. Within seconds of heat shock, the amount of HSF at the promoter increases drastically and HSF translocates from the nucleoplasm to several native loci, including HSP genes. Interestingly, the levels of HSF occupying the HSP70 promoter reach saturation soon after just one minute65,73.

HSF recruits the co-activating mediator complex to the heat shock loci, which acts as a bridge to transmit activating signals from transcription factors to the basal transcription machinery. The mediator complex is recruited by a direct interaction with HSF: the transactivation domain of D. melanogaster HSF binds to TRAP80(also known as MED17), a subunit of the mediator complex74. HSF probably has other macromolecular contacts with the preinitiation complex as it binds to TATA-binding protein (TBP) and the general transcription factor TFIIB in vitro75,76. In contrast to the rapid recruitment and elongation of RNAP II on heat shock, activated HSF exchanges very slowly at the HSP70 promoter. HSF stays stably bound to DNA in vivo and no turnover or disassembly of transcription activator is required for successive rounds of HSP70 transcription65,68.

Functional interplay between HSFs

Although HSF1 is the principal regulator of the heat shock response, HSF2 also binds to the promoters of HSP genes. In light of our current knowledge, HSF2 strictly depends on HSF1 for its stress-related functions as it is recruited to HSP gene promoters only in the presence of HSF1 and this cooperation requires an intact HSF1 DBD77. Nevertheless, HSF2 modulates, both positively and negatively, the HSF1-mediated inducible expression of HSP genes, indicating that HSF2 can actively participate in the transcriptional regulation of the heat shock response. Coincident with the stress-induced transcription of HSP genes, HSF1 and HSF2 colocalize and accumulate rapidly on stress into nuclear stress bodies (NSBs; BOX 2), where they bind to a subclass of satellite III repeats, predominantly in the human chromosome 9q12 (REFS 7880). Consequently, large and stable non-coding satellite III transcripts are synthesized in an HSF1-dependent manner in NSBs81,82. The function of these transcripts and their relationship with other HSF1 targets, and the heat shock response in general, remain to be elucidated.

 

Box 2

Nuclear stress bodies  

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3402356/bin/nihms281610u2.jpg

The cell nucleus is highly compartmentalized and dynamic. Many nuclear factors are diffusely distributed throughout the nucleoplasm, but they can also accumulate in distinct subnuclear compartments, such as nucleoli, speckles, Cajal bodies and promyelocytic leukaemia (PML) bodies136. Nuclear stress bodies (NSBs) are different from any other known nuclear bodies137,138. Although NSBs were initially thought to contain aggregates of denatured proteins and be markers of heat-shocked cells, their formation can be elicited by various stresses, such as heavy metals and proteasome inhibitors137. NSBs are large structures, 0.3–3 μm in diameter, and are usually located close to the nucleoli or nuclear envelope137,138. NSBs consist of two populations: small, brightly stained bodies and large, clustered and ring-like structures137.

NSBs appear transiently and are the main site of heat shock factor 1 (HSF1) and HSF2 accumulation in stressed human cells80. HSF1 and HSF2 form a physically interacting complex and colocalize into small and barely detectable NSBs after only five minutes of heat shock, but the intensity and size of NSBs increase after hours of continuous heat shock. HSF1 and HSF2 colocalize in HeLa cells that have been exposed to heat shock for one hour at 42°C (see the figure; confocal microscopy image with HSF1–green fluorescent protein in green and endogenous HSF2 in red). NSBs form on specific chromosomal loci, mainly on q12 of human chromosome 9, where HSFs bind to a subclass of satellite III repeats78,79,83. Stress-inducible and HSF1-dependent transcription of satellite III repeats has been shown to produce non-coding RNA molecules, called satellite III transcripts81,82. The 9q12 locus consists of pericentromeric heterochromatin, and the satellite III repeats provide scaffolds for docking components, such as splicing factors and other RNA-processing proteins139143.

HSF2 also modulates the heat shock response through the formation of heterotrimers with HSF1 in the NSBs when bound to the satellite III repeats83 (FIG. 4). Studies on the functional significance of heterotrimerization indicate that HSF1 depletion prevents localization of HSF2 to NSBs and abolishes the stress-induced synthesis of satellite III transcripts. By contrast, increased expression of HSF2 leads to its own activation and the localization of both HSF1 and HSF2 to NSBs, where transcription is spontaneously induced in the absence of stress stimuli. These results suggest that HSF2 can incorporate HSF1 into a transcriptionally competent heterotrimer83. It is possible that the amounts of HSF2 available for heterotrimerization with HSF1 influence stress-inducible transcription, and that HSF1–HSF2 heterotrimers regulate transcription in a temporal manner. During the acute phase of heat shock, HSF1 is activated and HSF1–HSF2 heterotrimers are formed, whereas upon prolonged exposures to heat stress the levels of HSF2 are diminished, thereby limiting heterotrimerization83. Intriguingly, in specific developmental processes such as corticogenesis and spermatogenesis, the expression of HSF2 increases spatiotemporarily, leading to its spontaneous activation. Therefore, it has been proposed that HSF-mediated transactivation can be modulated by the levels of HSF2 to provide a switch that integrates the responses to stress and developmental stimuli83 (FIG. 4). Functional relationships between different HSFs are emerging, and the synergy of DNA-binding activities among HSF family members offers an efficient way to control gene expression in a cell- and stimulus-specific manner to orchestrate the differential upstream signalling and target-gene networks.

Figure 4   http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3402356/bin/nihms281610f4.gif

 

Interactions between different HSFs provide distinct functional modes in transcriptional regulation

A new member of the mammalian HSF family, mouse HSF3, was recently identified10. Avian HSF3 was shown to be activated at higher temperatures and with different kinetics than HSF1 (REF. 84), whereas in mice, heat shock induces the nuclear translocation of HSF3 and activation of stress-responsive genes other than HSP genes10. Future experiments will determine whether HSF3 is capable of interacting with other HSFs, potentially through heterocomplex formation. HSF4 has not been implicated in the heat shock response, but it competes with HSF1 for common target genes in mouse lens epithelial cells85, which will be discussed below. It is important to elucidate whether the formation of homotrimers or hetero trimers between different family members is a common theme in HSF-mediated transcriptional regulation.

 

HSFs as developmental regulators

Evidence is accumulating that HSFs are highly versatile transcription factors that, in addition to protecting cells against proteotoxic stress, are vital for many physioogical functions, especially during development. The initial observations using deletion experiments of the D. melanogaster Hsf gene revealed defective oogenesis and larvae development86. These effects were not caused by obvious changes in HSP gene expression patterns, which is consistent with the subsequent studies showing that basal expression of HSP genes during mouse embryogenesis is not affected by the lack of HSF1 (REF. 13). These results are further supported by genome-wide gene expression studies revealing that numerous genes, not classified as HSP genes or molecular chaperones, are under HSF1-dependent control87,88.

Although mice lacking HSF1 can survive to adulthood, they exhibit multiple defects, such as increased prenatal lethality, growth retardation and female infertility13. Fertilized oocytes do not develop past the zygotic stage when HSF1-deficient female mice are mated with wild-type male mice, indicating that HSF1 is a maternal factor that is essential for early post-fertilization development89. Recently, it was shown that HSF1 is abundantly expressed in maturing oocytes, where it regulates specifically Hsp90α transcription90. The HSF1-deficient oocytes are devoid of HSP90α and exhibit a blockage of meiotic maturation, including delayed G2–M transition or germinal vesicle breakdown and defective asymmetrical division90. Moreover, intra-ovarian HSF1-depleted oocytes contain dysfunctional mitochondria and are sensitive to oxidative stress, leading to reduced survival91. The complex phenotype of Hsf1-knockout mice also demonstrates the involvement of HSF1 in placenta formation, placode development and the immune system15,85,92,93, further strengthening the evidence for a protective function of HSF1 in development and survival.

Both HSF1 and HSF2 are key regulators in the developing brain and in maintaining proteostasis in the central nervous system. Disruption of Hsf1 results in enlarged ventricles, accompanied by astrogliosis, neurodegeneration, progressive myelin loss and accumulation of ubiquitylated proteins in specific regions of the postnatal brain under non-stressed conditions94,95. The expression of HSP25 (also known as HSPB1) and α-crystallin B chain (CRYAB), which are known to protect cells against stress-induced protein damage and cell death, is dramatically decreased in brains lacking HSF1 (REF. 13). In contrast to HSF1, HSF2 is already at peak levels during early brain development in mice and is predominantly expressed in the proliferative neuronal progenitors of the ventricular zone and post-mitotic neurons of the cortical plate9699. HSF2-deficient mice have enlarged ventricles and defects in cortical lamination owing to abnormal neuronal migration9799. Incorrect positioning of superficial neurons during cortex formation in HSF2-deficient embryos is caused by decreased expression of the cyclin-dependent kinase 5 (CDK5) activator p35, which is a crucial regulator of the cortical migration signalling pathway100,101. The p35 gene was identified as the first direct target of HSF2 in cortex development99. As correct cortical migration requires the coordination of multiple signalling molecules, it is likely that HSF2, either directly or indirectly, also regulates other components of the same pathway.

 

Cooperativity of HSFs in development

In adult mice, HSF2 is most abundantly expressed in certain cell types of testes, specifically pachytene spermatocytes and round spermatids102. The cell-specific expression of HSF2 in testes is regulated by a microRNA, miR-18, that directly binds to the 3′ untranslated region (UTR) of HSF2 (J.K. Björk, A. Sandqvist, A.N. Elsing, N. Kotaja and L.S., unpublished observations). Targeting of HSF2 in spermatogenesis reveals the first physiological role for miR-18, which belongs to the oncomir-1 cluster associated mainly with tumour progression103. In accordance with the expression pattern during the maturation of male germ cells, HSF2-null male mice display several abnormal features in spermatogenesis, ranging from smaller testis size and increased apoptosis at the pachytene stage to a reduced amount of sperm and abnormal sperm head shape97,98,104. A genome-wide search for HSF2 target promoters in mouse testis revealed the occupancy of HSF2 on the sex chromosomal multi-copy genes spermiogenesis specific transcript on the Y 2 (Ssty2), Sycp3-like Y-linked (Sly) and Sycp3-like X-linked (Slx), which are important for sperm quality104. Compared with the Hsf2-knockout phenotype, disruption of both Hsf1 and Hsf2 results in a more pronounced phenotype, including larger vacuolar structures, more widely spread apoptosis and a complete lack of mature spermatozoa and male sterility105. The hypo thesis that the activities of HSF1 and HSF2 are intertwined and essential for spermatogenesis is further supported by our results that HSF1 and HSF2 synergistically regulate the sex chromosomal multi-copy genes in post-meiotic round spermatids (M.Å., A. Vihervaara, E.S. Christians, E. Henriksson and L.S., unpublished observations). Given that the sex chromatin mostly remains silent after meiosis, HSF1 and HSF2 are currently the only known transcriptional regulators during post-meiotic repression. These results, together with the earlier findings that HSF2 can also form heterotrimers with HSF1 in testes83, strongly suggest that HSF1 and HSF2 act in a heterocomplex and fine-tune transcription of their common target genes during the maturation of male germ cells.

HSF1 and HSF4 are required for the maintenance of sensory organs, especially when the organs are exposed to environmental stimuli for the first time after birth85,88. During the early postnatal period, Hsf1-knockout mice display severe atrophy of the olfactory epithelium, increased accumulation of mucus and death of olfactory sensory neurons88. Although lens development in HSF4-deficient mouse embryos is normal, severe abnormalities, including inclusion-like structures in lens fibre cells, appear soon after birth and the mice develop cataracts85,106,107. Intriguingly, inherited severe cataracts occurring in Chinese and Danish families have been associated with a mutation in the DBD of HSF4 (REF. 108). In addition to the established target genes, Hsp25Hsp70 and Hsp90, several new targets for HSF1 and HSF4, such as crystallin γF (Crygf), fibroblast growth factor 7 (Fgf7) and leukaemia inhibitory factor (Lif) have been found to be crucial for sensory organs85,88. Furthermore, binding of either HSF1 or HSF4 to the Fgf7 promoter shows opposite effects on gene expression, suggesting competitive functions between the two family members85. In addition to the proximal promoters, HSF1, HSF2 and HSF4 bind to other genomic regions (that is, introns and distal parts of protein-coding genes in mouse lens), and there is also evidence for either synergistic interplay or competition between distinct HSFs occupying the target-gene promoters109. It is possible that the different HSFs are able to compensate for each other to some extent. Thus, the identification of novel functions and target genes for HSFs has been a considerable step forward in understanding their regulatory mechanisms in development.

 

HSFs and lifespan

The lifespan of an organism is directly linked to the health of its tissues, which is a consequence of the stability of the proteome and functionality of its molecular machineries. During its lifetime, an organism constantly encounters environmental and physiological stress and requires an efficient surveillance of protein quality to prevent the accumulation of protein damage and the disruption of proteostasis. Proteotoxic insults contribute to cellular ageing, and numerous pathophysiological conditions, associated with impaired protein quality control, increase prominently with age11. From studies on the molecular basis of ageing, in which a wide range of different model systems and experimental strategies have been used, the insulin and insulin-like growth factor 1 receptor (IGF1R) signalling pathway, which involves the phosphoinositide 3-kinase (PI3K) and AKT kinases and the Forkhead box protein O (FOXO) transcription factors (such as DAF-16 in Caenorhabditis elegans), has emerged as a key process. The downregulation of HSF reduces the lifespan and accelerates the formation of protein aggregates in C. elegans carrying mutations in different components of the IGF1R-mediated pathway. Conversely, inhibition of IGF1R signalling results in HSF activation and promotes longevity by maintaining proteostasis110,111. These results have prompted many laboratories that use other model organisms to investigate the functional relationship between HSFs and the IGF1R signalling pathway.

The impact of HSFs on the lifespan of whole organisms is further emphasized by a recent study, in which proteome stability was examined during C. elegansageing112. The age-dependent misfolding and downregulation of distinct metastable proteins, which display temperature-sensitive missense mutations, was examined in different tissues. Widespread failure in proteostasis occurred rapidly at an early stage of adulthood, coinciding with the severely impaired heat shock response and unfolded protein response112. The age-dependent collapse of proteostasis could be restored by overexpression of HSF and DAF-16, strengthening the evidence for the unique roles of these stress-responsive transcription factors to prevent global instability of the proteome.

Limited food intake or caloric restriction is another process that is associated with an enhancement of lifespan. In addition to promoting longevity, caloric restriction slows down the progression of age-related diseases such as cancer, cardiovascular diseases and metabolic disorders, stimulates metabolic and motor activities, and increases resistance to environmental stress stimuli113. To this end, the dynamic regulation of HSF1 by the NAD+-dependent protein deacetylase SIRT1, a mammalian orthologue of the yeast transcriptional regulator Sir2, which is activated by caloric restriction and stress, is of particular interest. Indeed, SIRT1 directly deacetylates HSF1 and keeps it in a state that is competent for DNA binding. During ageing, the DNA-binding activity of HSF1 and the amount of SIRT1 are reduced. Consequently, a decrease in SIRT1 levels was shown to inhibit HSF1 DNA-binding activity in a cell-based model of ageing and senescence42. Furthermore, an age-related decrease in the HSF1 DNA-binding activity is reversed in cells exposed to caloric restriction114. These results indicate that HSF1 and SIRT1 function together to protect cells from stress insults, thereby promoting survival and extending lifespan. Impaired proteostasis during ageing may at least partly reflect the compromised HSF1 activity due to lowered SIRT1 expression.

 

Impact of HSFs in disease

The heat shock response is thought to be initiated by the presence of misfolded and damaged proteins, and is thus a cell-autonomous response. When exposed to heat, cells in culture, unicellular organisms, and cells in a multicellular organism can all trigger a heat shock response autonomously115117. However, it has been proposed that multicellular organisms sense stress differently to isolated cells. For example, the stress response is not properly induced even if damaged proteins are accumulated in neurodegenerative diseases like Huntington’s disease and Parkinson’s disease, suggesting that there is an additional control of the heat shock response at the organismal level118. Uncoordinated activation of the heat shock response in cells in a multicellular organism could cause severe disturbances of interactions between cells and tissues. In C. elegans, a pair of thermosensory neurons called AFDs, which sense and respond to temperature, regulate the heat shock response in somatic tissues by controlling HSF activity119,120. Moreover, the heat shock response in C. elegans is influenced by the metabolic state of the organism and is reduced under conditions that are unfavourable for growth and reproduction121. Neuronal control may therefore allow organisms to coordinate the stress response of individual cells with the varying metabolic requirements in different tissues and developmental stages. These observations are probably relevant to diseases of protein misfolding that are highly tissue-specific despite the often ubiquitous expression of damaged proteins and the heat shock response.

Elevated levels of HSF1 have been detected in several types of human cancer, such as breast cancer and prostate cancer122,123. Mice deficient in HSF1 exhibit a lower incidence of tumours and increased survival than their wild-type counterparts in a classical chemical skin carcinogenesis model and in a genetic model expressing an oncogenic mutation of p53. Similar results have been obtained in human cancer cells lines, in which HSF1 was depleted using an RNA interference strategy124. HSF1 expression is likely to be crucial for non-oncogene addiction and the stress phenotype of cancer cells, which are attributes given to many cancer cells owing to their high intrinsic level of proteotoxic and oxidative stress, frequent spontaneous DNA damage and aneuploidy125. Each of these features may disrupt proteostasis, raising the need for efficient chaperone and proteasome activities. Accordingly, HSF1 would be essential for the survival of cancer cells that experience constant stress and develop non-oncogene addiction.

 

HSFs as therapeutic targets

Given the unique role of HSF1 in stress biology and proteostasis, enhanced activity of this principal regulator during development and early adulthood is important for the stability of the proteome and the health of the cell. However, HSF1 is a potent modifier of tumorigenesis and, therefore, a potential target for cancer therapeutics125. In addition to modulating the expression of HSF1, the various PTMs of HSF1 that regulate its activity should be considered from a clinical perspective. As many human, age-related pathologies are associated with stress and misfolded proteins, several HSF-based therapeutic strategies have been proposed126. In many academic and industrial laboratories, small molecule regulators of HSF1 are actively being searched for (see Supplementary information S1 (table)). For example, celastrol, which has antioxidant properties and is a natural compound derived from the Celastreace family of plants, activates HSF1 and induces HSP expression with similar kinetics to heat shock, and could therefore be a potential candidate molecule for treating neurodegenerative diseases127,128. In a yeast-based screen, a small-molecule activator of human HSF1 was found and named HSF1A129. HSF1A, which is structurally distinct from the other known activators, activates HSF1 and enhances chaperone expression, thereby counteracting protein misfolding and cell death in polyQ-expressing neuronal precursor cells129. Triptolide, also from the Celastreace family of plants, is a potent inhibitor of the transactivating capacity of HSF1 and has been shown to have beneficial effects in treatments of pancreatic cancer xenografts130,131. These examples of small-molecule regulators of HSF1 are promising candidates for drug discovery and development. However, the existence of multiple mammalian HSFs and their functional interplay should also be taken into consideration when planning future HSF-targeted therapies.

 

Concluding remarks and future perspectives

HSFs were originally identified as specific heat shock-inducible transcriptional regulators of HSP genes, but now there is unambiguous evidence for a wide variety of HSF target genes that extends beyond the molecular chaperones. The known functions governed by HSFs span from the heat shock response to development, metabolism, lifespan and disease, thereby integrating pathways that were earlier strictly divided into either cellular stress responses or normal physiology.

Although the extensive efforts from many laboratories focusing on HSF biology have provided a richness of understanding of the complex regulatory mechanisms of the HSF family of transcription factors, several key questions remain. For example, what are the initial molecular events (that is, what is the ‘thermometer’) leading to the multistep activation of HSFs? The chromatin-based interaction between HSFs and the basic transcription machinery needs further investigation before the exact interaction partners at the chromatin level can be established. The activation and attenuation mechanisms of HSFs require additional mechanistic insights, and the roles of the multiple signal transduction pathways involved in post-translational regulation of HSFs are only now being discovered and are clearly more complex than anticipated. Although still lacking sufficient evidence, the PTMs probably serve as rheostats to allow distinct forms of HSF-mediated regulation in different tissues during development. Further emphasis should therefore be placed on understanding the PTMs of HSFs during development, ageing and different protein folding diseases. Likewise, the subcellular distribution of HSF molecules, including the mechanism by which HSFs shuttle between the cytoplasm and the nucleus, remains enigmatic, as do the movements of HSF molecules in different nuclear compartments such as NSBs.

Most studies on the impact of HSFs in lifespan and disease have been conducted with model organisms such as D. melanogaster and C. elegans, which express a single HSF. The existence of multiple members of the HSF family in mammals warrants further investigation of their specific and overlapping functions, including their extended repertoire of target genes. The existence of multiple HSFs in higher eukaryotes with different expression patterns suggests that they may have functions that are triggered by distinct stimuli, leading to activation of specific target genes. The impact of the HSF family in the adaptation to diverse biological environments is still poorly understood, and future studies are likely to broaden the prevailing view of HSFs being solely stress-inducible factors. To this end, the crosstalk between distinct HSFs that has only recently been uncovered raises obvious questions about the stoichiometry between the components in different complexes residing in different cellular compartments, and the mechanisms by which the factors interact with each other. Interaction between distinct HSF family members could generate new opportunities in designing therapeutics for protein-folding diseases, metabolic disorders and cancer.

 

  1. Role in the etiology of cancer

Expression of heat shock proteins and heat shock protein messenger ribonucleic acid in human prostate carcinoma in vitro and in tumors in vivo

Dan Tang,1 Md Abdul Khaleque,2 Ellen L. Jones,1 Jimmy R. Theriault,2 Cheng Li,3 Wing Hung Wong,3 Mary Ann Stevenson,2 and Stuart K. Calderwood1,2,4
Cell Stress Chaperones. 2005 Mar; 10(1): 46–58. doi:  10.1379/CSC-44R.1

Heat shock proteins (HSPs) are thought to play a role in the development of cancer and to modulate tumor response to cytotoxic therapy. In this study, we have examined the expression of hsf and HSP genes in normal human prostate epithelial cells and a range of prostate carcinoma cell lines derived from human tumors. We have observed elevated expressions of HSF1, HSP60, and HSP70 in the aggressively malignant cell lines PC-3, DU-145, and CA-HPV-10. Elevated HSP expression in cancer cell lines appeared to be regulated at the post–messenger ribonucleic acid (mRNA) levels, as indicated by gene chip microarray studies, which indicated little difference in heat shock factor (HSF) or HSP mRNA expression between the normal and malignant prostate cell lines. When we compared the expression patterns of constitutive HSP genes between PC-3 prostate carcinoma cells growing as monolayers in vitro and as tumor xenografts growing in nude mice in vivo, we found a marked reduction in expression of a wide spectrum of the HSPs in PC-3 tumors. This decreased HSP expression pattern in tumors may underlie the increased sensitivity to heat shock of PC-3 tumors. However, the induction by heat shock of HSP genes was not markedly altered by growth in the tumor microenvironment, and HSP40, HSP70, and HSP110 were expressed abundantly after stress in each growth condition. Our experiments indicate therefore that HSF and HSP levels are elevated in the more highly malignant prostate carcinoma cells and also show the dominant nature of the heat shock–induced gene expression, leading to abundant HSP induction in vitro or in vivo.

Heat shock proteins (HSPs) were first discovered as a cohort of proteins that is induced en masse by heat shock and other chemical and physical stresses in a wide range of species (Lindquist and Craig 1988Georgopolis and Welch 1993). The HSPs (Table 1) have been subsequently characterized as molecular chaperones, proteins that have in common the property of modifying the structures and interactions of other proteins (Lindquist and Craig 1988Beckmann et al 1990;Gething and Sambrook 1992Georgopolis and Welch 1993Netzer and Hartl 1998). Molecular chaperone function dictates that the HSP often interact in a stoichiometric, one-on-one manner with their substrates, necessitating high intracellular concentrations of the proteins (Lindquist and Craig 1988Georgopolis and Welch 1993). As molecules that shift the balance from denatured, aggregated protein conformation toward ordered, functional conformation, HSPs are particularly in demand when the protein structure is disrupted by heat shock, oxidative stress, or other protein-damaging events (Lindquist and Craig 1988;Gething and Sambrook 1992Georgopolis and Welch 1993). The HSP27, HSP40,HSP70, and HSP110 genes have therefore evolved a highly efficient mechanism for mass synthesis during stress, with powerful transcriptional activation, efficient messenger ribonucleic acid (mRNA) stabilization, and selective mRNA translation (Voellmy 1994). HSP27, HSP70, HSP90, and HSP110 increase to become the dominantly expressed proteins after stress (Hickey and Weber 1982Landry et al 1982Li and Werb 1982Subjeck et al 1982Henics et al 1999) (Zhao et al 2002). Heat shock factor (HSF) proteins have been shown to interact with the promoters of many HSP genes and ensure prompt transcriptional activation in stress and equally precipitous switch off after recovery (Sorger and Pelham 1988Wu 1995). The hsf gene family includes HSF1 (hsf1), the molecular coordinator of the heat shock response, as well as 2 less well-characterized genes, hsf2 and hsf4(Rabindran et al 1991Schuetz et al 1991) (Nakai et al 1997). In addition to the class of HSPs induced by heat, cells also contain a large number of constitutively expressed HSP homologs, which are also listed in Table 1. The constitutive HSPs are found in a variety of multiprotein complexes containing both HSPs and cofactors (Buchner 1999). These include HSP10-HSP60 complexes that mediate protein folding and HSP70- and HSP90-containing complexes that are involved in both generic protein-folding pathways and in specific association with regulatory proteins within the cell (Netzer and Hartl 1998). HSP90 plays a particularly versatile role in cell regulation, forming complexes with a large number of cellular kinases, transcription factors, and other molecules (Buchner 1999Grammatikakis et al 2002).

 

Table 1     http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1074571/bin/i1466-1268-10-1-46-t01.jpg

 

Heat shock protein family genes studied by microchip array analysis

Many tumor types contain high concentrations of HSP of the HSP28, HSP70, and HSP90 families compared with adjacent normal tissues (Ciocca et al 1993Yano et al 1999Cornford et al 2000Strik et al 2000Ricaniadis et al 2001Ciocca and Vargas-Roig 2002). We have concentrated here on HSP gene expression in prostate carcinoma. The progression of prostatic epithelial cells to the fully malignant, metastatic phenotype is a complex process and involves the expression of oncogenes as well as escape from androgen-dependent growth and survival (Cornford et al 2000). There is a molecular link between HSP expression and tumor progression in prostate cancer in that HSP56, HSP70, and HSP90 regulate the function of the androgen receptor (AR) (Froesch et al 1998Grossmann et al 2001). Escape from AR dependence during tumorigenesis may involve altered HSP-AR interactions (Grossmann et al 2001). The role of HSPs in tumor development may also be related to their function in the development of tolerance to stress (Li and Hahn 1981). Thermotolerance is induced in cells preconditioned by mild stress coordinately with the expression of high HSP levels (Landry et al 1982Li and Werb 1982Subjeck et al 1982). Elevated HSP expression appears to be a factor in tumor pathogenesis, and, among other mechanisms, this may involve the ability of individual HSPs to block the pathways of apoptosis and permit malignant cells to arise despite the triggering of apoptotic signals during transformation (Volloch and Sherman 1999). De novo HSP expression may also afford protection of cancer cells from treatments such as chemotherapy and hyperthermia by thwarting the proapoptotic influence of these modalities (Gabai et al 1998Hansen et al 1999Blagosklonny 2001Asea et al 2001Van Molle et al 2002). The mechanisms underlying HSP induction in tumor cells are not known but may reflect the genetic alterations accompanying malignancy or the disordered state of the tumor microenvironment, which would be expected to lead to cellular stress.

Here, we have examined expression of hsf and HSP genes in immortalized normal human prostate epithelial cells and a range of prostate carcinoma cells obtained from human tumors at the mRNA and protein levels. Our aim was to determine whether hsf-HSP expression profiles are conserved in cells that express varying degrees of malignancy, under resting conditions and after heat and ionizing radiation. In addition, we have compared HSP expression profiles of a metastatic human prostate carcinoma cell line growing either in monolayer culture or as a tumor xenograft in nude mice. These studies were prompted by findings in our laboratory that prostate carcinoma cells are considerably more sensitive to heat-induced apoptosis in vivo growing as tumors compared with similar cells growing in tissue culture in vitro. Our studies show that, although the hsf-HSP expression profiles are similar in normal and malignant prostate-derived cells at the mRNA level, expression at the protein level was very different. HSF1 and HSP protein expression was highest in the 3 aggressively metastatic prostate cancer cell lines (PC-3, DU-145, and CA-HPV-10). Although the gene expression patterns of constitutive HSP differ enormously in PC-3 cells in vitro and in xenografts in vivo, stress induction of HSP genes is not markedly altered by exposure to the tumor microenvironment, indicating the hierarchical rank of the stress response that permits it to override other forms of regulation. ……

The experiments described here are largely supportive of the notion that HSP gene expression and HSF activity and expression are increased in more advanced stages of cancer (Fig 4). The most striking finding in the study was the elevation of HSF1 and HSP levels in aggressively malignant prostate carcinoma cell lines (Fig 4). It is significant that these changes in HSF and HSP levels would not have been predicted from microarray studies of HSF (Fig 3) and HSP (Fig 1) mRNA levels. The increased HSF levels observed in the metastatic prostate carcinoma cell lines in particular appear to be due to altered regulation of either mRNA translation or protein turnover (or both) (Figs 3 and ​and4).4). Although we do not at this stage know the mechanisms involved, 1 candidate could be differential activity of the proteosome in the metastatic cell lines: both HSF1 and HSF2 are targets for proteosomal degradation (Mathew et al 1998). Despite these differences in HSP expression between cells of varying degrees of malignancy under growth conditions, stress caused a major shift in HSP gene expression and activation of HSP40-1, HSP70-1A, HSP70-1B, HSP70-6 (HSP70B), DNA-J2–like, and HSP105 in all cells (Fig 2). Even in LnCap cells with minimal HSF1 and HSF2 expression, heat-inducible HSP70 protein expression was observed (Fig 4). Interestingly, we observed minimal induction of the HSP70B gene in LnCap cells: because the HSP70B promoter is known to be almost exclusively induced by stress through the HSE in its promoter, the findings may suggest that a mechanism for HSP70 induction alternative to HSF1 activation may be operative in LnCap cells (Schiller et al 1988). Increased HSP expression in cancer patients has been shown to signal a poor response to treatment by a number of modalities, suggesting that HSP expression is involved with development of resistance to treatment in addition to being involved in the mechanisms of malignant progression (Ciocca et al 1993Cornford et al 2000Yamamoto et al 2001Ciocca and Vargas-Roig 2002;Mese et al 2002). In addition, subpopulations of LnCap-derived cells, selected for enhanced capacity to metastasize, have been shown to express elevated levels of HSF1, HSP70, and HSP27 compared with nonselected controls (Hoang et al 2000). This may be highly significant because our studies indicate minimal levels of HSF1 and HSP in the poorly metastatic parent LnCap cells (Figs 1 and ​and4).4). Previous studies have also indicated that elevated HSP70 expression occurs at an early stage in cellular immortalization from embryonic stem cells (Ravagnan et al 2001). We had to use immortalized prostatic epithelial cells for our normal controls and may have missed a very early change in HSP expression during the immortalization process.

As indicated by the kinetic studies (Figs 5–7), HSPs are activated at a number of regulatory levels by stress in addition to transcriptional activation, and these may include stress-induced mRNA stabilization, differential translation, and protein stabilization (Hickey and Weber 1982Zhao et al 2002). HSF1 activity and HSP expression appear to be subject to differential regulation by a number of pathways at normal temperatures but are largely independent of such regulation when exposed to heat shock, which overrides constitutive regulation and permits prompt induction of this emergency response.

Growth of PC-3 cells in vivo as tumor xenografts was accompanied by a marked decrease in constitutive HSP expression (Figs 8 and ​and11).11). Decreased HSP expression was part of a global switch in gene expression that accompanies the switch of PC-3 cells from growth as monolayers in tissue culture to growth as tumors in vivo (D. Tang and S.K. Calderwood, in preparation). Many reports indicate changes in a wide range of cellular properties as cells grow as tumors, and these properties may reflect the remodeling of gene expression patterns. These changes may reflect adaptation to the chemical nature of the tumor microenvironment and the alterations in cell-cell interaction in growth as a tumor in vivo. Our studies also indicate the remarkable sturdiness of the heat shock response that remains intact in the PC-3 cells growing in vivo despite the global rearrangements in other gene expressions mentioned above (Figs 10 and ​and1111).

The elevation in HSF1 and HSP levels in cancer shown in our studies and in those of others and its association with a poor prognosis and inferior response to therapy suggests the strategy of targeting HSP in cancer therapy. Treatment with HSP70 antisense oligonucleotides, for instance, can cause tumor cell apoptosis on its own and can synergize with heat shock in cell killing (Jones et al 2004). Indeed, it has been shown that antagonizing heat-inducible HSP expression with quercitin, a bioflavonoid drug that inhibits HSF1 activation, or by using antisense oligonucleotides directed against HSP70 mRNA further sensitizes PC-3 cells to heat-induced apoptosis in vitro and leads to tumor regression in vivo (Asea et al 2001Lepchammer et al 2002Jones et al 2004) (A. Asea et al, personal communication). The strategy of targeting HSP expression or function in cancer cells may thus be indicated. Such a strategy might prove particularly effective because constitutive HSP expression is reduced in tumors, and this might be related to increased killing of PC-3 tumor cells by heat (Fig 12).

 

  1. Molecular chaperones in aging

Aging and molecular chaperones

Csaba So˝ti*, Pe´ter Csermely
Exper Geront 2003; 38:1037–1040  http://195.111.72.71/docs/pcs/03exger.pdf

Chaperone function plays a key role in sequestering damaged proteins and in repairing proteotoxic damage. Chaperones are induced by environmental stress and are called as stress or heat shock proteins. Here, we summarize the current knowledge about protein damage in aged organisms, about changes in proteolytic degradation, chaperone expression and function in the aging process, as well as the involvement of chaperones in longevity and cellular senescence. The role of chaperones in aging diseases, such as in Alzheimer’s disease, Parkinson’s disease, Huntington’s disease and in other neurodegenerative diseases as well as in atherosclerosis and in cancer is discussed. We also describe how the balance between chaperone requirement and availability becomes disturbed in aged organisms, or in other words, how chaperone overload develops. The consequences of chaperone overload are also outlined together with several new research strategies to assess the functional status of chaperones in the aging process.

Molecular chaperones Chaperones are ubiquitous, highly conserved proteins (Hartl, 1996), either assisting in the folding of newly synthesized or damaged proteins in an ATP-dependent active process or working in an ATP-independent passive mode sequestering damaged proteins for future refolding or digestion. Environmental stress leads to proteotoxic damage. Damaged, misfolded proteins bind to chaperones, and liberate the heat shock factor (HSF) from its chaperone complexes. HSF is activated and transcription of chaperone genes takes place (Morimoto, 2002). Most chaperones, therefore, are also called stress or (after the archetype of experimental stress) heat shock proteins (Hsp-s).

Aging proteins—proteins of aging organisms During the life-span of a stable protein, various posttranslational modifications occur including backbone and side chain oxidation, glycation, etc. In aging organisms, the disturbed cellular homeostasis leads to an increased rate of protein modification: in an 80-year old human, half of all proteins may become oxidized (Stadtman and Berlett, 1998). Susceptibility to various proteotoxic damages is mainly increased due to dysfunction of mitochondrial oxidation of starving yeast cells (Aguilaniu et al., 2001). In prokaryotes, translational errors result in folding defects and subsequent protein oxidation (Dukan et al., 2000), which predominantly takes place in growth arrested cells (Ballesteros et al., 2001). Additionally, damaged signalling networks loose their original stringency, and irregular protein phosphorylation occurs (e.g.: the Parkinson disease-related a-synuclein also becomes phosphorylated, leading to misfolding and aggregation; Neumann et al., 2002).

Aging protein degradation Irreversibly damaged proteins are recognized by chaperones, and targeted for degradation. Proteasome level and function decreases with aging, and some oxidized, aggregated proteins exert a direct inhibition on proteasome activity. Chaperones also aid in lysosomal degradation. The proteolytic changes are comprehensively reviewed by Szweda et al. (2002). Due to the degradation defects, damaged proteins accumulate in the cells of aged organisms, and by aggregation may cause a variety of protein folding diseases (reviewed by So˝ti and Csermely, 2002a).

Aging chaperones I: defects in chaperone induction Damaged proteins compete with the HSF in binding to the Hsp90-based cytosolic chaperone complex, which may contribute to the generally observed constitutively elevated chaperone levels in aged organisms (Zou et al., 1998; So˝ti and Csermely, 2002b). On the contrary, the majority of the reports showed that stress-induced synthesis of chaperones is impaired in aged animals. While HSF activation does not change, DNA binding activity may be reduced during aging (Heydari et al., 2000). A number of signaling events use an overlapping network of chaperones not only to establish the activation-competent state of different transcription factors (e.g. steroid receptors), but also as important elements in the attenuation of respective responses. HSF transcriptional activity is also negatively influenced by higher levels of chaperones (Morimoto, 2002). Differential changes of these proteins in various organisms and tissues may lead to different extents of (dys)regulation. More importantly, the cross-talk between different signalling pathways through a shared pool of chaperones may have severe consequences during aging when the cellular conformational homeostasis is deranged (see below).

Aging chaperones II: defects in chaperone function   Direct studies on chaperone function in aged organisms are largely restricted to a-crystallin having a decreased activity in aged human lenses (Cherian and Abraham, 1995; Cherian-Shaw et al., 1999). In a recent study, an initial test of passive chaperone function of whole cytosols was assessed showing a decreased chaperone capacity in aged rats compared to those of young counterparts (Nardai et al., 2002). What can be the mechanism behind these deleterious changes in chaperone function? Chaperones may also be prone to oxidative damage, as GroEL is preferentially oxidized in growth-arrested E. coli (Dukan and Nystro¨m, 1999). Macario and Conway de Macario (2002) raised the idea of ‘sick chaperones’ in aged organisms in a recent review. Indeed, chaperones are interacting with a plethora of other proteins (Csermely, 2001a), which requires rather extensive binding surfaces. These exposed areas may make chaperones a preferential target for proteotoxic damage: chaperones may behave as ‘suicide proteins’ during aging, sacrificing themselves instead of ‘normal’ proteins. The high abundance of chaperones (which may constitute more than 5% of cellular proteins), and their increased constitutive expression in aged organisms makes them a good candidate for this ‘altruistic courtesy.’ It may be especially true for mitochondrial Hsp60, the role of which would deserve extensive studies.

Aging chaperones III: defects in capacity, the chaperone overload Another possible reason of decreased chaperone function is chaperone overload (Csermely, 2001b). In aging organisms, the balance between misfolded proteins and available free chaperones is grossly disturbed: increased protein damage, protein degradation defects increase the amount of misfolded proteins, while chaperone damage, inadequate synthesis of molecular chaperones and irreparable folding defects (due to posttranslational changes) decrease the amount of available free chaperones. Chaperone overload occurs, where the need for chaperones may greatly exceed the available chaperone capacity (Fig. 1). Under these conditions, the competition for available chaperones becomes fierce and the abundance of damaged proteins may disrupt the folding assistance to other chaperone targets, such as: (1) newly synthesized proteins; (2) ‘constantly damaged’ (mutant) proteins; and (3) constituents of the cytoarchitecture (Csermely, 2001a). This may cause defects in signal transduction, protein transport, immune recognition, cellular organization as well as the appearance of previously buffered, hidden mutations in the phenotype of the cell (Csermely, 2001b). Chaperone overload may significantly decrease the robustness of cellular networks, as well as shift their function towards a more stochastic behavior. As a result of this, aging cells become more disorganized, their adaptation is impaired.

Fig. 1. Chaperone overload: a shift in the balance between misfolded proteins and available free chaperones in aging organisms. The accumulation of chaperone substrates along with an impaired chaperone function may exhaust the folding assistance to specific chaperone targets and leads to deterioration in vital processes. Chaperone overload may significantly decrease the robustness of cellular networks, and compromise the adaptative responses. See text for details.

Senescent cells and chaperones The involvement of chaperones in aging at the cellular level is recently reviewed (So˝ti et al., 2003). Non-dividingsenescent-peripheral cells tend to have increased chaperone levels (Verbeke et al., 2001), and cannot preserve the induction of several chaperones (Liu et al., 1989), similarly to cells from aged animals. Activation and binding of HSF to the heat shock element is decreased in aged cells (Choi et al., 1990). Interestingly, cellular senescence seems to unmask a proteasomal activity leading to the degradation of HSF (Bonelli et al., 2001). Chaperone induction per se seems to counteract senescence. Repeated mild heat shock (a kind of hormesis) has been reported to delay fibroblast aging (Verbeke et al., 2001), though it does not seem to extend replicative lifespan. A major chaperone, Hsp90 is required for the correct function of telomerase, an important enzyme to extend the life-span of cells (Holt et al., 1999). Mortalin (mtHsp70/Grp75), a member of the Hsp70 family, produces opposing phenotypic effects related to its localization. In normal cells, it is pancytoplasmically distributed, and its expression causes senescence. Its upregulation and perinuclear distribution, however, is connected to transformation, probably via p53 inactivation. Mortalin also induces life-span extension in human fibroblasts or in C. elegans harboring extra copies of the orthologous gene (Kaul et al., 2002).

Aging organisms and chaperones: age-related diseases Unbalanced chaperone requirement and chaperone capacity in aged organisms helps the accumulation of aggregated proteins, which often cause folding diseases, mostly of the nervous system, due to the very limited proliferation potential of neurons. Over expression of chaperones often delays the onset or diminishes the symptoms of the disease (So˝ti and Csermely, 2002b). Other aging diseases, such as atherosclerosis and cancer are also related to chaperone action. Here space limitation precludes a detailed description of these rapidly developing fields, however, numerous recent reviews were published on these subjects, where the interested readers may find a good summary and several hints for further readings (Ferreira and Carlos, 2002; Neckers, 2002; Sarto et al., 2000; Wick and Xu, 1999).

 

Chaperones and Longevity

Increased chaperone induction leads to increased longevity (Tatar et al., 1997). Moreover, a close correlation exists between stress resistance and longevity in several long-lived C. elegans and Drosophila mutants (Lithgow and Kirkwood, 1996). As the other side of the same coin, damaged HSF has been found as an important gene to cause accelerated aging in C. elegans (Garigan et al., 2002). Caloric restriction, the only effective experimental manipulation known to retard aging in rodents and primates (Ramsey et al., 2000), restores age-impaired chaperone induction, while reversing the age-induced changes in constitutive Hsp levels (see So˝ti and Csermely, 2002a,b). These examples confirm the hypothesis that a better adaptation capacity to various stresses greatly increases the chances to reach longevity. 10. Conclusions and perspectives Aging can be defined as a multicausal process leading to a gradual decay of self-defensive mechanisms, and an exponential accumulation of damage at the molecular, cellular and organismal level. The protein oxidation, damage, misfolding and aggregation together with the simultaneously impaired function and induction of chaperones in aged organisms disturb the balance between chaperone requirement and availability. There are several important aspects for future investigation of this field: † the measurement of active chaperone function (i.e. chaperone-assisted refolding of damaged proteins) in cellular extracts does not have a well-established method yet; † we have no methods to measure free chaperone levels; † among the consequences of chaperone overload, changes in signal transduction, protein transport, immune recognition and cellular organization have not been systematically measured and/or related to the protein folding homeostasis of aging organisms and cells.

 

  1. Extracellular HSPs in inflammation and immunity

Cutting Edge: Heat Shock Protein (HSP) 60 Activates the Innate Immune Response: CD14 Is an Essential Receptor for HSP60 Activation of Mononuclear Cells1

Amir Kol,* Andrew H. Lichtman,† Robert W. Finberg,‡ Peter Libby,*† and Evelyn A. Kurt-Jones2‡
J  Immunol 2000; 164: 13–17.  https://www.researchgate.net/profile/Robert_Finberg/publication/12696457_Cutting_Edge_Heat_Shock_Protein_(HSP)_60_Activates_the_Innate_Immune_Response_CD14_Is_an_Essential_Receptor_for_HSP60_Activation_of_Mononuclear_Cells/links/53ee00460cf23733e80b21c0.pdf

Heat shock proteins (HSP), highly conserved across species, are generally viewed as intracellular proteins thought to serve protective functions against infection and cellular stress. Recently, we have reported the surprising finding that human and chlamydial HSP60, both present in human atheroma, can activate vascular cells and macrophages. However, the transmembrane signaling pathways by which extracellular HSP60 may activate cells remains unclear. CD14, the monocyte receptor for LPS, binds numerous microbial products and can mediate activation of monocytes/macrophages and endothelial cells, thus promoting the innate immune response. We show here that human HSP60 activates human PBMC and monocyte-derived macrophages through CD14 signaling and p38 mitogen-activated protein kinase, sharing this pathway with bacterial LPS. These findings provide further insight into the molecular mechanisms by which extracellular HSP may participate in atherosclerosis and other inflammatory disorders by activating the innate immune system.

There is increasing interest in the role of nontraditional mediators of inflammation in atherosclerosis (1). Recent studies from our laboratory have shown that chlamydial and human heat shock protein 60 (HSP60)3 colocalize in human atheroma (2), and either HSP60 induces adhesion molecule and cytokine production by human vascular cells and macrophages, in a pattern similar to that induced by Escherichia coli LPS (3, 4). These results suggested that HSP60 and LPS might share similar signaling mechanisms. CD14 is the major high-affinity receptor for bacterial LPS on the cell membrane of mononuclear cells and macrophages (5, 6). In addition to LPS, CD14 functions as a signaling receptor for other microbial products, including peptidoglycan from Gram-positive bacteria and mycobacterial lipoarabinomann (7, 8). CD14 is considered a pattern recognition receptor for microbial Ags and, with Toll-like receptor (TLR) proteins, an important mediator of innate immune responses to infection (9–14). We have examined the role of CD14 in the response of human monocytes and macrophages to HSP60.  …..

HSP may play a central role in the innate immune response to microbial infections. Because both microbes and stressed or injured host cells produce abundant HSP (36), and dying cells likely release these proteins, it is conceivable that HSP furnish signals that inform the innate immune system of the presence of infection and cell damage. The findings reported here, that human HSP60 induces IL-6 production by mononuclear cells and macrophages via the CD14, supports this hypothesis, suggesting that human HSP60 may act together with LPS or other microbial products to provoke innate immune responses.

Inflammation and immunity can contribute to the pathogenesis and complications of atherosclerosis (37). Moreover, the search for novel risk factors for atherosclerosis has revived the concept that microbial products might substantially contribute to the inflammatory reaction in the atheromatous vessel wall (38, 39). We have shown that chlamydial HSP60 colocalizes with human HSP60 in the macrophages of human atheroma (2). Therefore, bacterial and human HSP60, released from dying or injured cells during atherogenesis (40) or myocardial injury (41), may further promote local inflammation and possibly activate the innate immune system. Previous reports that immunization with mycobacterial HSP65 enhances atheroma formation in rabbits (42), have suggested an important role for HSPs in atherogenesis, particularly because the high degree of homology between HSPs of the same m.w. among different species might stimulate autoimmunity (43).

In conclusion, our findings, that CD14 mediates cellular activation induced by human HSP60 provide further insight into the molecular mechanisms by which HSP may activate the innate immune system and participate in atherogenesis and other inflammatory disorders.

DAMPs, PAMPs and alarmins: all we need to know about danger

Marco E. Bianchi1
J. Leukoc. Biol. 81: 1–5; 2007.   http://aerozon.ru/documents/publications/37_Bianche.pdf

Multicellular animals detect pathogens via a set of receptors that recognize pathogen associated molecular patterns (PAMPs). However, pathogens are not the only causative agents of tissue and cell damage: trauma is another one. Evidence is accumulating that trauma and its associated tissue damage are recognized at the cell level via receptor-mediated detection of intracellular proteins released by the dead cells. The term “alarmin” is proposed to categorize such endogenous molecules that signal tissue and cell damage. Intriguingly, effector cells of innate and adaptive immunity can secrete alarmins via nonclassical pathways and often do so when they are activated by PAMPs or other alarmins. Endogenous alarmins and exogenous PAMPs therefore convey a similar message and elicit similar responses; they can be considered subgroups of a larger set, the damage associated molecular patterns (DAMPs).

Multicellular animals must distinguish whether their cells are alive or dead and detect when microorganisms intrude, and have evolved surveillance/defense/repair mechanisms to this end. How these mechanisms are activated and orchestrated is still incompletely understood, and I will argue that that these themes define a unitary field of investigation, of both basic and medical interest.

A complete system for the detection, containment, and repair of damage caused to cells in the organism requires warning signals, cells to respond to them via receptors and signaling pathways, and outputs in the form of physiological responses. Classically, a subset of this system has been recognized and studied in a coherent form: pathogen-associated molecular patterns (PAMPs) are a diverse set of microbial molecules which share a number of different recognizable biochemical features (entire molecules or, more often, part of molecules or polymeric assemblages) that alert the organism to intruding pathogens [1]. Such exogenous PAMPs are recognized by cells of the innate and acquired immunity system, primarily through toll-like receptors (TLRs), which activate several signaling pathways, among which NF-kB is the most distinctive. As a result, some cells are activated to destroy the pathogen and/or pathogen-infected cells, and an immunological response is triggered in order to produce and select specific T cell receptors and antibodies that are best suited to recognize the pathogen on a future occasion. Most of the responses triggered by PAMPs fall into the general categories of inflammation and immunity.

However, pathogens are not the only causative agents of tissue and cell damage: trauma is another one. Tissues can be ripped, squashed, or wounded by mechanical forces, like falling rocks or simply the impact of one’s own body hitting the ground. Animals can be wounded by predators. In addition, tissues can be damaged by excessive heat (burns), cold, chemical insults (strong acids or bases, or a number of different cytotoxic poisons), radiation, or the withdrawal of oxygen and/or nutrients. Finally, humans can also be damaged by specially designed drugs, such as chemotherapeutics, that are meant to kill their tumor cells with preference over their healthy cells. Very likely, we would not be here to discuss these issues if evolution had not incorporated in our genetic program ways to deal with these damages, which are not caused by pathogens but are nonetheless real and common enough. Tellingly, inflammation is also activated by these types of insults. A frequently quoted reason for the similarity of the responses evoked by pathogens and trauma is that pathogens can easily breach wounds, and infection often follows trauma; thus, it is generally effective to respond to trauma as if pathogens were present. In my opinion, an additional reason is that pathogens and trauma both cause tissue and cell damage and thus trigger similar responses.

None of these considerations is new; however, a new awareness of the close relationship between trauma- and pathogenevoked responses emerged from the EMBO Workshop on Innate Danger Signals and HMGB1, which was held in February 2006 in Milano (Italy); many of the findings presented at the meeting are published in this issue of the Journal of Leukocyte Biology. At the end of the meeting, Joost Oppenheim proposed the term “alarmin” to differentiate the endogenous molecules that signal tissue and cell damage. Together, alarmins and PAMPs therefore constitute the larger family of damage-associated molecular patterns, or DAMPs.

Extranuclear expression of HMGB1 has been involved in a number of pathogenic conditions: sepsis [44], arthritis [45, 46], atherosclerosis [10], systemic lupus erythematosus (SLE) [47], cancer [48] and hepatitis [49, this issue]. Uric acid has been known to be the aethiologic agent for gout since the 19th century. S100s may be involved in arthritis [31, this issue] and psoriasis [50]. However, although it is clear that excessive alarmin expression might lead to acute and chronic diseases, the molecular mechanisms underlying these effects are still largely unexplored.

The short list of alarmins presented above is certainly both provisional and incomplete and serves only as an introduction to the alarmin concept and to the papers published in this issue of JLB. Other molecules may be added to the list, including cathelicidins, defensins and eosinophil-derived neurotoxin (EDN) [51], galectins [52], thymosins [53], nucleolin [54], and annexins [55; and 56, this issue]; more will emerge with time. Eventually, the concept will have to be revised and adjusted to the growing information. Indeed, I have previously argued that any misplaced protein in the cell can signal damage [57], and Polly Matzinger has proposed that any hydrophobic surface (“Hyppo”, or Hydrophobic protein part) might act as a DAMP [58]. As most concepts in biology, the alarmin category serves for our understanding and does not correspond to a blueprint or a plan in the construction of organisms. Biology proceeds via evolution, and evolution is a tinkerer or bricoleur, finding new functions for old molecules. In this, the reuse of cellular components as signals for alerting cells to respond to damage and danger, is a prime example.

 

  1. Role of heat shock and the heat shock response in immunity and cancer

 

Heat Shock Proteins: Conditional Mediators of Inflammation in Tumor Immunity

Stuart K. Calderwood,1,* Ayesha Murshid,1 and Jianlin Gong1
Front Immunol. 2012; 3: 75.  doi:  10.3389/fimmu.2012.00075

Heat shock protein (HSP)-based anticancer vaccines have undergone successful preclinical testing and are now entering clinical trial. Questions still remain, however regarding the immunological properties of HSPs. It is now accepted that many of the HSPs participate in tumor immunity, at least in part by chaperoning tumor antigenic peptides, introducing them into antigen presenting cells such as dendritic cells (DC) that display the antigens on MHC class I molecules on the cell surface and stimulate cytotoxic lymphocytes (CTL). However, in order for activated CD8+ T cells to function as effective CTL and kill tumor cells, additional signals must be induced to obtain a sturdy CTL response. These include the expression of co-stimulatory molecules on the DC surface and inflammatory events that can induce immunogenic cytokine cascades. That such events occur is indicated by the ability of Hsp70 vaccines to induce antitumor immunity and overcome tolerance to tumor antigens such as mucin1. Secondary activation of CTL can be induced by inflammatory signaling through Toll-like receptors and/or by interaction of antigen-activated T helper cells with the APC. We will discuss the role of the inflammatory properties of HSPs in tumor immunity and the potential role of HSPs in activating T helper cells and DC licensing.

Heat shock protein, vaccine, inflammation, antigen presentation

Heat shock proteins (HSP) were first discovered as a group of polypeptides whose level of expression increases to dominate the cellular proteome after stress (Lindquist and Craig, 1988). These increases in HSPs synthesis correlate with a marked resistance to potentially toxic stresses such as heat shock (Li and Werb,1982). The finding that such proteins have extracellular immune functions suggested that, as highly abundant intracellular proteins they could be prime candidates as danger signals to the immune response (Srivastava and Amato,2001). There are several human HSP gene families with known immune significance and their classification is reviewed in Kampinga et al. (2009). These include the HSPA (Hsp70) family, which includes the HPA1A and HSPA1B genes encoding the two major stress-inducible Hsp70s, that together are often referred to as Hsp72. When referring to Hsp70 in this chapter, we generally refer to the products of these two genes. The Hsp70 family also includes two other members with immune function – HSPA8 and HSPA5 genes, whose protein products are known as Hsc70 the major constitutive Hsp70 family member and Grp78, a key ER-resident protein. In addition two more Hsp70 related genes have immune significance and these include HSPH2 (Hsp110) and HSPH4 the ER-resident class H protein Grp170. The Hsp90 family also has major functions in tumor immunity and these include HSPC2 and HSPC3, which encode the major cytoplasmic proteins Hsp90a and Hsp90b, and HSPC4 that encodes ER chaperone Grp94. In addition, the product of the HSPD1 gene, the mitochondrial chaperone Hsp60 has some immunological functions. Mice have been shown to encode orthologs of each of these genes (Kampinga et al., 2009).

It has been suggested that many of the HSPs have the property of damage associated molecular patterns (DAMPs), inducers of sterile inflammation and innate immunity (Kono and Rock, 2008). The additional discovery that intracellular HSPs function as molecular chaperones and can bind to a wide spectrum of intracellular polypeptides further indicated that they could play a broad role in the immune response and might mediate both innate immunity due to their status as DAMPs and adaptive immunity by chaperoning antigens.

Heat shock proteins are currently employed as vaccines in cancer immunotherapy (Tamura et al., 1997; Murshid et al., 2011a). The rationale behind the approach is that if HSPs can be extracted from tumor tissue bound to the polypeptides which they chaperone during normal metabolism, they may retain antigenic peptides specific to the tumor (Noessner et al., 2002; Srivastava, 2002; Wang et al., 2003; Enomoto et al., 2006; Gong et al., 2010). Indeed, vaccines based on Hsp70, Hsp90, Grp94, Hsp110, and Grp170 polypeptide complexes have been used successfully to immunize mice to a range of tumor types and Hsp70 and Grp94 vaccines have undergone recent clinical trials (rev: Murshid et al., 2011a). These effects of the HSP vaccines on tumor immunity appear to be mediated largely to the associated, co-isolated tumor polypeptides, although in the case of Grp94 this question is still controversial and tumor regression was observed in mice treated with the chaperone devoid of its peptide binding domain (Udono and Srivastava, 1993; Srivastava, 2002; Nicchitta, 2003; Chandawarkar et al., 2004; Nicchitta et al.,2004). Use of such HSP vaccines is potentially a powerful approach to tumor immunotherapy as the majority of the antigenic repertoire of most individual tumor cells is unknown (Srivastava and Old, 1988; Srivastava, 1996). Individual cancer cells are likely to take a lone path in accumulating a spectrum of random mutations. Although some mutations are functional, permitting cells to become transformed and to progress into a highly malignant state, many such changes are likely to be passenger mutations not required to drive tumor growth (Srivastava and Old, 1988; Srivastava, 1996). Some of these individual mutant sequences will be novel antigenic epitopes and together with the few known shared tumor antigens comprise an “antigenic fingerprint” for each individual tumor (Srivastava,1996). Accumulation of mutations in cancer appears to be related to, and may drive the increases in HSPs observed in many tumors (Kamal et al., 2003; Whitesell and Lindquist, 2005; Trepel et al., 2010). As the mutant conformations of tumor proteins are “locked in” due to the covalent nature of the alterations, cancer cells appear to be under permanent proteotoxic stress and rich in HSP expression (Ciocca and Calderwood, 2005). For tumor immunology these conditions may offer a therapeutic opportunity as individual HSPs, whose expression is expanded in cancer will chaperone a cross-section of the “antigenic fingerprint” of the individual tumors (Murshid et al., 2011a). This approach was first utilized by Srivastava (20002006) and led to the development of immunotherapy using HSP–peptide complexes.

In addition to using HSP–peptide complexes extracted from tumors, in cases where tumor antigens are known, these can be directly loaded onto purified or recombinant HSPs and the complex used as a vaccine. This procedure has been carried out successfully in the case of the “large HSPs,” Hsp110 and Grp170 (Manjili et al., 20022003). A variant of this approach employs the molecular engineering of tumor antigens in order to produce molecular chaperone-fusion genes which encode products in which the HSP is fused covalently to the antigen. The fusion proteins are then employed as vaccines. This approach was pioneered by Young et al. who showed that a fusion between mycobacterial Hsp70 and ovalbumin could induced cytotoxic lymphocytes (CTL) in mice with the capacity to kill Ova-expressing cancer cells (Suzue et al., 1997). The vaccines could be used effectively without adjuvant and adjuvant properties were ascribed to the molecular chaperone component of the fusion protein. Subsequent studies have confirmed the utility of the approach in targeting common tumor antigens such as the melanoma antigen Mage3 (Wang et al., 2009).

HSPs and Immunosurveillance in Cancer

The question next arises as to the role of endogenous HSPs, with or without bound antigens in immunosurveillance of cancer cells. Although the immune system can recognize tumor antigens and generate a CTL response, most cancers evade immune cell killing by a range of strategies (van der Bruggen et al., 1991; Pardoll,2003). These include the down-regulation of surface MHC class I molecules by individual tumor cells and release of immunosuppressive IL-10 by tumors (Moller and Hammerling, 1992; Chouaib et al., 2002). Tumors in vivo also appear to attract a range of hematopoietic cells with immunosuppressive action including regulatory CD4+CD25+FoxP3+ T cells (Treg), M2 macrophages, myeloid-derived suppressor cells (MDSC) and some classes of natural killer cells (Pekarek et al.,1995; Terabe et al., 2005; Mantovani et al., 2008; Marigo et al., 2008). The tumor milieu also contain a small fraction of cells of mesenchymal origin identified by surface fibroblast activation protein-a (FAP cells) that suppress antitumor immune responses (Kraman et al., 2010). Endogenous tumor HSPs may also participate in immune suppression. Although the majority of the HSPs function as intracellular molecular chaperones, a fraction of these proteins can be released from cells even under unstressed conditions and may participate in immune functions (rev: Murshid and Calderwood, 2012). Intracellular Hsp70 can be actively secreted from tumor cells in either free form or packaged into lipid-bounded structures called exosomes (Mambula and Calderwood, 2006b; Chalmin et al., 2010). In addition Hsp70 and Hsp90 can also be found associated with the surfaces of tumor cells where they can function as molecular chaperones or as recognition structures for immune cells (Sidera et al., 2008; Qin et al., 2010; Multhoff and Hightower, 2011). As Hsp70 was shown in a number of earlier studies to be pro-inflammatory due to its interaction with pattern recognition receptors such as Toll-like receptors 2 and 4 (TLR2 and TLR4), these findings might suggest, as mentioned above, that Hsp70 released by tumors could be pro-inflammatory and possess the properties of DAMPs (Asea et al., 20002002; Vabulas et al., 2002). However, subsequent studies indicated that a portion of the TLR4 activation detected in the earlier reports, involving exposure of monocytes, macrophages, or dendritic cells (DC) to HSPs in vitro may be due to trace contamination with bacterial pathogen associated molecular patterns (PAMPs), potent TLR activators (Tsan and Gao,2004). In spite of these drawbacks, an overwhelming amount of evidence now seems to indicate the interaction of Hsp70 and other HSPs with TLRs (particularly TLR4) in vivo – in a wide range of physiological and pathological conditions, leading to acute inflammation in many conditions (Chase et al., 2007; Wheeler et al., 2009; see Appendix for a full list of references). Thus both TLR2 and TLR4 appear to be important components of inflammatory responses to Hsp70 under many pathophysiological conditions. In cancer therapy it has been shown that autoimmunity can be triggered in mice through necrotic killing of melanocytes engineered to overexpress Hsp70; such treatment led to the concomitant immune destruction of B16 melanoma tumors that share patterns of antigen expression with the killed melanocytes (Sanchez-Perez et al., 2006). Hsp70 appears to play an adjuvant role in this form of therapy through its interaction with TLR4 and induction of the cytokine TNF-a (Sanchez-Perez et al., 2006). However, despite these findings it has also been shown that depletion of Hsp70 in cancer cells can, in the absence of other treatments lead to tumor regression by inducing antitumor immunity (Rerole et al., 2011). This effect appears to be due to the secretion by cancer cells of immunosuppressive exosomes containing Hsp70 that activate MDSC and lead to local immunosuppression (Chalmin et al., 2010). Under normal circumstances therefore, release of endogenous Hsp70 into the extracellular microenvironment may be a component of the tumor defenses against immunosurveillance. Extracellular Hsp60 has also been shown be immunomodulatory and can increase levels of FoxP3 Treg in vitro and suppress T cell-mediated immunity (de Kleer et al., 2010; Aalberse et al., 2011).

The pro-inflammatory properties of extracellular HSPs may be more evident underin vivo situations particularly in the context of tissue damage (Sanchez-Perez et al.,2006). For instance when elevated temperatures were used to boost Hsp70 release from Lewis Lung carcinoma cells in vivo, antitumor immunity was activated along with release of chemokines CCL2, CCL5, and CCL10, in a TLR4-dependent manner, leading to attraction of DC and T cells into the tumor (Chen et al., 2009). Thus under resting conditions, the tumor milieu appears to be a specialized immunosuppressive environment, rich in inhibitory cells such as Treg, MDSC, and M2 macrophages and inaccessible to “exhausted” CD8+ T cells that often fail to penetrate the tumor microcirculation. However, under inflammatory conditions involving necrotic cell killing of tumor cells, extracellular HSPs may be able to amplify the anticancer immune response, intracellular HSPs may be released to further increase such a response and CTL may triggered to penetrate the tumor milieu, inducing antigen-specific cancer cell killing (Evans et al., 2001; Mambula and Calderwood, 2006a; Sanchez-Perez et al., 2006; Chen et al., 2009).

 

HSP-Based Anticancer Vaccines

It is apparent that a number of HSP types, conjugated to peptide complexes (HSP.PC) from cancer cells form effective bases for immunotherapy approaches with unique properties, as mentioned above (Calderwood et al., 2008; Murshid et al., 2011a). The immunogenicity of most HSP.PC appears to involve the ability of the HSPs to sample the tumor “antigenic fingerprint,” deliver the antigens to antigen presenting cells (APC) such as DC and stimulate activation of CTL (Tamura et al., 1997; Singh-Jasuja et al., 2000b; Wang et al., 2003; Murshid et al.,2010). A number of studies show that HSPs can chaperone tumor antigens and deliver them to the appropriate destination – MHC class I molecules on the DC surface (Singh-Jasuja et al., 2000a,b; Srivastava and Amato, 2001; Delneste et al.,2002; Enomoto et al., 2006; Gong et al., 2009). In addition, Hsp70 has been shown to chaperone viral antigenic peptides and increase cross priming of human CTL under ex vivo conditions (Tischer et al., 2011). However, it is still far from clear how the process of HSP-mediated cross priming unfolds. For instance, the CD8+ expressing DC subpopulation in lymph nodes is regarded as the primary cross-presenting APC (Heath and Carbone, 2009). It is not however currently known whether the CD8+ DC subset or other peripheral or lymph-node resident, DC interact with HSP vaccines to induce cross presentation. HSPs appear to be able to enter APC, such as mouse bone marrow derived DC (BMDC) and human DC in a receptor-mediated manner (Basu et al., 2001; Delneste et al., 2002; Gong et al.,2009; Murshid et al., 2010). However, no unique endocytosing HSP receptor has emerged and HSP–antigen complexes appear instead to be taken up by proteins with “scavenger” function such as LOX-1, SRECI, and CD91 that can each take up a wide range of extracellular ligands (Basu et al., 2001; Delneste et al., 2002; Theriault et al., 2006; Murshid et al., 2010). A pathway for Hsp90–peptide (Hsp90.PC) uptake has been characterized in mouse BMDC by scavenger receptor SRECI (Murshid et al., 2010). SRECI is able to mediate the whole process of Hsp90.PC endocytosis, trafficking through the cytoplasm to the sites of antigen processing and presentation of antigens to CD8+ T lymphocytes on MHC class I molecules (Murshid et al., 2010). This process is known as antigen cross presentation (Kurts et al., 2010). It is not currently clear what the relative contribution to antigen cross presentation of the various HSP receptors might be under in vivo conditions. It may be that each receptor class contributes to an individual aspect of CTL activation by HSP peptide complexes although a definitive understanding may await studies in mice deficient in each receptor class.

 

HSPs and CTL Programming

It is evident that that HSPs can mediate antigen cross presentation and activate CD8+ T lymphocytes. However, presentation of tumor antigens by DC is not sufficient for CTL programming and, in the absence of co-stimulatory molecules and innate immunity, the “helpless” CD8+ cells will cease to proliferate abundantly and will most likely undergo apoptosis (Schurich et al., 2009; Kurts et al., 2010). One mechanism for enhancing CTL programming involves activation of the TLR pathways that lead to synthesis of co-stimulatory molecules (Rudd et al.,2009; Yamamoto and Takeda, 2010). The co-stimulatory molecules, including CD80 and CD86 then become expressed on the DC cell surface and amplify the signals induced by binding of the T cell receptor on CD8+ T cells to MHC class I peptide complexes on the presenting DC (Parra et al., 1995; Rudd et al., 2009). This process is important in pathogen infection in which microbially derived antigens are encountered in the presence of inflammatory PAMPs that can activate innate immune transcriptional networks. Originally it had been thought that HSPs could provide analogous stimulation through their suspected activity as DAMPs and their inbuilt ability to trigger innate immunity through TLR2 and TLR4 on DC (Asea et al., 20002002; Vabulas et al., 2002). (The potential role of HSPs as DAMPs has been the subject of a recent review: van Eden et al., 2012). Subsequent studies on the capacity of HSPs to bind TLRs do not indicate avid binding of Hsp70 to either TLR2 or TLR4 when expressed in cells deficient in HSP receptors in vitro (Theriault et al., 2006). In vivo however, TLR signaling is essential for Hsp70 vaccine-induced tumor cell killing. Studies of tumor-bearing mice treated with an Hsp70 vaccine in vivo indicated that vaccine function is depleted by knockout of the TLR signaling intermediate Myd88 and completely abrogated by double knockout of TLR2 and TLR4 (Gong et al., 2009). These findings were somewhat complicated by the fact that TLR4 is involved in upstream regulation of the expression of Hsp70 receptor SRECI, but do strongly implicate a role for these receptors in amplifying immune signaling by Hsp70 vaccines and Hsp70-based immunotherapy (Sanchez-Perez et al., 2006; Gong et al., 2009). It is still not clear to what degree HSPs are capable of providing a sturdy DC maturing signal through TLR2/TLR4. The potency of HSP anticancer vaccines could potentially be improved by addition of PAMPs such as CpG DNA shown to activate TLR9, or double stranded RNA that can activate TLR3 (Murshid et al., 2011a). As mentioned, one contradictory factor in the earlier studies was that, although TLR2 and TLR4 are required for a sturdy Hsp70 vaccine-mediated immune response, direct binding of Hsp70 to these receptors was not observed (Theriault et al., 2006; Gong et al., 2009; Murshid et al., 2012). A rationale for these findings might be that HSPs can activate TLR signaling indirectly through primary binding to established HSP receptors such as LOX-1 and SRECI which secondarily recruit and activate the TLRs (Murshid et al., 2011b). Both of these scavenger receptors bind to TLR2 upon stimulation and activate TLR2-based signaling (Jeannin et al., 2005; A. Murshid and SK Calderwood, in preparation). In addition, we have found that Hsp90–SRECI complexes move to the lipid raft compartment of the cell, an environment highly enriched in TLR2 and TLR4 (Triantafilou et al., 2002; Murshid et al., 2010).

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3342006/bin/fimmu-03-00075-g001.jpg

Heat shock protein–peptide complexes extracted from tumor cells interact with endocytosing receptors (HSP-R) such as SRECI or signaling receptors (TLR) such as TLR4 on DC. SREC1 mediates uptake and intracellular processing of antigens and the presentation of resulting peptides on surface MHC class I and MHC class II proteins. MHC class II receptor–peptide complexes then bind to T cell receptors on CD4+ cells. One consequence of binding is interaction of CD40 ligand on the MHC class II cell with CD40 on the DC leading to the licensing interaction that results in enhanced expression of co-stimulatory proteins on the DC cell surface. The licensed DC may then interact with CD8+ T cells through T cell interaction with MHC class I peptide complexes. This effect will be enhanced by simultaneous interaction of CD80 or Cd86 co-stimulatory complexes on the DC with CD28 on the CD8+ cells, leading to effective CD8+ CTL that can lyse tumor cells. T cell programming can also be amplified by signals emanating from activated TLR that can boost levels of CD80 and CD86 as well as inflammatory cytokines (not shown).

 

Hsp70, Cell Damage, and Inflammation

The question of whether Hsp70 acts as DAMP and could by itself induce an inflammatory response in cancer patients in vivo is still open. However, some recent studies by Vile et al. using a gene therapy approach may shed some light on the inflammatory role of Hsp70 in tumor therapy. In this approach, as mentioned above, normal murine tissues were engineered to express high Hsp70 levels then subjected to treatments that lead to necrotic killing. The aim was to stimulate an autoimmune response that could lead to bystander immune killing of tumor cells that share the antigenic repertoire as the killed normal cells (Sanchez-Perez et al.,2006). In the initial studies, normal melanocytes were preloaded with Hsp70 plasmids and then necrotic cell death was triggered (Daniels et al., 2004). This treatment led to T cell-mediated immune killing of syngeneic B16 melanoma cells transplanted at a distant site in the mouse, presumably in response to antigens shared by the killed normal melanocytes and melanoma cell (Daniels et al., 2004). This effect only occurred when melanocytes were induced to undergo necrosis and Hsp70 levels were elevated, indicating a role for high levels of Hsp70 in the tumor specific immune response. Interestingly, these conditions did not lead to a prolonged autoimmune response, an effect mediated by the induction of a delayed Treg response (Srivastava, 2003; Daniels et al., 2004). It is notable that some early studies of chaperone-based tumor vaccines in animal models demonstrated a primary CTL response to tumors in response to treatment followed by delayed activation of a Treg reaction, and that chaperone levels must be carefully titrated for effective induction of tumor immunity (Udono and Srivastava, 1993; Liu et al.,2009). The role of Hsp70 in autoimmune rejection of tumors was also investigated in prostate cancer (Kottke et al., 2007). Ablation of normal prostate cells by necrotic killing with fusogenic viruses in the absence of Hsp70 elevation led to the induction of the cytokines IL-10 and TGF-b in the mouse prostate and a Treg response. However, when Hsp70 levels were elevated in these cells, IL-10, TGF-b, and IL-6 were induced simultaneously, the IL-6 component leading to further induction of IL-17, a profound Th17 response and tumor rejection (Kottke et al.,2007). Thus elevated levels of Hsp70, presumably released from cells undergoing necrosis can influence the local cytokine patterns and lead to an inflammatory statein vivo. Interestingly, these results seem to be tissue specific as inflammatory killing of pancreatic cells even in the presence of elevated Hsp70 did not provoke IL-6 release, a Th17 response or tumor rejection and the Treg response dominated under these conditions (Kottke et al., 2009). Thus the role of Hsp70 in tissue inflammation and tumor rejection seems to require elevated concentrations of extracellular chaperones, significant levels of necrotic cell killing, and tissue specific cytokine release.

Conclusion

  • Earlier studies investigating HSP vaccines considered such structures to be the “Swiss penknives” of immunology able to deliver antigens directly to APC and confer a maturing signal that could render DC able to effectively program CTL (Srivastava and Amato, 2001; Noessner et al., 2002). It is well established now that Hsp70, Hsp90, Hsp110, and GRP170 can chaperone tumor antigens and activate antigen cross presentation (Murshid et al., 2011a). In addition, HSPs were thought to be DAMPs with ability to strongly activate TLR signaling and innate immunity (Asea et al., 2000). However, although there is compelling evidence to indicate that Hsp70, for instance can interact with TLR4 under a number of pathological situations (see Appendix, Sanchez-Perez et al., 2006), it remains unclear whether free Hsp70 binds directly to the Toll-like receptor and induces innate immunity in the absence of other treatments in vitro(Tsan and Gao, 2004).
  • Elevated levels of extracellular HSPs appear to have the capacity to amplify the effects of inflammatory signals emanating from necrotic cells in vivoin a TLR4-dependent manner (Daniels et al., 2004; Sanchez-Perez et al., 2006; Kottke et al., 2007). In the presence of cell injury and death, elevated levels of Hsp70 appear to increase the production of inflammatory signals that involve cytokines such as IL-6 and IL-17 and lead to a specific T cell-mediated immune response to tumor cells sharing antigens with the dying cells (Kottke et al., 2007). The mechanisms involved in these processes are not clear although one possibility is that HSPs can induce the engulfment of necrotic cells. Hsp70 has been shown to increase bystander engulfment of a variety of structures (Wang et al., 2006a,b). In addition, tumor cells treated with elevated temperatures release inflammatory chemokines in an Hsp70 and TLR4-dependent mechanisms and this effect may be significant in CTL programming and tumor cell killing (Chen et al., 2009). Our studies indicate that CTL induction by Hsp70 vaccines in vivo has an absolute requirement for TLR2 and TLR4 suggesting that at least in vivo HSPs can trigger innate immunity through TLR signaling (Gong et al., 2009).
  • HSPs appear also to be able to direct antigen presentation through the class II pathway in DC and may stimulate T helper cells (Gong et al., 2009). It may thus be possible that HSPs participate in DC licensing and reinforce CTL programming during exposure to HSP vaccines. Future studies will address these questions.
  • A further interesting consideration is whether HSPs released from untreated tumor cells enhance or depress tumor immunity. One initial study shows that Hsp70 released from tumor cells in exosomes can strongly decrease tumor immunity through effects on MDSC (Chalmin et al., 2010). Further studies will be required to make a definitive statement on these questions.

 

  1. Protein aggregation disorders and HSP expression

Chaperone suppression of aggregation and altered subcellular proteasome localization imply protein misfolding in SCA1

Christopher J. Cummings1,5, Michael A. Mancini3, Barbara Antalffy4, Donald B. DeFranco7, Harry T. Orr8 & Huda Y. Zoghbi1,2,6
Nature Genetics 19, 148 – 154 (1998) http://dx.doi.org:/10.1038/502

Spinocerebellar ataxia type 1 (SCA1) is an autosomal dominant neurodegenerative disorder caused by expansion of a polyglutamine tract in ataxin-1. In affected neurons of SCA1 patients and transgenic mice, mutant ataxin-1 accumulates in a single, ubiquitin-positive nuclear inclusion. In this study, we show that these inclusions stain positively for the 20S proteasome and the molecular chaperone HDJ-2/HSDJ. Similarly, HeLa cells transfected with mutant ataxin-1 develop nuclear aggregates which colocalize with the 20S proteasome and endogenous HDJ-2/HSDJ. Overexpression of wild-type HDJ-2/HSDJ in HeLa cells decreases the frequency of ataxin-1 aggregation. These data suggest that protein misfolding is responsible for the nuclear aggregates seen in SCA1, and that overexpression of a DnaJ chaperone promotes the recognition of a misfolded polyglutamine repeat protein, allowing its refolding and/or ubiquitin-dependent degradation.

Effects of heat shock, heat shock protein 40 (HDJ-2), and proteasome inhibition on protein aggregation in cellular models of Huntington’s disease

Andreas Wyttenbach, Jenny Carmichael, Jina Swartz, Robert A. Furlong, Yolanda Narain, Julia Rankin, and David C. Rubinsztein*
https://www.researchgate.net/profile/David_Rubinsztein/publication/24447892_Effects_of_heat_shock_heat_shock_protein_40_(HDJ2)_and_proteasome_inhibition_on_protein_aggregation_in_cellular_models_of_Huntington’s_disease/links/00b7d528b80aab69bb000000.pdf

Huntington’s disease (HD), spinocerebellar ataxias types 1 and 3 (SCA1, SCA3), and spinobulbar muscular atrophy (SBMA) are caused by CAGypolyglutamine expansion mutations. A feature of these diseases is ubiquitinated intraneuronal inclusions derived from the mutant proteins, which colocalize with heat shock proteins (HSPs) in SCA1 and SBMA and proteasomal components in SCA1, SCA3, and SBMA. Previous studies suggested that HSPs might protect against inclusion formation, because overexpression of HDJ-2yHSDJ (a human HSP40 homologue) reduced ataxin-1 (SCA1) and androgen receptor (SBMA) aggregate formation in HeLa cells. We investigated these phenomena by transiently transfecting part of huntingtin exon 1 in COS-7, PC12, and SH-SY5Y cells. Inclusion formation was not seen with constructs expressing 23 glutamines but was repeat length and time dependent for mutant constructs with 43–74 repeats. HSP70, HSP40, the 20S proteasome and ubiquitin colocalized with inclusions. Treatment with heat shock and lactacystin, a proteasome inhibitor, increased the proportion of mutant huntingtin exon 1-expressing cells with inclusions. Thus, inclusion formation may be enhanced in polyglutamine diseases, if the pathological process results in proteasome inhibition or a heat-shock response. Overexpression of HDJ-2yHSDJ did not modify inclusion formation in PC12 and SH-SY5Y cells but increased inclusion formation in COS-7 cells. To our knowledge, this is the first report of an HSP increasing aggregation of an abnormally folded protein in mammalian cells and expands the current understanding of the roles of HDJ-2yHSDJ in protein folding.

 

  1. Hsp70 in blood cell differentiation.

 

Apoptosis Versus Cell Differentiation -Role of Heat Shock Proteins HSP90, HSP70 and HSP27

David Lanneau, Aurelie de Thonel, Sebastien Maurel, Celine Didelot, and Carmen Garrido
Prion. 2007 Jan-Mar; 1(1): 53–60.  http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2633709/

Heat shock proteins HSP27, HSP70 and HSP90 are molecular chaperones whose expression is increased after many different types of stress. They have a protective function helping the cell to cope with lethal conditions. The cytoprotective function of HSPs is largely explained by their anti-apoptotic function. HSPs have been shown to interact with different key apoptotic proteins. As a result, HSPs can block essentially all apoptotic pathways, most of them involving the activation of cystein proteases called caspases. Apoptosis and differentiation are physiological processes that share many common features, for instance, chromatin condensation and the activation of caspases are frequently observed. It is, therefore, not surprising that many recent reports imply HSPs in the differentiation process. This review will comment on the role of HSP90, HSP70 and HSP27 in apoptosis and cell differentiation. HSPs may determine de fate of the cells by orchestrating the decision of apoptosis versus differentiation.

Key Words: apoptosis, differentiation, heat shock proteins, chaperones, cancer cells, anticancer drugs

Go to:

Introduction

Stress or heat shock proteins (HSPs) were first discovered in 19621 as a set of highly conserved proteins whose expression was induced by different kinds of stress. It has subsequently been shown that most HSPs have strong cytoprotective effects and behave as molecular chaperones for other cellular proteins. HSPs are also induced at specific stages of development, differentiation and during oncogenesis.2 Mammalian HSPs have been classified into five families according to their molecular size: HSP100, HSP90, HSP70, HSP60 and the small HSPs. Each family of HSPs is composed of members expressed either constitutively or regulated inducibly, and/or targeted to different sub-cellular compartments. The most studied HSPs are HSP90, the inducible HSP70 (also called HSP72) and the small heat shock protein HSP27.

HSP90 is a constitutively abundant chaperone that makes up 1–2% of cytosolic proteins. It is an ATP-dependent chaperone that accounts for the maturation and functional stability of a plethora of proteins termed HSP90 client proteins. In mammals, HSP90 comprises 2 homologue proteins (HSP90α and HSP90β) encoded by separated but highly conserved genes that arose through duplication during evolution.3 Most studies do not differentiate between the two isoforms because for a long time they have been considered as having the same function in the cells. However, recent data and notably out-of-function experiments indicate that at least some functions of the beta isoform are not overlapped by HSP90α’s functions.4 HSP70, like HSP90, binds ATP and undergoes a conformational change upon ATP binding, needed to facilitate the refolding of denatured proteins. The chaperone function of HSP70 is to assist the folding of newly synthesized polypeptides or misfolded proteins, the assembly of multi-protein complexes and the transport of proteins across cellular membranes.5,6 HSP90 and HSP70 chaperone activity is regulated by co-chaperones like Hip, CHIP or Bag-1 that increase or decrease their affinity for substrates through the stabilization of the ADP or ATP bound state. In contrast to HSP90 and HSP70, HSP27 is an ATP-independent chaperone, its main chaperone function being protection against protein aggregation.7 HSP27 can form oligomers of more than 1000 Kda. The chaperone role of HSP27 seems modulated by its state of oligomerization, the multimer being the chaperone competent state.8 This oligomerization is a very dynamic process modulated by the phosphorylation of the protein that favors the formation of small oligomers. Cell-cell contact and methylglyoxal can also modulate the oligomerization of the protein.9

It is now well accepted that HSPs are important modulators of the apoptotic pathway. Apoptosis, or programmed cell death, is a type of death essential during embryogenesis and, latter on in the organism, to assure cell homeostasis. Apoptosis is also a very frequent type of cell death observed after treatment with cytotoxic drugs.10 Mainly, two pathways of apoptosis can be distinguished, although cross-talk between the two signal transducing cascades exists (Fig. 1). The extrinsic pathway is triggered through plasma membrane proteins of the tumor necrosis factor (TNF) receptor family known as death receptors, and leads to the direct activation of the proteases called caspases, starting with the receptor-proximal caspase-8. The intrinsic pathway involves intracellular stress signals that provoke the permeabilization of the outer mitochondrial membrane, resulting in the release of pro-apoptotic molecules normally confined to the inter-membrane space. Such proteins translocate from mitochondria to the cytosol in a reaction that is controlled by Bcl-2 and Bcl-2-related proteins.11 One of them is the cytochrome c, which interacts with cytosolic apoptosis protease-activating factor-1 (Apaf-1) and pro-caspase-9 to form the apoptosome, the caspase-3 activation complex.12Apoptosis inducing factor (AIF) and the Dnase, EndoG, are other mitochondria intermembrane proteins released upon an apoptotic stimulus. They translocate to the nucleus and trigger caspase-independent nuclear changes.13,14 Two additional released mitochondrial proteins, Smac/Diablo and Htra2/Omi, activate apoptosis by neutralizing the inhibitory activity of the inhibitory apoptotic proteins (IAPs) that associate with and inhibit caspases15 (Fig. 1).

Figure 1     http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2633709/figure/F1/

Modulation of apoptosis and differentiation by HSP90, HSP70 and HSP27. In apoptosis (upper part), HSP90 can inhibit caspase (casp.) activation by its interaction with Apaf1. HSP90 stabilizes proteins from the survival signaling including RIP, Akt and 

Apoptosis and differentiation are two physiological processes that share different features like chromatin condensation or the need of caspase activity.16 It has been demonstrated in many differentiation models that the activation of caspases is preceded by a mitochondrial membrane depolarization and release of mitochondria apoptogenic molecules.17,18 This suggests that the mitochondrial-caspase dependent apoptotic pathway is a common intermediate for conveying apoptosis and differentiation. Timing, intensity and cellular compartmentalization might determine whether a cell is to die or differentiate. HSPs might be essential to orchestrate this decision. This review will describe the role of HSP90, HSP70 and HSP27 in apoptosis and cell differentiation.

 

HSP27, HSP70 and HSP90 are Anti-Apoptotic Proteins

Overexpression of HSP27, HSP70 or HSP90 prevents apoptosis triggered by various stimuli, including hyperthermia, oxidative stress, staurosporine, ligation of the Fas/Apo-1/CD95 death receptor or anticancer drugs.2,1921 Downregulation or inhibition of HSP27, HSP70 or HSP90 have been shown to be enough to sensitize a cell to apoptosis, proving that endogenous levels of those chaperones seem to be sufficiently high to control apoptosis.2224 It is now known that these chaperones can interact with key proteins of the apoptotic signaling pathways (Fig. 1).

 

HSP90: A survival protein through its client proteins.

HSP90 client proteins include a number of signaling proteins like ligand-dependent transcription factors and signal transducing kinases that play a role in the apoptotic process. Upon binding and hydrolysis of ATP, the conformation of HSP90 changes and the client protein, which is no longer chaperoned, is ubiquitinated and degraded by the proteasome.25

A function for HSP90 in the serine/threonine protein kinase Akt pathway was first suggested by studies using an HSP90 inhibitor that promoted apoptosis in HEK293T and resulted in suppressed Akt activity.26 A direct interaction between Akt and HSP90 was reported later.27 Binding of HSP90 protects Akt from protein phosphatase 2A (PP2A)-mediated dephosphorylation.26 Phosphorylated Akt can then phosphorylate the Bcl-2 family protein Bad and caspase-9 leading to their inactivation and to cell survival.28,29 But Akt has been also shown to phosphorylate IkB kinase, which results in promotion of NFkB-mediated inhibition of apoptosis.30 When the interaction HSP90/Akt was prevented by HSP90 inhibitors, Akt was dephosphorylated and destabilized and the likelihood of apoptosis increased.27 Additional studies showed that another chaperone participates in the Akt-HSP90 complex, namely Cdc37.26 Together this complex protects Akt from proteasome degradation. In human endothelial cells during high glucose exposure, apoptosis can be prevented by HSP90 through augmentation of the protein interaction between eNOS and HSP90 and recruitment of the activated Akt.31 HSP90 has also been shown to interact with and stabilize the receptor interacting protein (RIP). Upon ligation of TNFR-1, RIP-1 is recruited to the receptor and promotes the activation of NFκB and JNK. Degradation of RIP-1 in the absence of HSP90 precludes activation of NFκB mediated by TNFα and sensitizes cells to apoptosis.32 Another route by which HSP90 can affect NFκB survival activity is via the IKK complex.33 The HSP90 inhibitor geldanamycin prevents TNF-induced activation of IKK, highlighting the role of HSP90 in NFκB activation. Some other HSP90 client proteins through which this chaperone could participate in cell survival are p5334 and the transcription factors Her2 and Hif1α.35,36

But the anti-apoptotic role of HSP90 can also be explained by its effect and interaction with proteins not defined as HSP90 client proteins (i.e., whose stability is not regulated by HSP90). HSP90 overexpression in human leukemic U937 cells can prevent the activation of caspases in cytosolic extracts treated with cytochrome c probably because HSP90 can bind to Apaf-1 and inhibit its oligomerization and further recruitment of procaspase-9.37

Unfortunately, most studies do not differentiate between HSP90α and HSP90β. It has recently been demonstrated in multiple myeloma, in which an over expression of HSP90 is necessary for cell survival, that depletion of HSP90β by siRNA is sufficient to induce apoptosis. This effect is strongly increased when also HSP90α is also depleted,23 suggesting different and cooperating anti-apoptotic properties for HSP90α and HSP90β. Confirming this assumption, in mast cells, HSP90β has been shown to associate with the anti-apoptotic protein Bcl-2. Depletion of HSP90β with a siRNA or inhibion of HSP90 with geldanamycin inhibits HSP90β interaction with Bcl-2 and results in cytochrome c release, caspase activation and apoptosis.38

In conclusion, HSP90 anti-apoptotic functions can largely be explained by its chaperone role assuring the stability of different proteins. Recent studies suggest that the two homologue proteins, HSP90α and HSP90β, might have different survival properties. It would be interesting to determine whether HSP90α and HSP90β bind to different client proteins or bind with different affinity.

 

HSP70: A quintessential inhibitor of apoptosis.

HSP70 loss-of-function studies demonstrated the important role of HSP70 in apoptosis. Cells lacking hsp70.1 and hsp70.3, the two genes that code for inductive HSP70, are very sensitive to apoptosis induced by a wide range of lethal stimuli.39Further, the testis specific isoform of HSP70 (hsp70.2) when ablated, results in germ cell apoptosis.40 In cancer cells, depletion of HSP70 results in spontaneous apoptosis.41

HSP70 has been shown to inhibit the apoptotic pathways at different levels (Fig. 1). At the pre-mitochondrial level, HSP70 binds to and blocks c-Jun N-terminal Kinase (JNK1) activity.42,43 Confirming this result, HSP70 deficiency induces JNK activation and caspase-3 activation44 in apoptosis induced by hyperosmolarity. HSP70 also has been shown to bind to non-phosphorylated protein kinase C (PKC) and Akt, stabilizing both proteins.45

At the mitochondrial level, HSP70 inhibits Bax translocation and insertion into the outer mitochondrial membrane. As a consequence, HSP70 prevents mitochondrial membrane permeabilization and release of cytochrome c and AIF.46

At the post-mitochondrial level HSP70 has been demonstrated to bind directly to Apaf-1, thereby preventing the recruitment of procaspase-9 to the apoptosome.47However, these results have been contradicted by a study in which the authors demonstrated that HSP70 do not have any direct effect on caspase activation. They explain these contradictory results by showing that it is a high salt concentration and not HSP70 that inhibits caspase activation.48

HSP70 also prevents cell death in conditions in which caspase activation does not occur.49 Indeed, HSP70 binds to AIF, inhibits AIF nuclear translocation and chromatin condensation.39,50,51 The interaction involves a domain of AIF between aminoacids 150 and 228.52 AIF sequestration by HSP70 has been shown to reduce neonatal hypoxic/ischemic brain injury.53 HSP70 has also been shown to associate with EndoG and to prevent DNA fragmentation54 but since EndoG can form complexes with AIF, its association with HSP70 could involve AIF as a molecular bridge.

HSP70 can also rescue cells from a later phase of apoptosis than any known survival protein, downstream caspase-3 activation.55 During the final phases of apoptosis, chromosomal DNA is digested by the DNase CAD (caspase activated DNase), following activation by caspase-3. The enzymatic activity and proper folding of CAD has been reported to be regulated by HSP70.56

At the death receptors level, HSP70 binds to DR4 and DR5, thereby inhibiting TRAIL-induced assembly and activity of death inducing signaling complex (DISC).57 Finally, HSP70 has been shown to inhibit lysosomal membrane permeabilization thereby preventing cathepsines release, proteases also implicated in apoptosis.58,59

In conclusion, HSP70 is a quintessential regulator of apoptosis that can interfere with all main apoptotic pathways. Interestingly, the ATP binding domain of HSP70 is not always required. For instance, while the ATPase function is needed for the Apaf-150 and AIF binding,51 it is dispensable for JNK60 or GATA-161binding/protection. In this way, in erythroblasts, in which HSP70 blocks apoptosis by protecting GATA-1 from caspase-3 cleavage, a HSP70 mutant that lacks the ATP binding domain of HSP70 is as efficient as wild type HSP70 in assuring the protection of erythroblasts.61

 

HSP27: An inhibitor of caspase activation.

HSP27 depletion reports demonstrate that HSP27 essentially blocks caspase-dependent apoptotic pathways. Small interefence targeting HSP27 induces apoptosis through caspase-3 activation.62,63 This may be consequence of the association of HSP27 with cytochrome c in the cytosol, thereby inhibiting the formation of the caspase-3 activation complex as demonstrated in leukemia and colon cancer cells treated with different apoptotic stimuli.6466 This interaction involves amino-acids 51 and 141 of HSP27 and do not need the phosphorylation of the protein.65 In multiple myeloma cells treated with dexamethasone, HSP27 has also been shown to interact with Smac.67

HSP27 can also interfere with caspase activation upstream of the mitochondria.66This effect seems related to the ability of HSP27 to interact and regulate actin microfilaments dynamics. In L929 murine fibrosarcoma cells exposed to cytochalasin D or staurosporine, overexpressed HSP27 binds to F-actin68preventing the cytoskeletal disruption, Bid intracellular redistribution and cytochrome c release66 (Fig. 1). HSP27 has also important anti-oxidant properties. This is related to its ability to uphold glutathione in its reduced form,69 to decrease reactive oxygen species cell content,19 and to neutralize the toxic effects of oxidized proteins.70 These anti-oxidant properties of HSP27 seem particularly relevant in HSP27 protective effect in neuronal cells.71

HSP27 has been shown to bind to the kinase Akt, an interaction that is necessary for Akt activation in stressed cells. In turn, Akt could phosphorylate HSP27, thus leading to the disruption of HSP27-Akt complexes.72 HSP27 also affects one downstream event elicited by Fas/CD95. The phosphorylated form of HSP27 directly interacts with Daxx.73 In LNCaP tumor cells, HSP27 has been shown to induce cell protection through its interaction with the activators of transcription 3 (Stat3).74 Finally, HSP27 protective effect can also be consequence of its effect favouring the proteasomal degradation of certain proteins under stress conditions. Two of the proteins that HSP27 targets for their ubiquitination/proteasomal degradation are the transcription factor nuclear factor κB (NFκB) inhibitor IκBα and p27kip1. The pronounced degradation of IkBα induced by HSP27 overexpression increases NFκB dependent cell survival75 while that of p27kip1facilitates the passage of cells to the proliferate phases of the cellular cycle. As a consequence HSP27 allows the cells to rapidly resume proliferation after a stress.76

Therefore, HSP27 is able to block apoptosis at different stages because of its interaction with different partners. The capacity of HSP27 to interact with one or another partner seems to be determined by the oligomerization/phosphorylation status of the protein, which, at its turn, might depend on the cellular model/experimental conditions. We have demonstrated in vitro and in vivo that for HSP27 caspase-dependent anti-apoptotic effect, large non-phosphorylated oligomers of HSP27 were the active form of the protein.77 Confirming these results, it has recently been demonstrated that methylglyoxal modification of HSP27 induces large oligomers formation and increases the anti-apoptotic caspase-inhibitory properties of HSP27.78 In contrast, for HSP27 interaction with the F-actin and with Daxx, phosphorylated and small oligomers of HSP27 were necessary73,79 and it is its phosphorylated form that protects against neurotoxicity.80

 

HSP27, HSP70 and HSP90 and Cell Differentiation

Under the prescribed context of HSPs as powerful inhibitors of apoptosis, it is reasonable to assume that an increase or decrease in their expression might modulate the differentiation program. The first evidence of the role of HSPs in cell differentiation comes from their tightly regulated expression at different stages of development and cell differentiation. For instance during the process of endochondrial bone formation, they are differentially expressed in a stage-specific manner.81 In addition, during post-natal development, time at which extensive differentiation takes place, HSPs expression is regulated in neuronal and non-neuronal tissues.82 In hemin-induced differentiation of human K562 erythroleukemic cells, genes coding for HSPs are induced.83

In leukemic cells HSP27 has been described as a pre-differentiation marker84because its induction occurs early during differentiation.8588 HSP27 expression has also been suggested as a differentiation marker for skin keratinocytes89 and for C2C12 muscle cells.90 This role for HSP27 in cell differentiation might be related to the fact that HSP27 expression increases as cells reach the non proliferative/quiescent phases of the cellular cycle (G0/G1).19,76

Subcellular localization is another mechanism whereby HSPs can determine whether a cell is to die or to differentiate. We, and others, have recently demonstrated the essential function of nuclear HSP70 for erythroid differentiation. During red blood cells’ formation, HSP70 and activated caspase-3 accumulate in the nucleus of the erythroblast.91 HSP70 directly associates with GATA-1 protecting this transcription factor required for erythropoiesis from caspase-3 cleavage. As a result, erythroblats continue their differentiation process instead of dying by apoptosis.61 HSP70, during erythropoiesis in TF-1 cells, have been shown to bind to AIF and thereby to block AIF-induced apoptosis, thus allowing the differentiation of erythroblasts to proceed.18

HSP90 has been required for erythroid differentiation of leukemia K562 cells induced by sodium butyrate92 and for DMSO-differentiated HL-60 cells. Regulation of HSP90 isoforms may be a critical event in the differentiation of human embryonic carcinoma cells and may be involved in differentiation into specific cell lineages.93 This effect of HSP90 in cell differentiation is probably because multiple transduction proteins essential for differentiation are client proteins of HSP90 such as Akt,94 RIP32 or Rb.95 Loss of function studies confirm that HSP90 plays a role in cell differentiation and development. In Drosophila melanogaster, point mutations of HSP83 (the drosophila HSP90 gene) are lethal as homozygotes. Heteterozygous mutant combinations produce viable adults with the same developmental defect: sterility.96 In Caenorhabditis elegans, DAF-21, the homologue of HSP90, is necessary for oocyte development.97 In zebrafish, HSP90 is expressed during normal differentiation of triated muscle fibres. Disruption of the activity of the proteins or the genes give rise to failure in proper somatic muscle development.98 In mice, loss-of-function studies demonstrate that while HSP90α loss-of-function phenotype appears to be normal, HSP90β is lethal. HSP90β is essential for trophoblasts differentiation and thereby for placenta development and this function can not be performed by HSP90α.4

HSP90 inhibitors have also been used to study the role of HSP90 in cell differentiation. These inhibitors such as the benzoquinone ansamycin geldanamycin or its derivative the 17-allylamino-17-demethoxygeldanamycin (17-AAG), bind to the ATP-binding “pocket” of HSP90 with higher affinity than natural nucleotides and thereby HSP90 chaperone activity is impaired and its client proteins are degraded. As could be expected by the reported role of HSP90 in cell differentiation, inhibitors of HSP90 block C2C12 myoblasts differentiation.99 In cancer cells and human leukemic blasts, 17-AAG induces a retinoblastoma-dependent G1 block. These G1 arrested cells do not differentiate but instead die by apoptosis.100

However, some reports describe that inhibitors of HSP90 can induce the differentiation process. In acute myeloid leukemia cells, 17-AAG induced apoptosis or differentiation depending on the dose and time of the treatment.101Opposite effects on cell differentiation and apoptosis are also obtained with the HSP90 inhibitor geldanamycin: in PC12 cells it induced apoptosis while in murin neuroblastoma N2A cells it induced differentiation.102 In breast cancer cells, 17-AAG-induced G1 block is accompanied by differentiation followed by apoptosis.103 The HSP90 inhibitor PU3, a synthetic purine that like 17-AAG binds with high affinity to the ATP “pocket” of HSP90, caused breast cancer cells arrest in G1 phase and differentiation.104

These contradictory reports concerning the inhibitors of HSP90 and cell differentiation could be explained if we consider that these drugs, depending on the experimental conditions, can have some side effects more or less independent of HSP90. Another possibility is that these studies do not differentiate between the amount of HSP90α and HSP90β inhibited. It is presently unknown whether HSP90 inhibitors equally block both isoforms, HSP90α and HSP90β. It not known neither whether post-translational modifications of HSP90 (acetylation, phosphorylation.) can affect their affinity for the inhibitors. HSP90α has been reported to be induced by lethal stimuli while the HSP90β can be induced by growth factors or cell differentiating signals.105 Mouse embryos out-of-function studies clearly show the role of HSP90β in the differentiation process and, at least for HSP90β role in embryo cell differentiation, there is not an overlap with HSP90α functions. Therefore, we can hypothesized that it can be the degree of inhibition of HSP90β by the HSP90 inhibitors that would determine whether or not there is a blockade of the differentiation process. This degree of inhibition of the different HSP90 isoforms might be conditioned by their cellular localization and their post-translational modifications. It should be noted, however, that the relative relevance of HSP90β in the differentiation process might depend on the differentiation model studied.

To summarize, we can hypothesize that the role in the differentiation process of a chaperone will be determined by its transient expression, subcellular redistribution and/or post-translational modifications induced at a given stage by a differ- entiation factor. How can HSPs affect the differentiation process? First by their anti-apoptotic role interfering with caspase activity, we and other authors have shown that caspase activity was generally required for cell differentiation.16,17Therefore, HSPs by interfering with caspase activity at a given moment, in a specific cellular compartment, may orchestrate the decision differentiation versus apoptosis. In this way, we have recently shown that HSP70 was a key protein to orchestrate this decision in erythroblasts.61 Second, HSPs may affect the differentiation process by regulating the nuclear/cytosolic shuttling of proteins that take place during differentiation. For instance, c-IAP1 is translocated from the nucleus to the cytosol during differentiation of hematopoietic and epithelial cells, and we have demonstrated that HSP90 is needed for this c-IAP1 nuclear export.106It has also been shown that, during erythroblast differentiation, HSP70 is needed to inhibit AIF nuclear translocation.18 Third, in the case of HSP90, the role in the differentiation process could be through certain of its client proteins, like RIP or Akt, whose stability is assured by the chaperone.

 

Repercussions and Concluding Remarks

The ability of HSPs to modulate the fate of the cells might have important repercussions in pathological situations such as cancer. Apoptosis, differentiation and oncogenesis are very related processes. Defaults in differentiation and/or apoptosis are involved in many cancer cells’ aetiology. HSPs are abnormally constitutively high in most cancer cells and, in clinical tumors, they are associated with poor prognosis. In experimental models, HSP27 and HSP70 have been shown to increase cancer cells’ tumorigenicty and their depletion can induce a spontaneous regression of the tumors.24,107 Several components of tumor cell-associated growth and survival pathways are HSP90 client proteins. These qualities have made HSPs targets for anticancer drug development. Today, although many research groups and pharmaceutical companies look for soluble specific inhibitors of HSP70 and HSP27, only specific soluble inhibitors of HSP90 are available for clinical trials. For some of them (17-AAG) phase II clinical trials are almost finished.108 However, considering the new role of HSP90β in cell differentiation, it seems essential to re-evaluate the functional consequences of HSP90 blockade.

Differential expression of heat shock protein 70 (hsp70) in …

by D Lang – ‎2000 Journal of Leukocyte Biology  www.jleukbio.org/content/68/5/729.long

 

Related articles

Differential expression of heat shock protein 70 (hsp70) in human monocytes … Induction of hsp70 in different cell lines also increases the resistance to … (NO), oxidative stress, chemotherapeutic agents, ceramide, or radiation []. ….. and type-2 cytokines in the regulation of human monocyte apoptosis  Blood 90,1618-1625.

Cell Death and Disease – Do not stress, just differentiate …

Nature Jan 29, 2015 by C Boudesco – ‎2015 – ‎Related articles

– The concept that cell differentiation needs a specific pattern of HSPs was first … shock, suggesting a specific role for HSPs in red blood cell formation. … Conversely, HSP70, the well-described role of which is to assist the … Trinklein ND et al Cell Stress Chaperones 2004; 9: 21–28.

Cell Death and Differentiation – Pharmacological induction …

Nature by ZN Demidenko – ‎2006 – ‎ ‎Related articles

Nov 25, 2005 – Pharmacological induction of Hsp70 protects apoptosis-prone cells from …. GA did not cause cleavage of caspase-9 and PARP in HL60 cells …

HSP90 and HSP70: Implication in Inflammation Processes …

by M Sevin – ‎2015  – ‎Related articles  www.hindawi.com/journals/mi/2015/970242/

Sep 27, 2015 – In Bcr-abl leukaemia cells, the expression of the protein HSP70 is also elevated ….. GATA-1 protein level during erythroid cell differentiation,” Blood, vol. …. Cdc37 and Hsp90,” Molec Cell 2002; 9(2): 401–410

 

HSF-1 activates the ubiquitin proteasome system to promote non-apoptotic developmental cell death inC. elegans

A new pathway for non-apoptotic cell death

The results presented here allow us to construct a model for the initiation and execution of LCD in C. elegans (Figure 7). The logic of the LCD pathway may be similar to that of developmental apoptotic pathways. In C. elegans and Drosophila, where the control of specific cell deaths has been primarily examined, cell lineage or fate determinants control the expression of specific transcription factors that then impinge on proteins regulating caspase activation (Fuchs and Steller, 2011). Likewise, LCD is initiated by redundant determinants that require a transcription factor to activate protein degradation genes.

Figure 7.

https://elife-publishing-cdn.s3.amazonaws.com/12821/elife-12821-fig7-v3-480w.jpg

Figure 7. Model for linker cell death.

Green, upstream regulators. Orange, HSF-1. Purple, proteolytic components.    DOI: http://dx.doi.org/10.7554/eLife.12821.016

 

Our data suggest that three partially redundant signals control LCD initiation. The antagonistic Wnt pathways we describe may provide positional information to the linker cell, as the relevant ligands are expressed only near the region where the linker cell dies. The LIN-29 pathway, which controls timing decisions during the L4-adult molt, may ensure that LCD takes place only at the right time. Finally, while the TIR-1/SEK-1 pathway could act constitutively in the linker cell, it may also respond to specific cues from neighboring cells. Indeed, MAPK pathways are often induced by extracellular ligands. We propose that these three pathways, together, trigger activation of HSF-1. Our data support a model in which HSF-1 is present in two forms, HSF-1LC, promoting LCD, and HSF-1HS, protecting cells from stresses, including heat shock. We postulate that the redundant LCD initiation pathways tip the balance in favor of HSF-1LC, allowing this activity to bind to promoters and induce transcription of key LCD effectors, including LET-70/UBE2D2 and other components of the ubiquitin proteasome system (UPS), functioning through E3 ligase complexes consisting of CUL-3, RBX-1, BTBD-2, and SIAH-1.

Importantly, the molecular identification of LCD components and their interactions opens the door to testing the impact of this cell death pathway on vertebrate development. For example, monitoring UBE2D2 expression during development could reveal upregulation in dying cells. Likewise, genetic lesions in pathway components we identified may lead to a block in cell death. Double mutants in apoptotic and LCD genes would allow testing of the combined contributions of these processes.

The proteasome and LCD

As is the case with caspase proteases that mediate apoptosis (Pop and Salvesen, 2009), how the UPS induces LCD is not clear, and remains an exciting area of future work. That loss of BTBD-2, a specific E3 ligase component, causes extensive linker cell survival suggests that a limited set of targets may be required for LCD. Previous work demonstrated that BTBD2, the vertebrate homolog of BTBD-2, interacts with topoisomerase I (Khurana et al., 2010; Xu et al., 2002), raising the possibility that this enzyme may be a relevant target, although other targets may exist.

The UPS has been implicated in a number of cell death processes in which it appears to play a general role in cell dismantling, most notably, perhaps, in intersegmental muscle remodeling during metamorphosis in moths (Haas et al., 1995). However, other studies suggest that the UPS can have specific regulatory functions, as with caspase inhibition by IAP E3 ligases (Ditzel et al., 2008).

During Drosophila sperm development, caspase activity is induced by the UPS to promote sperm individualization, a process that resembles cytoplasm-specific activation of apoptosis (Arama et al., 2007). While C. elegans caspases are dispensible for LCD, it remains possible that they participate in linker cell dismantling or serve as a backup in case the LCD program fails.

Finally, the proteasome contains catalytic domains with target cleavage specificity reminiscent of caspases; however, inactivation of the caspase-like sites does not, alone, result in overt cellular defects (Britton et al., 2009), suggesting that this activity may be needed to degrade only specific substrates. Although the proteasome generally promotes proteolysis to short peptides, site-specific cleavage of proteins by the proteasome has been described (Chen et al., 1999). It is intriguing to speculate, therefore, that caspases and the proteasome may have common, and specific, targets in apoptosis and LCD.

A pro-death developmental function for HSF-1

Our discovery that C. elegans heat-shock factor, HSF-1, promotes cell death is surprising. Heat-shock factors are thought to be protective proteins, orchestrating the response to protein misfolding induced by a variety of stressors, including elevated temperature. Although a role for HSF1 has been proposed in promoting apoptosis of mouse spermatocytes following elevated temperatures (Nakai et al., 2000), it is not clear whether this function is physiological. In this context, HSF1 induces expression of the gene Tdag51 (Hayashida et al., 2006). Both pro- and anti-apoptotic activities have been attributed to Tdag51 (Toyoshima et al., 2004), and which is activated in sperm is not clear. Recently, pathological roles for HSF1 in cancer have been detailed (e.g. Mendillo et al., 2012), but in these capacities HSF1 still supports cell survival.

Developmental functions for HSF1 have been suggested in which HSF1 appears to act through transcriptional targets different from those of the heat-shock response (Jedlicka et al., 1997), although target identity remains obscure. Here, we have shown that HSF-1 has at least partially non-overlapping sets of stress-induced and developmental targets. Indeed, typical stress targets of HSF-1, such as the small heat-shock gene hsp-16.49 as well as genes encoding larger chaperones, likehsp-1, are not expressed during LCD, whereas let-70, a direct transcriptional target for LCD, is not induced by heat shock. Interestingly, the yeast let-70 homologs ubc4 and ubc5 are induced by heat shock (Seufert and Jentsch, 1990), supporting a conserved connection between HSF and UBE2D2-family proteins. However, the distinction between developmental and stress functions is clearly absent in this single-celled organism, raising the possibility that this separation of function may be a metazoan innovation.

What distinguishes the stress-related and developmental forms of HSF-1? One possibility is that whereas the stress response appears to be mediated by HSF-1 trimerization, HSF-1 monomers or dimers might promote LCD roles. Although this model would nicely account for the differential activities in stress responses and LCD of the HSF-1(R145A) transgenic protein, which would be predicted to favor inactivation of a larger proportion of higher order HSF-1 complexes, the identification of conserved tripartite HSEs in the let-70 and rpn-3 regulatory regions argues against this possibility. Alternatively, selective post-translational modification of HSF-1 could account for these differences. In mammals, HSF1 undergoes a variety of modifications including phosphorylation, acetylation, ubiquitination, and sumoylation (Xu et al., 2012), which, depending on the site and modification, stimulate or repress HSF1 activity. In this context, it is of note that p38/MAPK-mediated phosphorylation of HSF1 represses its stress-related activity (Chu et al., 1996), and the LCD regulator SEK-1 encodes a MAPKK. However, no single MAPK has been identified that promotes LCD (E.S.B., M.J.K. unpublished results), suggesting that other mechanisms may be at play.

Our finding that POP-1/TCF does not play a significant role in LCD raises the possibility that Wnt signaling exerts direct control over HSF-1 through interactions with β-catenin. However, we have not been able to demonstrate physical interactions between these proteins to date (M.J.K, unpublished results).

Finally, a recent paper (Labbadia and Morimoto, 2015) demonstrated that in young adult C. elegans, around the time of LCD, global binding of HSF-1 to its stress-induced targets is reduced through changes in chromatin modification. Remarkably, we showed that chromatin regulators play a key role in let-70 induction and LCD (J.A.M., M.J.K and S.S., manuscript in preparation), suggesting, perhaps, that differences in HSF-1 access to different loci may play a role in distinguishing its two functions.

LCD and neurodegeneration

Previous studies from our lab raised the possibility that LCD may be related to degenerative processes that promote vertebrate neuronal death. Nuclear crenellation is evident in dying linker cells and in degenerating cells in polyQ disease (Abraham et al., 2007) and the TIR-1/Sarm adapter protein promotes LCD in C. elegans as well as degeneration of distal axonal segments following axotomy in Drosophila and vertebrates (Osterloh et al., 2012). The studies we present here, implicating the UPS and heat-shock factor in LCD, also support a connection with neurodegeneration. Indeed, protein aggregates found in cells of patients with polyQ diseases are heavily ubiquitylated (Kalchman et al., 1996). Chaperones also colocalize with protein aggregates in brain slices from SCA patients, and HSF1 has been shown to alleviate polyQ aggregation and cellular demise in both polyQ-overexpressing flies and in neuronal precursor cells (Neef et al., 2010). While the failure of proteostatic mechanisms in neurodegenerative diseases is generally thought to be a secondary event in their pathogenesis, it is possible that this failure reflects the involvement of a LCD-like process, in which attempts to engage protective measures instead result in activation of a specific cell death program.

Read Full Post »

Transcription Modulation

Author and Curator: Larry H. Bernstein, MD, FCAP 

 

This portion of the transcription series deals with transcription factors and the effects of their binding on metabolism. This also has implications for pharmaceutical target identification.

The Functional Consequences of Variation in Transcription Factor Binding
DA. Cusanovich, B Pavlovic, JK. Pritchard*, Y Gilad*
1 Department of Human Genetics, 2 Howard Hughes Medical Institute, University of Chicago, Chicago, IL 3 Departments of Genetics and Biology and Howard Hughes Medical Institute, Stanford University, Stanford, CA.
PLoS Genet 2014;10(3):e1004226.  http://dx.doi.org:/10.1371/journal.pgen.1004226

One goal of human genetics is to understand how the information for precise and dynamic gene expression programs is encoded in the genome. The interactions of transcription factors (TFs) with DNA regulatory elements clearly

  • play an important role in determining gene expression outputs, yet
  • the regulatory logic underlying functional transcription factor binding is poorly understood.

An important question in genomics is to understand how a class of proteins called ‘‘transcription factors’’ controls the expression level of other genes in the genome in a cell type-specific manner – a process that is essential to human development. One major approach to this problem is to study where these transcription factors bind in the genome, but this does not tell us about the effect of that binding on gene expression levels and

  • it is generally accepted that much of the binding does not strongly influence gene expression.

To address this issue, we artificially reduced the concentration of 59 different transcription factors in the cell and then

  • examined which genes were impacted by the reduced transcription factor level.

Our results implicate some attributes

  • that might influence what binding is functional, but they also suggest that
  • a simple model of functional vs. non-functional binding may not suffice.

Many studies have focused on characterizing the genomic locations of TF binding, but

  • it is unclear whether TF binding at any specific locus has
  • functional consequences with respect to gene expression output.

We knocked down 59 TFs and chromatin modifiers in one HapMap lymphoblastoid cell line

  • to evaluate the context of functional TF binding.

We then identified genes whose expression was affected by the knockdowns

  • by intersecting the gene expression data with transcription factor binding data
    (based on ChIP-seq and DNase-seq)
  • within 10 kb of the transcription start sites of expressed genes.

This combination of data allowed us to infer functional TF binding.
Only a small subset of genes bound by a factor were

  • differentially expressed following the knockdown of that factor,
  • suggesting that most interactions between TF and chromatin
  • do not result in measurable changes in gene expression levels
  • of putative target genes.

We found that functional TF binding is enriched

  • in regulatory elements that harbor a large number of TF binding sites,
  • at sites with predicted higher binding affinity, and
  • at sites that are enriched in genomic regions annotated as ‘‘active enhancers.’’

We aim to be able to predict the expression pattern of a gene based on its regulatory
sequence alone. However, the regulatory code of the human genome is much more complicated than

  • the triplet code of protein coding sequences, and is highly context-specific,
  • depending on cell-type and other factors.

Moreover, regulatory regions are not necessarily organized into

  • discrete, easily identifiable regions of the genome and
  • may exert their influence on genes over large genomic distances

Genomic studies addressing questions of the regulatory logic of the human genome have largely taken one of two approaches.

  1. collecting transcription factor binding maps using techniques such as ChIPseq
    and DNase-seq
  2. mapping various quantitative trait loci (QTL), such as gene expression levels
    (eQTLs) [7], DNA methylation (meQTLs) [8] and chromatin accessibility (dsQTLs)

Cumulatively, binding map studies and QTL map studies have

  • led to many insights into the principles and mechanisms of gene regulation.

However, there are questions that neither mapping approach on its own is well equipped to address. One outstanding issue is

  • the fraction of factor binding in the genome that is ‘‘functional’’,
    which we define here to mean that
  • disturbing the protein-DNA interaction leads to a measurable
  • downstream effect on gene regulation.

Transcription factor knockdown could be used to address this problem, whereby

  • the RNA interference pathway is employed to greatly reduce
  • the expression level of a specific target gene by using small interfering RNAs (siRNAs).

The response to the knockdown can then be measured by collecting RNA after the knockdown and

  • measuring global changes in gene expression patterns
  • after specifically attenuating the expression level of a given factor.

Combining a TF knockdown approach with TF binding data can help us to

  • distinguish functional binding from non-functional binding

This approach has previously been applied to the study of human TFs, although for the most part studies have only focused on

  • the regulatory relationship of a single factor with its downstream targets.

The FANTOM consortium knocked down 52 different transcription factors in

  • the THP-1 cell line, an acute monocytic leukemia-derived cell line, and
  • used a subset of these to validate certain regulatory predictions based on binding motif enrichments.

We and others previously studied the regulatory architecture of gene expression in

  • the model system of HapMap lymphoblastoid cell lines (LCLs) using both
  • binding map strategies and QTL mapping strategies.

We now sought to use knockdown experiments targeting transcription factors in a HapMap LCL

  • to refine our understanding of the gene regulatory circuitry of the human genome.

Therefore, We integrated the results of the knockdown experiments with previous data on TF binding to

  • better characterize the regulatory targets of 59 different factors and
  • to learn when a disruption in transcription factor binding
  • is most likely to be associated with variation in the expression level of a nearby gene.

Gene expression levels following the knockdown were compared to

  • expression data collected from six samples that were transfected with negative control siRNA.

The expression data from all samples were normalized together using

  • quantile  normalization followed by batch correction using the RUV-2 method.

We then performed several quality control analyses to confirm

  1. that the quality of the data was high,
  2. that there were no outlier samples, and
  3. that the normalization methods reduced the influence of confounders

In order to identify genes that were expressed at a significantly different level

  • in the knockdown samples compared to the negative controls,
  • we used likelihood-ratio tests within the framework of a fixed effect linear model.

Following normalization and quality control of the arrays,

  • we identified genes that were differentially expressed between
  • the three knockdown replicates of each factor and the six controls.

Depending on the factor targeted, the knockdowns resulted in

  • between 39 and 3,892 differentially expressed genes at an FDR of 5%
    (Figure 1B; see Table S3 for a summary of the results).

The knockdown efficiency for the 59 factors ranged

  • from 50% to 90% (based on qPCR; Table S1).

The qPCR measurements of the knockdown level were significantly

  • correlated with estimates of the TF expression levels
  • based on the microarray data (P =0.001; Figure 1C).

Reassuringly, we did not observe a significant correlation between

  • the knockdown efficiency of a given factor and
  • the number of genes classified as differentially expressed foci.

Because we knocked down 59 different factors in this experiment

  • we were able to assess general patterns associated with the perturbation of transcription factors
  • beyond merely the number of affected target genes.

Globally, despite the range in the number of genes we identified as

  • differentially expressed in each knockdown,
  • the effect sizes of the differences in expression were relatively modest and
  • consistent in magnitude across all knockdowns.

The median effect size following the knockdown experiment for genes classified as

  • differentially expressed at an FDR of 5% in any knockdown was
  • a 9.2% difference in expression level between the controls and the knockdown (Figure 2),
  • while the median effect size for any individual knockdown experiment ranged between 8.1% and 11.0%.
    (this was true whether we estimated the knockdown effect based on qPCR (P = 0.10; Figure 1D) or microarray (P = 0.99; not shown) data.

Nor did we observe a correlation between

  • variance in qPCR-estimated knockdown efficiency (between replicates) and
  • the number of genes differentially expressed (P = 0.94; Figure 1E).

We noticed that the large variation in the number of differentially expressed genes

  • extended even to knockdowns of factors from the same gene family.

Figure 1. Differential expression analysis.
(a) Examples of differential expression analysis results for the genes HCST and IRF4. The top two panels are ‘MA plots’ of the mean Log2(expression level) between the knockdown arrays and the controls for each gene (x-axis) to the Log2(Fold-Change) between the knockdowns and controls (y-axis). Differentially expressed genes at an FDR of 5% are plotted in yellow (points 50% larger). The gene targeted by the siRNA is highlighted in red. The bottom two panels are ‘volcano plots’ of the Log2(Fold-Change) between the knockdowns and controls (x-axis) to the P-value for differential expression (y-axis). The dashed line marks the 5% FDR threshold. Differentially expressed genes at an FDR of 5% are plotted in yellow (points 50% larger). The red dot marks the gene targeted by the siRNA.
(b) Barplot of number of differentially expressed genes in each knockdown experiment.
(c) Comparison of the knockdown level measured by qPCR (RNA sample collected 48 hours posttransfection) and the knockdown level measured by microarray.
(d) Comparison of the level of knockdown of the transcription factor at 48 hrs (evaluated by qPCR; x-axis) and the number of genes differentially expressed in the knockdown experiment (y-axis).
(e) Comparison of the variance in knockdown efficiency between replicates for each transcription factor (evaluated by qPCR; x-axis) and the number of differentially expressed genes in the knockdown experiment (y-axis).

Differential expression analysis

Differential expression analysis

http://dx.doi.org:/10.1371/journal.pgen.1004226.g001

Figure 2. Effect sizes for differentially expressed genes.
Boxplots of absolute Log2(fold-change) between knockdown arrays and control arrays for all genes identified as differentially expressed in each experiment. Outliers are not plotted. The gray bar indicates the interquartile range across all genes differentially expressed in all knockdowns. Boxplots are ordered by the number of genes differentially expressed in each experiment. Outliers were not plotted.

Effect sizes for differentially expressed genes

Effect sizes for differentially expressed genes

http://dx.doi.org:/10.1371/journal.pgen.1004226.g002

Knocking down SREBF2 (1,286 genes differentially expressed), a key regulator of cholesterol homeostasis,

  • results in changes in the expression of genes that are
  • significantly enriched for cholesterol and sterol biosynthesis annotations.

While not all factors exhibited striking enrichments for relevant functional categories and pathways,

  • the overall picture is that perturbations of many of the factors
  • primarily affected pathways consistent with their known biology.

In order to assess functional TF binding, we next incorporated

  • binding maps together with the knockdown expression data.

We combined binding data based on DNase-seq footprints in 70 HapMap LCLs, reported by Degner et al. (Table S5)

  • and from ChIP-seq experiments in LCL GM12878, published by ENCODE.

We were thus able to obtain genome wide binding maps for a total of 131 factors that were either

  • directly targeted by an siRNA in our experiment (29 factors) or were
  • differentially expressed in one of the knockdown experiments.

We classified a gene as a bound target of a particular factor when

  • binding of that factor was inferred within 10kb of the transcription start site (TSS) of the target gene.

Using this approach, we found that the 131 TFs were bound

  • in proximity to a median of 1,922 genes per factor (range 11 to 7,053 target genes).

We considered binding of a factor to be functional if the target gene

  • was differentially expressed after perturbing the expression level the bound transcription factor.

We then asked about the concordance between

  • the transcription factor binding data and the knockdown expression data.
  •  the extent to which differences in gene expression levels following the knockdowns
  • might be predicted by binding of the transcription factors
  • within the putative regulatory regions of the responsive genes. and also
  • what proportion of putative target (bound) genes of a given TF were
  • differentially expressed following the knockdown of the factor.

Focusing only on the binding sites classified using the DNase-seq data
(which were assigned to a specific instance of the binding motif, unlike the ChIP data),

  • we examined sequence features that might distinguish functional binding.

In particular, whether binding at conserved sites was more likely to be functional  and

  • whether binding sites that better matched the known PWM for the factor were more likely to be functional.

We did not observe a significant shift in the conservation of functional binding sites (Wilcoxon rank sum P = 0.34),

  • but we did observe that binding around differentially expressed genes occurred at sites
  • that were significantly better matches to the canonical binding motif.

Figure 3. Intersecting binding data and expression data for each knockdown.
(a) Example Venn diagrams showing the overlap of binding and differential expression for the knockdowns of HCST and IRF4 (the same genes as in Figure 1).
(b) Boxplot summarizing the distribution of the fraction of all expressed genes that are bound by the targeted gene or downstream factors.
(c) Boxplot summarizing the distribution of the fraction of bound genes that are classified as differentially expressed, using an FDR of either 5% or 20%.

Intersecting binding data and expression data for each knockdown

Intersecting binding data and expression data for each knockdown

http://dx.doi.org:/10.1371/journal.pgen.1004226.g003

Considering bound targets determined from either the ChIP-seq or DNase-seq data, we observed that

  • differentially expressed genes were associated with both
  • a higher number of binding events for the relevant factors within 10 kb of the TSS (P,10216; Figure 4A)
  • as well as with a larger number of different binding factors
    (considering the siRNA-targeted factor and any TFs that were DE in the knockdown; P,10216; Figure 4B).

Figure 4. Degree of binding correlated with function. Boxplots comparing
(a) the number of sites bound, and
(b) the number of differentially expressed transcription factors binding events near functionally or non-functionally bound genes. We considered binding for siRNA-targeted factor and any factor differentially expressed in the knockdown.
(c) Focusing only on genes differentially expressed in common between each pairwise set of knockdowns we tested for enrichments of functional binding (y-axis). Pairwise comparisons between knockdown experiments were binned by the fraction of differentially expressed transcription factors in common between the two experiments. For these boxplots, outliers were not plotted.

Degree of binding correlated with function

Degree of binding correlated with function

http://dx.doi.org:/10.1371/journal.pgen.1004226.g004

We examined the distribution of binding about the TSS. Most factor binding was concentrated

  • near the TSS whether or not the genes were classified as differentially expressed (Figure 5A).
  • the distance from the TSS to the binding sites was significantly longer for differentially expressed genes (P,10216; Fig. 5B).

Figure 5. Distribution of functional binding about the TSS.
(a) A density plot of the distribution of bound sites within 10 kb of the TSS for both functional and non-functional genes. Inset is a zoom-in of the region +/21 kb from the TSS (b) Boxplots comparing the distances from the TSS to the binding sites for functionally bound genes and non-functionally bound genes. For the boxplots, 0.001 was added before log10 transforming the distances and outliers were not plotted.

Distribution of functional binding about the TSS

Distribution of functional binding about the TSS

http://dx.doi.doi:/10.1371/journal.pgen.1004226.g005

We investigated the distribution of factor binding across various chromatin states, as defined by Ernst et al. This dataset lists

  • regions of the genome that have been assigned to different activity states
  • based on ChIP-seq data for various histone modifications and CTCF binding.

For each knockdown, we separated binding events

  • by the genomic state in which they occurred and then
  • tested whether binding in that state was enriched around differentially expressed genes.

After correcting for multiple testing of genes that were differentially expressed.

  • 19 knockdowns showed significant enrichment for binding in ‘‘strong enhancers’’
  • four knockdowns had significant enrichments for ‘‘weak enhancers’’,
  • eight knockdowns showed significant depletion of binding in ‘‘active promoters’’ ,
  • six knockdowns had significant depletions for ‘‘transcription elongation’’,

Did the factors tended to have a consistent effect (either up- or down-regulation)

  • on the expression levels of genes they purportedly regulated?

All factors we tested are associated with both up- and down-regulation of downstream targets (Figure 6).

A slight majority of downstream target genes were expressed at higher levels

  • following the knockdown for 15 of the 29 factors for which we had binding information (Figure 6B).

The factor that is associated with the largest fraction (68.8%) of up-regulated target genes following the knockdown is EZH2,

  • the enzymatic component of the Polycomb group complex.

On the other end of the spectrum was JUND, a member of the AP-1 complex, for which

  • 66.7% of differentially expressed targets were down-regulated following the knockdown.

Figure 6. Magnitude and direction of differential expression after knockdown.
(a) Density plot of all Log2(fold-changes) between the knockdown arrays and controls for genes that are differentially expressed at 5% FDR in one of the knockdown experiments as well as bound by the targeted transcription factor.
(b) Plot of the fraction of differentially expressed putative direct targets that were up-regulated in each of the knockdown experiments.

Magnitude and direction of differential expression after knockdown

Magnitude and direction of differential expression after knockdown

http://dx.doi.org:/10.1371/journal.pgen.1004226.g006

We found no correlation between the number of paralogs and the fraction of bound targets that were differentially expressed. We also did not observe a significant correlation when we considered whether

  • the percent identity of the closest paralog might be predicative of
  • the fraction of bound genes that were differentially expressed following the knockdown (Figure S8).

While there is compelling evidence for our inferences, the current chromatin functional annotations

  • do not fully explain the regulatory effects of the knockdown experiments.

For example, the enrichments for binding in ‘‘strong enhancer’’ regions of the genome range from 7.2% to 50.1% (median = 19.2%),

  • much beyond what is expected by chance alone, but far from accounting for all functional binding.

In addition to considering

  • the distinguishing characteristics of functional binding, we also examined
  • the direction of effect that perturbing a transcription factor had on the expression level of its direct targets.

We specifically addressed whether

  • knocking down a particular factor tended to drive expression of its putatively direct (namely, bound) targets up or down,
  • which can be used to infer that the factor represses or activates the target, respectively.

Transcription factors have traditionally been thought of primarily as activators, and previous work from our group is consistent with that notion. Surprisingly, the most straightforward inference from the present study is that

  • many of the factors function as repressors at least as often as they function as activators.
  1. EZH2 had a negative regulatory relationship with the largest fraction of direct targets (68.8%),
    consistent with – the known role of EZH2 as the active member of the Polycomb group complex PC2
  2. while JUND seemed to have a positive regulatory relationship with the largest fraction of direct targets (66.7%),
    and with – the biochemical characterization of the AP-1 complex (of which JUND is a component) as a transactivator.

More generally, however, our results, combined with the previous work from our group and others make for a complicated view

  • of the role of transcription factors in gene regulation as
  • it seems difficult to reconcile the inference from previous work that
  • many transcription factors should primarily act as activators with the results presented here.

One somewhat complicated hypothesis, which nevertheless can resolve the apparent discrepancy, is that

  • the ‘‘repressive’’ effects we observe for known activators may be
  • at sites in which the activator is acting as a weak enhancer of transcription and
  • that reducing the cellular concentration of the factor
  • releases the regulatory region to binding by an alternative, stronger activator.

To more explicitly address the effect that our proximity-based definition of target genes might have on our analyses, we reanalyzed

  • the overlap between factor binding and differential expression following the knockdowns
  • using an independent, empirically determined set of target genes.

Thurman et al. used correlations in DNase hypersensitivity between

  • intergenic hypersensitive sites and promoter hypersensitive sites across diverse tissues
  • to assign intergenic regulatory regions to specific genes,
  • independently of proximity to a particular promoter.

We performed this alternative analysis in which we

  • assigned binding events to genes based on the classification of Thurman et al.

We then considered the overlap between binding and differential expression in this new data set. The results were largely

  • consistent with our proximity-based observations.

A median of 9.5% of genes that were bound by a factor were

  • also differentially expressed following the knockdown of that factor
    (compared to 11.1% when the assignment of binding sites to genes is based on proximity).

From the opposite perspective, a median of 28.0% of differentially expressed genes were bound by that factor
(compared to 32.3% for the proximity based definition). The results of this analysis are summarized in Table S7.

Our results should not be considered a comprehensive census of regulatory events in the human genome. Instead, we adopted a gene-centric approach,

  • focusing only on binding events near the genes for which we could measure expression
  • to learn some of the principles of functional transcription factor binding.

In light of our observations a reassessment of our estimates of binding may be warranted. In particular, because functional binding is skewed away from promoters (our system is apparently not well-suited to observe functional promoter binding, perhaps because of protection by large protein complexes),

  • a more conservative estimate of the fraction of binding that is indeed functional would not consider data within the promoter.

Importantly, excluding the putative promoter region from our analysis (i.e. only considering a window .1 kb from the TSS and ,10 kb from the TSS)

  • does not change our conclusions.

Considering this smaller window,

  • a median of 67.0% of expressed genes are still classified as bound by
  1. either the knocked down transcription factor or
  2. a downstream factors that is differentially expressed in each experiment,

yet a median of only 8.1% of the bound genes are

  • also differentially expressed after the knockdowns.

Much of what distinguishes functional binding (as we define it) has yet to be explained. We are unable to explain much of the differential expression observed in our experiments by the presence of least one relevant binding event. This may not be altogether surprising, as

  • we are only considering binding in a limited window around the transcription start site.

To address these issues, more factors should be perturbed to further evaluate the robustness of our results and to add insight. Together, such studies will help us develop a more sophisticated understanding of functional transcription factor binding in particular, the gene regulatory logic more generally.

Assessing quality and completeness of human transcriptional regulatory pathways on a genome-wide scale

E Shmelkov, Z Tang, I Aifantis, A Statnikov*
Biology Direct 2011; 6(15).  http://www.biology-direct.com/content/6/1/15

Recently the biological pathways have become a common and probably the most popular form of representing biochemical information for hypothesis generation and validation. These maps store wide knowledge of complex molecular interactions and regulations occurring in the living organism in a simple and obvious way, often using intuitive graphical notation. Two major types of biological pathways could be distinguished.

  1. Metabolic pathways incorporate complex networks of protein-based interactions and modifications, while
  2. signal transduction and transcriptional regulatory pathways are usually considered to provide information on mechanisms of transcription

While there are a lot of data collected on human metabolic processes,

  • the content of signal transduction and transcriptional regulatory pathways varies greatly in quality and completeness.

An indicative comparison of MYC transcriptional targets reported in ten different pathway databases reveals that these databases differ greatly from each other (Figure 1). Given that MYC is involved

  • in the transcriptional regulation of approximately 15% of all genes,

one cannot argue that the majority of pathway databases that contain

  • less than thirty putative transcriptional targets of MYC are even close to complete.

More importantly, to date there have been no prior genome-wide evaluation studies (that are based on genome-wide binding and gene expression assays) assessing pathway databases

Background: While pathway databases are becoming increasingly important in most types of biological and translational research, little is known about the quality and completeness of pathways stored in these databases. The present study conducts a comprehensive assessment of transcriptional regulatory pathways in humans for seven well-studied transcription factors:

  1. MYC,
  2. NOTCH1,
  3. BCL6,
  4. TP53,
  5. AR,
  6. STAT1,
  7. RELA.

The employed benchmarking methodology first involves integrating

  • genome-wide binding with functional gene expression data
  • to derive direct targets of transcription factors.

Then the lists of experimentally obtained direct targets

  • are compared with relevant lists of transcriptional targets from 10 commonly used pathway databases.

Results: The results of this study show that for the majority of pathway databases,

  • the overlap between experimentally obtained target genes and
  • targets reported in transcriptional regulatory pathway databases is
  • surprisingly small and often is not statistically significant.

The only exception is MetaCore pathway database which

  • yields statistically significant intersection with experimental results in 84% cases.

The lists of experimentally derived direct targets obtained in this study can be used

  • to reveal new biological insight in transcriptional regulation,  and we
  • suggest novel putative therapeutic targets in cancer.

Conclusions: Our study opens a debate on validity of using many popular pathway databases to obtain transcriptional regulatory targets. We conclude that the choice of pathway databases should be informed by

  • solid scientific evidence and rigorous empirical evaluation.

In the current study we perform

(1) an evaluation of ten commonly used pathway databases,

  • assessing the transcriptional regulatory pathways, considered in the current study as
  • the interactions of the type ‘transcription factor-transcriptional targets’.

This involves integration of human genome wide functional microarray or RNA-seq gene expression data with

  • protein-DNA binding data from ChIP-chip, ChIP-seq, or ChIP-PET platforms
  • to find direct transcriptional targets of the seven well known transcription factors:
  • MYC, NOTCH1, BCL6, TP53, AR, STAT1, and RELA.

The choice of transcription factors is based on their important role in oncogenesis and availability of binding and expression data in the public domain.

(2) the lists of experimentally derived direct targets are used to assess the quality and completeness of 84 transcriptional regulatory pathways from four publicly available (BioCarta, KEGG, WikiPathways and Cell Signaling Technology) and six commercial (MetaCore, Ingenuity Pathway Analysis, BKL TRANSPATH, BKL TRANSFAC, Pathway Studio and GeneSpring Pathways) pathway databases.

(3) We measure the overlap between pathways and experimentally obtained target genes and assess statistical significance of this overlap, and we demonstrate that experimentally derived lists of direct transcriptional targets

  • can be used to reveal new biological insight on transcriptional regulation.

We show this by analyzing common direct transcriptional targets of

  • MYC, NOTCH1 and RELA
  • that act in interconnected molecular pathways.

Detection of such genes is important as it could reveal novel targets of cancer therapy.

Figure 1 Number of genes in common between MYC transcriptional targets derived from ten different pathway databases. Cells are colored according to their values from white (low values) to red (high values). (not shown)

statistical methodology for comparison

statistical methodology for comparison

Figure 2 Illustration of statistical methodology for comparison between a gold-standard and a pathway database

Since we are seeking to compare gene sets from different studies/databases, it is essential to transform genes to standard identifiers. That is why we transformed all
gene sets to the HUGO Gene Nomenclature Committee approved gene symbols and names. In order to assess statistical significance of the overlap between the resulting gene sets, we used the hypergeometric test at 5% a-level with false discovery rate correction for multiple comparisons by the method of Benjamini and Yekutieli. The alternative hypothesis of this test is that two sets of genes (set A from pathway
database and set B from experiments) have greater number of genes in common than two randomly selected gene sets with the same number of genes as in sets A and B. For example, consider that for some transcription factor there are 300 direct targets in the pathway database #1 and 700 in the experimentally derived list (gold-standard), and their intersection is 16 genes (Figure 2a). If we select on random from a total of
20,000 genes two sets with 300 and 700 genes each, their overlap would be greater or equal to 16 genes in 6.34% times. Thus, this overlap will not be statistically significant at 5% a-level (p = 0.0634). On the other hand, consider that for the pathway database #2, there are 30 direct targets of that transcription factor, and their intersection with the 700-gene gold-standard is only 6 genes. Even though the size of this intersection is rather small, it is unlikely to randomly select 30 genes (out of 20,000) with an overlap greater or equal to 6 genes with a 700-gene gold-standard (p = 0.0005, see Figure 2a). This overlap is statistically significant at 5% a-level.

We also calculate an enrichment fold change ratio (EFC) for every intersection between a gold-standard and a pathway database. For a given pair of a gold-standard and a pathway database, EFC is equal to the observed number of genes in their intersection, divided by the expected size of intersection under the null hypothesis (plus machine epsilon, to avoid division by zero). Notice however that larger values of EFC may correspond to databases that are highly incomplete and contain only a few relations. For example, consider that for some transcription factor there are 300 direct targets in the pathway database #1 and 50 in the experimentally derived list (gold-standard), and their intersection is 30 genes (Figure 2b). If we select on random from a total of 20,000 genes two sets with 300 and 50 genes each, their expected overlap under the null hypothesis will be equal to 0.75. Thus, the EFC ratio will be equal to 40 (= 30/0.75). On the other hand, consider that for the pathway database #2, there are 2 direct
targets of that transcription factor, and their intersection with the 50-gene gold-standard is only 1 gene. Even though the expected overlap under the null hypothesis will be equal to 0.005 and EFC equal to 200 (5 times bigger than for the database #1), the size of this intersection with the gold-standard is 30 times less than for database #1 (Figure 2b).

Figure 3 Comparison between different pathway databases and experimentally derived gold-standards for all considered transcription factors. Value in a given cell is a number of overlapping genes between a gold-standard and a pathway-derived gene set. Cells
are colored according to their values from white (low values) to red (high values). Underlined values in red represent statistically significant intersections. (not shown)

Figure 4 Summary of the pathway databases assessment. Green cells represent statistically significant intersections between experimentally derived gold-standards and transcriptional regulatory pathways. White cells denote results that are not statistically significant. Numbers are the enrichment fold change ratios (EFC) calculated for each intersection. (not shown)

At the core of this study was creation of gold-standards of transcriptional regulation in humans that can be compared with target genes reported in transcriptional regulatory pathways. We focused on seven well known transcription factors and obtained gold-standards

  • by integrating genome-wide transcription factor-DNA binding data (from ChIP-chip, ChIP-seq, or ChIP-PET platforms)
  • with functional gene expression microarray and RNA-seq data.

The latter data allows to survey changes in the transcriptomes on a genome-wide scale

  • after the inhibition or over-expression of the transcription factor in question.

However, change in the expression of a particular gene could be caused either by the direct effect of the removal or introduction of a given transcription factor, as well as by an indirect effect, through the change in expression level of some other gene(s). It is essential

  • to integrate data from these two sources to
  • obtain an accurate list of gene targets that are directly regulated by a transcription factor.

It is worth noting that tested pathway databases typically do not give distinction between cell-lines, experimental conditions, and other details relevant to experimental systems in which data were obtained. These databases in a sense propose a ‘universal’ list of transcriptional targets. However, it is known that

  • transcriptional regulation in a cell is dynamic and works differently for different systems and stimuli.

This accentuates the major limitation of pathway databases and emphasizes

  • importance of deriving a specific list of transcriptional targets for the current experimental system.

In this study we followed the latter approach by developing gold-standards for specific cell characterized biological systems and experimental conditions.

The approach used here  for building gold-standards of direct mechanistic knowledge has several limitations. (see article).  Nevertheless, our results suggest that multiple transcription factors can co-operate and control both physiological differentiation and malignant transformation, as demonstrated utilizing combinatorial gene-profiling for

  • NOTCH1, MYC and RELA targets.

These studies might lead us to multi-pathway gene expression “signatures”

  • essential for the prediction of genes that could be targeted in cancer treatments.

In agreement with this hypothesis, several of the genes identified in our analysis have been suggested to be putative therapeutic targets in leukemia, with either preclinical or clinical trials underway (CDK4, CDK6, GSK3b, MYC, LCK, NFkB2, BCL2L1, NOTCH1).

Single-molecule tracking in live cells reveals distinct target-search strategies of transcription factors in the nucleus

I Izeddin†, V Récamier†‡, L Bosanac, II Cissé, L Boudarene, et al.
1Functional Imaging of Transcription, Institut de Biologie de l’Ecole Normale Supérieure (IBENS), Inserm, and CNRS UMR; 2Laboratoire Kastler Brossel, CNRS UMR, Departement de Physique et Institut de Biologie
de l’Ecole Normale Supérieure (IBENS), Paris, Fr; 3Transcription Imaging Consortium, Janelia Farm Research Campus, Howard Hughes Medical Institute, Ashburn, US; + more.
Biophysics and structural biology | Cell biology eLife 2014;3:e02230. http://dx.doi.org:/10.7554/eLife.02230

Transcription factors are

  • proteins that control the expression of genes in the nucleus, and
  • they do this by binding to other proteins or DNA.

First, however, these regulatory proteins need to overcome the challenge of

  • finding their targets in the nucleus, which is crowded with other proteins and DNA.

Much research to date has focused on measuring how fast proteins can diffuse and spread out throughout the nucleus. However these measurements only make sense if these proteins have access to the same space within the nucleus.

Now, Izeddin, Récamier et al. have developed a new technique to track

  • single protein molecules in the nucleus of mammalian cells.

A transcription factor called c-Myc and another protein called P-TEFb

  • were tracked and while they diffused at similar rates,
  • they ‘explored’ the space inside the nucleus in very different ways.

Izeddin, Récamier et al. found that c-Myc explores the nucleus in a so-called ‘non-compact’ manner: this means that it

  • can move almost everywhere inside the nucleus, and has an equal chance
  • of reaching any target regardless of its position in this space.

P-TEFb, on the other hand, searches

  • the nucleus in a ‘compact’ way.

This means that it is constrained to follow a specific path

  • through the nucleus and is therefore guided to its potential targets.

Izeddin, Récamier et al. explain that

  • the different ‘search strategies’ used by these two proteins
  • influence how long it takes them to find their targets and
  • how far they can travel in a given time.

These findings, together with information about

  • where and when different proteins interact in the nucleus,

will be essential to understand how the organization of the genome within the nucleus

  • can control the expression of genes.

The next challenge will now be to

  • uncover what determines a
  • protein’s search strategy in the nucleus, as well as
  • the potential ways that this strategy might be regulated.

Mueller et al., 2010; Normanno et al., 2012). These transient interactions are essential to ensure a fine regulation of binding site occupancy—by competition or by altering the TF concentration—but must also be persistent enough to enable the assembly of multicomponent complexes (Dundr, 2002; Darzacq and Singer, 2008; Gorski et al., 2008; Cisse et al., 2013).
In parallel to the experimental evidence of the fast diffusive motion of nuclear factors, our understanding of the intranuclear space has evolved from a homogeneous environment to an organelle where spatial arrangement among genes and regulatory sequences play an important role in transcriptional control (Heard and Bickmore, 2007). The nucleus of eukaryotes displays a hierarchy of organized structures (Gibcus and Dekker, 2013) and is often referred to as a
crowded environment.
How crowding influences transport properties of macromolecules and organelles in the cell is a fundamental question in quantitative molecular biology. While a restriction of the available space for diffusion can slow down transport processes, it can also channel molecules towards their targets increasing their chance to meet interacting partners. A widespread observation in quantitative cell biology is that the diffusion of molecules is anomalous, often attributed to crowding in the nucleoplasm, cytoplasm, or in the membranes of the cell (Höfling and Franosch, 2013). An open debate remains on how to determine whether diffusion is anomalous or normal (Malchus and Weiss, 2009; Saxton, 2012), and the mechanisms behind anomalous diffusion (Saxton, 2007). The answer to these questions bears important consequences for the understanding of the biochemical reactions of the cell.
The problem of diffusing molecules in non-homogenous media has been investigated in different fields. Following the seminal work of de Gennes (1982a), (1982b) in polymer physics, the study of diffusivity of particles and their reactivity has been generalized to random or disordered media (Kopelman, 1986; Lindenberg et al., 1991). These works have set a framework to interpret the mobility of macromolecular complexes in the cell, and recently in terms of kinetics of biochemical reactions (Condamin et al., 2007). Experimental evidence has also been found, showing the influence
of the glass-like properties of the bacterial cytoplasm in the molecular dynamics of intracellular processes (Parry et al., 2014). These studies demonstrate that the geometry of the medium in which diffusion takes place has important repercussions for the search kinetics of molecules. The notion of compact and non-compact exploration was introduced by de Gennes (1982a) in the context of dense polymers and describes two fundamental types of diffusive behavior. While a non-compact explorer leaves a significant number of available sites unvisited, a compact explorer performs a redundant
exploration of the space. In chemistry, the influence of compactness is well established to describe dimensional effects on reaction rates (Kopelman, 1986).
In this study, we aim to elucidate the existence of different types of mobility of TFs in the eukaryotic nucleus, as well as the principles governing nuclear exploration of factors relevant to transcriptional control. To this end, we used single-molecule (SM) imaging to address the relationship between the nuclear geometry and the search dynamics of two nuclear factors having distinct functional roles: the proto-oncogene c-Myc and the positive transcription elongation factor (P-TEFb). c-Myc is a basic helix-loop-helix DNA-binding transcription factor that binds to E-Boxes; 18,000 E-boxes are found in the genome, and c-Myc affects the transcription of numerous genes (Gallant and Steiger, 2009).
Recently, c-Myc has been demonstrated to be a general transcriptional activator upregulating transcription of nearly all genes (Lin et al., 2012; Nie et al., 2012). P-TEFb is an essential actor in the transcription regulation driven by RNA Polymerase II. P-TEFb is a cyclin-dependent kinase, comprising a CDK9 and a Cyclin T subunit. It phosphorylates the elongation control factors SPT5 and NELF to allow productive elongation of class II gene transcription (Wada et al., 1998). The carboxy-terminal domain (CTD) of the catalytic subunit RPB1 of polymerase II is also a major target of P-TEFb (Zhou et al., 2012). c-Myc and P-TEFb are therefore two good examples of transcriptional regulators binding to numerous sites in the nucleus; the latter binds to the transcription machinery itself and the former directly to DNA.

Single particle tracking (SPT) constitutes a powerful method to probe the mobility of molecules in living cells (Lord et al., 2010). In the nucleus, SPT has been first employed to investigate the dynamics of mRNAs (Fusco et al., 2003; Shav-Tal et al., 2004) or for rheological measurements of the nucleoplasm using inert probes (Bancaud et al., 2009). Recently, the tracking of single nuclear factors has been facilitated by the advent of efficient in situ tagging methods such as Halo
tags (Mazza et al., 2012). An alternative approach takes advantage of photoconvertible tags (Lippincott-Schwartz and Patterson, 2009) and photoactivated localization microscopy (PALM) (Betzig et al., 2006; Hess et al., 2006). Single particle tracking PALM (sptPALM) was first used to achieve high-density diffusion maps of membrane proteins (Manley et al., 2008). However, spt-PALM experiments have typically been limited to proteins with slow mobility (Manley et al., 2008) or those that undergo restricted motions (Frost et al., 2010; English et al., 2011).

Recently, by inclusion of light-sheet illumination, it has been used to determine the binding characteristics of TFs to DNA (Gebhardt et al., 2013). In this study, we developed a new sptPALM procedure adapted for the recording of individual proteins rapidly diffusing in the nucleus of mammalian cells. We used the photoconvertible fluorophore Dendra2 (Gurskaya et al., 2006) and took advantage of tilted illumination (Tokunaga et al., 2008). A careful control of the photoconversion rate minimized the background signal due to out-of-focus activated molecules, and we could thus follow the motion of individual proteins freely diffusing within the nuclear volume. With this sptPALM technique, we recorded large data sets (on the order of 104 single translocations in a single imaging session), which were essential for a proper statistical analysis of the search dynamics.
We applied our technique to several nuclear proteins and found that diffusing factors do not sense a unique nucleoplasmic architecture: c-Myc and P-TEFb adopt different nuclear space-exploration strategies, which drastically change the way they reach their specific targets. The differences observed between the two factors were not due to their diffusive kinetic parameters but to the geometry of their exploration path. c-Myc and our control protein, ‘free’ Dendra2, showed free diffusion in a three-dimensional nuclear space. In contrast, P-TEFb explored the nuclear volume by sampling a space of reduced dimensionality, displaying characteristics of exploration constrained in fractal structures.
The role of the space-sampling mode in the search strategy has long been discussed from a theoretical point of view (de Gennes, 1982a; Kopelman, 1986; Lindenberg et al., 1991). Our experimental results support the notion that it could indeed be a key parameter for diffusion-limited chemical reactions in the closed environment of the nucleus (Bénichou et al., 2010). We discuss the implications of our observations in terms of gene expression control, and its relation to the spatial organization of genes within the nucleus.

Read Full Post »

The Role of Informatics in The Laboratory

Larry H. Bernstein, M.D.

Introduction

The clinical laboratory industry, as part of a larger healthcare entrerprise, is in the midst of large changes that can be traced to the mid 1980’s, and that have accelerated in the last decade.   These changes are associated with a host of dramatic events that require accelerated readjustments in the work force, scientific endeavors, education, and the healthcare enterprise.   These changes are highlighted by the following (not unrelated) events:  globalization, a postindustrial information explosion driven by advances in computers and telecommunications networks, genomics and proteomics in drug discovery, consolidation in retail, communication, transportation, the healthcare and pharmaceutical industries.   Let us consider some of these events.   Globalization is driven by the principle that a manufacturer may seek to purchase labor, parts or supplies from sources that are less than is available at home.   The changes in the airline industry have been characterized by growth in travel, reductions in force, and ability of customers to find the best fares.   The discoveries in genetics that have evolved from asking questions about replication, translation and transcription of the genetic code, has moved to functional genomics and to elucidation of cell signaling pathways.   All of these changes were impossible without the information explosion.

The Laboratory as a Production Environment

The clinical laboratory produces about 60 percent of the information used by nurses and physicians to make decisions about patient care.   In addition, the actual cost of the laboratory is only about 3 – 4 percent of the cost of the enterprise.   The result is that the requirements for the support of the laboratory don’t receive attention without a proactive argument of how it contributes to realizing the goals of the organization.   The key issues affecting laboratory performance are:  staffing requirement, instrument configuration, workflow, what to send out, what to move to point-of-care, how to reconfigure workstations, and how to manage the information generated by the laboratory.

Staffing requirement, instrument configuration and workflow are being addressed by industry automation.   The first attempt was based on connecting instruments by tracks.   This  system proved unable to handle STAT specimens without a noticeable degrading of turnaround time.   The consequence of the failure is to drive creation of  a parallel system of point-of-care, and connecting them in a network with a RAWLS.  Another adjustment was to have an infrastructure for pneumatic tube delivery of specimens, and to redesign the laboratory. This had some success, but required capitalization.   The pneumatic tube system could be justified on the basis to a value to the organization in supporting services besides the laboratory. The industry is moving in the direction of connected modules that share an automated pipettor and reduce the amount of specimen splitting.   These are primarily PREANALYTICAL refinements.

There are other improvements that affect quality and cost that are not standard, and should be.   These are:  autoverification, embedded quality control rules and algorithms, and incorporation of the X-bar into standard quality monitoring.   This can be accomplished using middleware between the enterprise computer and the instruments designed to do more than just connect instruments with the medical information system.   The most common problem encountered when installing a medical repository is the repeated slowdown of the system as more users are connected with the system.   The laboratory has to be protected from this phenomenon, which can be relieved considerably by an open-architecture.   Another function of middleware will be to keep track of productivity by instrument, and to establish the cost per reportable result.

The Laboratory and Informatics

A few informatics requirements for the processing of tests are:

  1. Reject release of runs that fail Quality Control rules
  2. Flag results that fail clinical rules for automatic review
  3. Ability to construct a report that has correlated information for physician review, regardless of where the test is produced (RBC, MCV, reticulocytes and ferritin)
  4. Ability to present critical information in a production environment without technologist intervention (platelet count or hemoglobin in preparation of transfusion request)
  5. Ability to download 20,000 patients from an instrument for review of reference ranges
  6. Ability to look at quality control of results on more than one test on more than one instrument at a time
  7. Ability to present risks in a report for physicians for medical decisions as an alternative to a traditional cutoff value

I list essential steps of the workload processing sequence and identification of informatics enhancement of the process (bolded):

Prelaboratory (ER) 1:

Nurse draws specimens from patient (without specimen ID) and places tubes in bag labeled with name

Nurse prints labels after patient is entered.

Labels put on tubes

Orders entered into computer and labels put on tubes

Tubes sent to laboratory

Lab test  is shown as PENDING

Prelaboratory 2:

Tubes in bags sent to lab (by pneumatic tube)

Time of arrival is not same as time of order entry (10 minutes later)

If order entry is not done prior to sending specimen – entry is done in front processing area –

Sent to lab area 10 minutes later after test is entered into computer

Preanalytical:

Centrifugation

Delivery to workareas (bins)

Aliquoting for serological testing

Workstation assignment

Dating and amount of reagents

Blood gas or co-oximetry – no centrifugation

Hematology – CBC – no centrifugation
send specimen for Hgb A1c

Send specimen for Hgb electrophoresis and Hgb F/Hgb A2

Specimen to Aeroset and then to Centaur

Analytical:

Use of bar code to encode information

Check alignment of bar code

Quality control and calibration at required interval – check before run

Run tests

Manual:

2 hrs per run

enter accession #

enter results 1 accession at a time

Post analytical:

Return to racks or send to another workarea

Verify results

Enter special comments

Special  problems:

Calling results

Add-on tests

Misaligned bar code label

Inability to find specimen

Coagulation

Manual differentials

Informatics and Information Technology

The traditional view of the laboratory environment has been that it is a manufacturing center, but the main product of the laboratory is information, and the environment is a knowledge business.   This will require changes in the education of clinical laboratory professionals.   Biomedical Informatics has been defined as the scientific field that deals with the storage, retrieval, sharing, and optimal use of biomedical information, data, and knowledge for problem solving and decision making. It touches on all basic and applied fields in biomedical science and is closely tied to modern information technologies, notably in the areas of computing and communication.   The services supported by an informatics architecture include operations and quality management, clinical monitoring, data acquisition and management, and statistics supported by information technology.

The importance of a network architecture is clear.   We are moving from computer-centric processing to a data-centric environment. We will soon manage a wide array of complex and inter-related decision-making resources. The resources, commonly referred to as objects and contents, can now include voice, video, text, data, images, 3D models, photos, drawings, graphics, audio and compound documents.  The architectural features required to achieve this is in Fig 1.

According to Coeira and Dowton (Coiera E and Dowton SB. Reinventing ourselves: How innovations such as on-line ‘just-in-time’ CME may help bring about a genuinely evidence-based clinical practice. Medical Journal of Australia 2000;173:343-344), echoing Lawrence Weed, “Clinicians in the past were trained to master clinical knowledge and become experts in knowing why and how. Today’s clinicians have no hope of mastering any substantial portion of the medical knowledge base.  Every time we make a clinical decision, we should stop to consider whether we need to access the clinical evidence-base. Sometimes that will be in the form of on-line guidelines, systematic reviews or the primary clinical literature.”

Fig 1

Interoperability across environments

Define representation for storage that is independent of  implementation

Define a representation of collection that is independent of the database – schema, table structures

Informatics and the Education of Laboratory Professionals

The increasing dependence on laboratory information and the incorporation of laboratory information into Evidence-Based Guidelines necessitates a significant component of education in informatics.   The public health service has mandated informatics as a component of competencies for health services professionals (“Core Competencies for Public Health Professionals” compendium developed by the Council on Linkages Between Academia and Public Health Practice.), and nursing informatics competencies have already been written.   Coiera (E. Coiera, Medical informatics meets medical education: There’s more to understanding information than technology, Medical Journal of Australia 1998; 168: 319-320) has suggested 10 essential informatics skills for physicians.

I have put together a list below with items taken from Coiera and the Public Health Service competencies for elaboration of competencies for Clinical Laboratory Sciences.
A.   Personal Maintenance
1.   Understands the dynamic and uncertain nature of medical knowledge and know how to keep personal knowledge and skills up-to-date

  1.  Searches for and assesses knowledge according to the statistical basis of scientific evidence
  2. Understands some of the logical and statistical models of the diagnostic process
  3. Interprets uncertain clinical data and deals with artefact and error
  4. Evaluates clinical outcomes in terms of risks and benefits

B.   Effective Use of Information

Analytic Assessment Skills

  1. Identifies and retrieves current relevant scientific evidence
  2. Identifies the limitations of research
  3. Determines appropriate uses and limitations of both quantitative and qualitative data

9.  Evaluates the integrity and comparability of data and identifies gaps in data sources

10.  Applies ethical principles to the collection, maintenance, use, and dissemination of data and information
11.  Makes relevant inferences from quantitative and qualitative data
12.  Applies data collection processes, information technology applications, and computer systems storage/retrieval strategies

13.  Manages information systems for collection, retrieval, and use of data for decision-making
14.  Conducts cost-effectiveness, cost-benefit, and cost utility analyses

  1. Effective Use of Information Technology
  1.  Select and utilize the most appropriate communication method for a given task (eg, face-to-face conversation, telephone, e-mail, video, voice-mail, letter)
  2.  Structure and communicate messages in a manner most suited to the recipient, task and chosen communication medium.

17.  Utilizes personal computers and other office information technologies for working with documents and other computerized files

  1.  Utilizes modern information technology tools for the full range of electronic communication appropriate to one’s duties and programmatic area.
  2. Utilizes information technology so as to ensure the integrity and protection of electronic files and computer systems
  1.  Applies all relevant procedures (policies) and technical means (security) to ensure that confidential information is appropriately protected.

I expand on these recommended standards.   The first item is personal maintenance.   This requires continued education to meet the changing needs of the profession in expanding knowledge and access to knowledge that requires critical evaluation.   The payment for the profession has been paid for recognizing the technical contributions made by the laboratory profession as a task oriented contribution, but not for a contribution as a knowledge worker.   This can be changed, but it can’t be realized through the usual bacchalaureate educated requirement.   Most technologists want to get out in the workforce, but after they are out in the workforce – what next?   In many institutions, it falls back on the laboratory to provide the expertise to drive the organization in the computer and information restructuring, from staff taken from the transfusion service, microbiology, and elsewhere.   The laboratory is recognized for an information expertise, but then there is still reason to do more.   The fact is that the mind set of the laboratory staff has been in a manufacturing productivity related to test production, but the data that the production represents is information.   We have the quality control of the test process, but we are required to manage the total process, including the quality of the information we generate.   Another consideration is that the information we generate is used for clinical trials, and a huge variation in the way the information is used is problematic.

The first category for discussion is personal maintenance.  These items are keeping up with knowledge about advances in medical knowledge,  being critical about the quality of the evidence for current knowledge, and being aware of the statistical underpinnings for that thinking (1-5).   It is not enough to keep up with changes in medical thinking using only the professional laboratory literature. A systematic review of problem topics using PubMed as a guide is also essential.  This requires that the clinical laboratory scientist will have to know how to access the internet and search for key studies concerning the questions that are being asked.   The reading of abstracts and papers also requires an education in methods of statistical analysis, contingency tables, study design, and critical thinking.   The most common methods used in clinical laboratory evaluation are linear regression, linear regression, and yes, linear regression.  A discussion over distance learning among members of the American Statistical Association reveals that much of statistical education for the biologists, chemists, and engineers now comes from *software*.  Knowledge workers in drug development and in molecular diagnostics are increasingly challenged with larger, more complicated data sets, and there is a need to interpret and report results quickly. This need is not confined to basic research or the clinical setting, and it may have to be done without consulting with statisticians.  Category A slides into category B, effective use of information.

Effective use of information requires skills that support the design of evaluations of laboratory tests, methods of statistical analysis, and the critical assessment of published work (6-9), and the processes for collecting data, using information technology application, and interpreting the data (10-12).   Items 13 and 14 address management issues.

There is a vocabulary that has to be mastered and certain questions that have to be answered whenever a topic is being investigated.   I identify a number of these at this point in the discussion.

Contingency Table:  A table of frequencies, usually two-way, with event type in columns and test results as positive or negative in rows.   A multi-way table can be used for multivalued categorical analysis.   The conventional 2X2 contingency table is shown below –

No disease Disease
Test negative A  (TN) B  (FN) A+B

PVN =

TN/(FN+TN)Test positiveC  (FP)D  (TP)C+D

PVP =

TP/(TP+FP) A+C

Specificity=
TN/(FP+TN)B+C

Sensitivity =
TP/(TP+FN)A+B+C+D

Type I error:  There is no finding when one actually exists (missed diagnosis)(false negative error).

Type II error:  There is a finding when none exists (false positive error).

Sensitivity:  Percentage of true positive results.  D/(B + D)

Specificity:  Percentage of true negative results. A/(A + C)

False positive error rate:  The percentage of results that are positive in the absence of disease (1 – specificity).  C/(A + C)

ROC curve:  Receiver operator characteristic curve is plot of sensitivity vs I-specificity.  Two methods can be compared in ROC analysis by the area under the curve.   The optimum decision point can be identified as within a narrow range of coordinates on the curve.

Predictive value (+)(PVP):  Probability there is disease when a test is positive (D/C + D), or percentage of patients with disease, given a positive test.   The observed and expected probability may be the same or different.

Predictive value (-)(PVN):  Probability of absence of disease given a negative test result (A/A + B), or percentage of patients without disease given a negative test.  The observed and expected probability may be the same or different.

Power:   When a statement is made that there is no effect, or a test fails to predict the finding of disease, are there enough patients included in the study to see the effect if it exists.   This applies to randomized controlled drug studies as well as studies of tests. Power protects against the error of finding no effect when it exists.

Selection Bias:  It is common to find a high performance claimed for a test that is not later substantiated when it is introduced and widely used.   Why does this occur?   A common practice in experimental design is to define inclusion criteria and exclusion criteria so that the effect is very specific for the condition and to eliminate the interference by “confounders”, unanticipated effects that are not intended.   A common example of this is the removal of patients with acute renal failure and chronic renal insufficiency because of delayed clearance of analytes from the circulation.   The result is that the test is introduced into a population different than the trial population with claims  based on the performance in a limited population.   The error introduced could be prediction of disease in an individual in whom the effect is not true.   This error is reduced by elimination of selection bias, which may require multiple studies using patients who have the confounding conditions (renal insufficiency, myxedema).   Unanticipated effects often aren’t designed into a study.   In many studies about cardiac markers, the study design included only patients who had Acute Coronary Syndrome (ACS)  This is an example of selection bias.   Patients who have ACS  have chest pain of anginal nature that lasts at least 30 minutes, and usually have more than a single episode in 24 hours.   That is not how a majority of patients present to the emergency department who are suspected of having a myocardial infarct.   How then is one to evaluate the effectiveness of a cardiac marker?

Randomization:   Randomization is the assignment of the treatment group to either placebo (no treatment) or treatment.   The investigator and the participant enrolled in the study are blinded.   The analyst might also be blinded.   A potential problem is selection bias from dropouts who skew the characteristics of the population.

Critical questions:

What is the design of the study that you are reading?   Is there sufficient power or is there selection bias?  What are the conclusions of the authors?   Are the conclusions in line with the study design, or overstated?

Statistical tests and terms:   

Normal distribution:  Symmetrical bell shaped curve (Gaussian distribution).   The 2 standard deviation limits is approximately the 95% confidence interval.

Chi square test:  Has a chi square distribution.   Used for measuring probability from a contingency table.   Non-parametric test.

Student’s t-test:  Parametric measure of difference between two population means.

F-test:  An F-test ( Snedecor and Cochran, 1983) is used to test if the standard deviations of two populations are equal.  In comparing two independent samples of size N1 and N2 the F Test provides a measure for the probability that they have the same variance. The estimators of the variance are s12 and s22. We define as test statistic their ratio T = s12/ s22, which follows an F Distribution with f1= N1-1 and f2= N2-1 degrees of freedom.

F Distribution: The F distribution is the ratio of two chi-square distributions with degrees of freedom and , respectively, where each chi-square has first been divided by its degrees of freedom.

Z scores:  Z scores are sometimes called “standard scores”. The z score transformation is especially useful when seeking to compare the relative standings of items from distributions with different means and/or different standard deviations.

Analysis of variance:  Parametric measure of two or more population means by the comparison of variances between the populations.   Probability is measured by the F-test.

Linear Regression:  A classic statistical problem is to try to determine the relationship between two random variables  X and Y. For example, we might consider height and weight of a sample of adults.  Linear regression attempts to explain this relationship with a straight line fit to the data.  The simplest case of regression — one dependent and one independent variable — one can visualize in a scatterplot, is simple linear regression (see below).   The linear regression model is the most commonly used model in Clinical Chemistry.

Multiple Regression:  The general purpose of multiple regression (the term was first used by Pearson, 1908) is to learn more about the relationship between several independent or predictor variables and a dependent or criterion variable.  The general computational problem that needs to be solved in multiple regression analysis is to fit a straight line to a number of points.  A multiple regression fits a line using two or more predictors to the dependent variable by a model — Y = a1X1 + a2X + b + g.

Discriminant function:  Discriminant analysis is a technique for classifying a set of observations into predefined classes. The purpose is to determine the class of an observation based on a set of variables known as predictors or input variables. The model is built based on a set of observations for which the classes are known. This set of observations is sometimes referred to as the training set. Based on the training set , the technique constructs a set of linear functions of the predictors, known as discriminant functions, such that

L = b1x1 + b2x2 + … + bnxn + c , where the b’s are discriminant coefficients, the x’s are the input variables or predictors and c is a constant.

These discriminant functions are used to predict the class of a new observation with unknown class. For a k class problem k discriminant functions are constructed. Given a new observation, all the k discriminant functions are evaluated and the observation is assigned to class i if the ith discriminant function has the highest value.

Nonparametric Methods:

Logistic Regression: Researchers often want to analyze whether some event occurred or not.  The outcome is binary.  Logistic regression is a type of regression analysis where the dependent variable is a dummy variable (coded 0, 1).   The linear probability model, expressed as Y = a + bX + e, is problematic because

  1. The variance of the dependent variable is dependent on the values of the independent variables.
  2. e, the error term, is not normally distributed.
  3. The predicted probabilities can be greater than 1 or less than 0.

The “logit” model has the form:

ln[p/(1-p)] = a + BX + e or

[p/(1-p)] = expa expBX expe

where:

  • ln is the natural logarithm, logexp, where exp=2.71828…
  • p is the probability that the event Y occurs, p(Y=1)
  • p/(1-p) is the “odds ratio”
  • ln[p/(1-p)] is the log odds ratio, or “logit”

The logistic regression model is simply a non-linear transformation of the linear regression. The logit distribution constrains the estimated probabilities to lie between 0 and 1.

Graphical Ordinal Logit Regression:  The logistic regression fits a non-parametric solution to a two-valued event.   The outcome in question might have 3 or more values.

For example, scaled values of a test – low, normal, and high – might have different meanings.   This type of behavior occurs in certain classification problems.  For example, the model has to deal with anemia, normal, and polycythemia, or similarly, neutropenia, normal, and systemic inflammatory response (sepsis).   This model fits the data quite readily.

Clustering methods:  There are a number of methods to classify data when the dependent variable is not known, but is presumed to exist.   A commonly used method classifies data using geometric distance of the average point coordinates.   A very powerful method used is Latent Class Cluster analysis.

Data Extraction:

Data can be extracted from databases, but have to be worked at in a flat file format.   The easiest and most commonly used methods are to collect data in a relational database, such as Access (if the format is predefined), or the convert data into an Excel format.   A common problem is the inability to extract certain data because it is not in an extractable or usable format.

Let us examine how these methods are actually used in a clinical laboratory setting.

The first example is a test introduced almost 30 years ago into quality control in hematology by Brian Bull at Loma LindaUniversity called the x-bar function (also the Bull algorithm).   The method looks at the means of runs of the population data on the assumption the means of the MCV don’t vary for a stable population from day-to-day.  This is a very useful method that can be applied to the evaluation of laboratory.   It is a standard quality control program used in industrial processes since the 1930s.

We next examine the Chi Square distribution.  Review the formula for calculating chi square and calculations of expected frequencies.  Take a two-by-two table of the type

Effect               No effect          Sum Column

Predictor positive          87                     12                     99

Predictor negative         18                     93                    111

Sum Rows                    105                   105                   210

Experiment with the recalculation of chi square by changing the frequencies in the columns for effect and no effect, keeping the total frequencies the same.  The result is a decrease in the chi square as predictor negative – effect and predictor positive – no effect both increase.  The exercise can be carried out on the chi square calculator using Google to find the site.   The chi square can be used to test the contingency table that is used to indicate the effectiveness of fetal fibronectin for assessing low risk of preterm delivery.

For example,

No Preterm Labor Yes Preterm Labor Sum Row
FFN – neg

99

1

100

FFN – pos

35

65

100

Sum Column

134

66

200

PVN = 100*(1/100)% = 99%

99% observed probability that there will not be preterm delivery with a negative test.

Chi square goodness of fit:

Degrees of freedom: 1
Chi-square = 92.6277702397105
p is less than or equal to 0.001.
The distribution is significant.

Examine the effects of scaling of continuous data from a heart attack study to obtain ordered intervals.  Look at the chi square test for the heart attack test by a Nx2 table with the table columns as heart attack or no heart attack.  This allowed us to determine the significance of the test in predicting heart attack.   Look at the Student T test for comparing the continuous values of the test between the heart attack and non-heart attack population.  The T test is like the one-way analysis of variance with only two values for the factor variable.  The T test and ANOVA1 compares the means between two populations.  If the result is significant, then the null hypothesis that the data is taken from the same population is rejected.  The alternative hypothesis is that they are different.

One can visualize the difference by plotting the means and confidence intervals for the two groups.

One can visualize the difference by plotting the means and confidence intervals for the two groups.

We can plot a frequency distribution before we calculate the means and check the distribution around the means.   The simplest way to do this is the histogram.   The histogram for a large sample of potassium values is used to illustrate this.   The mean is 4.2.

We can use a method for quality control called the X-bar (Beckman Coulter has it on the hematology analyzer) to test the deviation from the means of runs.   I illustrate the validity of the X-bar by comparing the means of a series of runs.

Sample size                   =       958

Lowest value                  =        84.0000

Highest value                 =        90.7000

Arithmetic mean               =        87.8058

Median                        =        87.8000

Standard deviation            =         0.9362

————————————————————

Kolmogorov-Smirnov test

for Normal distribution       :   accept Normality (P=0.353)

If I compare the means by the T-test, I am testing whether the sampling is taken from the same or different populations.   When we introduce a third group, then we are asking whether the sampling is taken from a single population or to reject the hypothesis, taking the alternative hypothesis that the samples are different.   This is illustrated by sampling from a group of patients with no cardiac disease and normal, neither of which have acute myocardial infarction.   This is illustrated below:

Two-sample t-test on CKMB grouped by OTHER against Alternative = ‘not equal’

      Group N Mean SD
   0          660       1.396       3.085
   1          90       4.366       4.976

Separate variance:

t                         =       -5.518

df                        =         98.5

p-value                   =        0.000

Bonferroni adj p-value    =        0.000

Pooled variance:

t                         =       -7.851

df                        =          748

p-value                   =        0.000

Bonferroni adj p-value    =        0.000

Two-sample t-test on TROP grouped by OTHER against Alternative = ‘not equal’

      Group N Mean SD
   0          661       0.065       0.444
   1          90       1.072       3.833

Separate variance:

t                         =       -2.489

df                        =         89.3

p-value                   =        0.015

Bonferroni adj p-value    =        0.029

Pooled variance:

t                         =       -6.465

df                        =          749

p-value                   =        0.000

Bonferroni adj p-value    =        0.000

Another example illustrates the application of this significance test.   Beta thalassemia is characterized by an increase in hemoglobin A2.   Thalassemia gets more complicated when we consider delta beta deletion and alpha thalassemia.   Nevertheless, we measure the hemoglobin A2 by liquid chromatography on the Biorad Variant II.   The comparison of hemoglobin A2 in affected and unaffected is shown below (with random resampling):

Two-sample t-test on A2 grouped by THALASSEMIA DIAGNOSIS against Alternative = ‘not equal’

      Group N Mean SD
   0          257       3.250       1.131
   1          61       6.305       2.541

Separate variance:

t                         =       -9.177

df                        =         65.7

p-value                   =        0.000

Bonferroni adj p-value    =        0.000

Pooled variance:

t                         =      -14.263

df                        =          316

p-value                   =        0.000

Bonferroni adj p-value    =        0.000

When we do a paired comparison of the Variant hemoglobin A2 versus quantitation of Helena isoelectric focusing, the results with the T-test shows no significance.

Paired samples t-test on A2 vs A2E with 130 cases

Alternative = ‘not equal’

Mean A2                   =        3.638

Mean A2E                  =        3.453

Mean difference           =        0.185

SD of difference          =        1.960

t                         =        1.074

df                        =          129

p-value                   =        0.285

Bonferroni adj p-value    =        0.285

Consider overlay box plots of the troponin I means for normal, stable cardiac patients and AMI patients:

The means between two subgroups may be close and the confidence intervals around the means may be wide so that it is not clear whether to accept or reject the null hypothesis.  I illustrate this by taking for comparison the two groups that feature normal cardiac status and stable cardiac disease, neither having myocardial infarction.   I use the nonparametric Kruskal Wallis analysis of ranks between two groups, and I increase the sample size to 100,000 patients by a resampling algorithm.   The result for CKMB and for troponin I is:

Kruskal-Wallis One-Way Analysis of Variance for 93538 cases

Dependent variable is CKMB

Grouping variable is OTHER

Group       Count   Rank Sum

0                     83405            3.64937E+09

1                    10133             7.25351E+08

Mann-Whitney U test statistic =  1.71136E+08

Probability is        0.000

Chi-square approximation =     9619.624 with 1 df

Kruskal-Wallis One-Way Analysis of Variance for 93676 cases

Dependent variable is TROP

Grouping variable is OTHER

Group       Count   Rank Sum

0                    83543             3.59446E+09

1                    10133             7.93180E+08

Mann-Whitney U test statistic =  1.04705E+08

Probability is        0.000

Chi-square approximation =    21850.251 with 1 df

Examine a unique data set in which a test is done on amniotic fluid to determine whether there is adequate surfactant activity so that fetal lung compliance is good at delivery.  If there is inadequate surfactant activity there is risk of respiratory distress of the newborn soon after delivery.  The data includes the measure of surfactant activity, gestational age, and fetal status at delivery.  This study emphasized the calculation of the odds-ratio and probability of RDA using surfactant measurement with, and without gestational age for infants delivered within 72 hours of the test.  The statistical method (Goldmine) has a graphical display with the factor variable as the abscissa and the scaled predictor and odds-ratio as the ordinate.  The data acquisition required a multicenter study of the National Academy of Clinical Biochemistry led by John Chapman (Chapel Hill, NC) and Lawrence Kaplan (Bellevue Hospital, NY, NY), published in Clin Chimica Acta (2002).

The table generated is as follows:

Probability and Odds-Ratios for Regression of S/A on Respiratory Outcomes

S/A interval Probability of RDS Odds Ratio
0 – 10  0.87  713
11 – 20  0.69  239
21 – 34  0.43  80
35 – 44  0.20  27
45 – 54  0.08 9
55 – 70  0.03 3
> 70 0.01 1

There is a plot corresponding to the table above.  It is patented as GOLDminer (graphical ordinal logit display).  As the risk increases, the odds-ratio (and probability of an event)  increases.  The calculation is an advantage when there is more than two values of the factor variable, such as, heart attack, not heart attack, and something else.  We  look at the use of the Goldminer algorithm, this time using the acute myocardial infarction and troponin T example.   The ECG finding is scaled so that the result is normal (0), NSSTT (1), ST depression or t-wave inversion, ST elevation.   The troponin T is scaled to: 0.03, 0.031-0.06, 0.061-0.085, 0.086-0.1, 0.11-0.2, > 0.20 ug/L.   The Goldminer plot is shown below with troponin T as 2nd predictor.

(Joint Y)                                   DXSCALE

average            0                      4

X-profile          score                1.00                 0.00

4,5                   3.64                 0.00                 0.68

4,4                   3.51                 0.00                 0.59

4,3                   3.35                 0.00                 0.48

3,5                   3.07                 0.01                 0.34

4,1                   2.87                 0.02                 0.27

3,4                   2.79                 0.02                 0.24

4,0                   2.54                 0.04                 0.17

3,3                   2.43                 0.06                 0.15

3,2                   2.00                 0.12                 0.08

2,5                   1.88                 0.15                 0.07

3,1                   1.55                 0.23                 0.04

2,4                   1.42                 0.26                 0.03

3,0                   1.12                 0.36                 0.01

2,3                   1.02                 0.40                 0.01

2,2                   0.70                 0.53                 0.00

2,1                   0.47                 0.65                 0.00

2,0                   0.32                 0.74                 0.00

1,3                   0.29                 0.77                 0.00

1,2                   0.20                 0.83                 0.00

1,1                   0.13                 0.88                 0.00

1,0                   0.09                 0.91                 0.00

The table is the table of probabilities from the Goldminer program.   The diagnosis scale 4 is MI.   Diagnosis 0 is baseline normal.

We return to a comparison of CKMB and troponin I.   CKMB may be used as a surrogate test for examining the use of troponin I.   We scale the CKMB to 3 and the troponin to 6 intervals.   We construct a 3-by-6 table shown below, with the chi square analysis.

Frequencies

TNISCALE (rows) by CKMBSCALE (columns)

0 1 2 Total
         0          709          12          9          730
         1          14          0          2          16
         2          3          0          0          3
         3          2          0          0          2
         4          4          0          0          4
         5          22          5          17          44
Total          754          17          28          799

Expected values

TNISCALE (rows) by CKMBSCALE (columns)

0 1 2
   0             688.886             15.532             25.582
   1             15.099             0.340             0.561
   2             2.831             0.064             0.105
   3             1.887             0.043             0.070
   4             3.775             0.085             0.140
   5             41.522             0.936             1.542
Test statistic Value df Prob
Pearson Chi-square             198.580             10.000             0.000

How do we select the best value for a test?  The standard accepted method is a ROC plot.  We have seen how to calculate sensitivity, specificity, and error rates.  The false positive error is 1 – specificity.  The ROC curve plots sensitivity vs 1 – specificity.  The ROC plot requires determination of the “disease” variable by some means other than the test that is being evaluated.   What if the true diagnosis is not accurately known?   The question posed introduces the concept of Latent Class Models.

 

A special nutritional study set was used in which the definition of the effect is not as clear as that for heart attack.  The risk of malnutrition is assessed at the bedside by a dietitian using observed features (presence of wound, malnutrition related condition, and poor oral intake), and by laboratory tests, using serum albumin (protein), red cell hemoglobin, and lymphocyte count.  The composite score was a value of 1 to 4.  Data was collected by Linda Brugler, RD, MBA, at St.FrancisHospital, (Wilmington, DE) on 62 patients to determine whether a better model could be developed using new predictors.

The new predictors were laboratory tests not used in the definition of the risk level, which could be problematic.  The tests albumin, lymphocyte count, and hemoglobin were expected to be highly correlated with the risk level because they were used in its definition.  The prealbumin, but not retinol binding protein or C reactive protein, was correlated with risk score and improved the prediction model.

The crosstable for risk level versus albumin is significant at p < 0.0001.

  A GOLDminer plot showed scaled prealbumin versus levels 3 & 4.    A value less than 5 is severe malnutrition and over 19 is not malnourished.  Mild and moderate malnutrition are between these values.

A method called latent class cluster analysis is used to classify the data.   A latent class is identified when the classification isn’t accurately known.   The result of the analysis is shown in Table 4.   The percent of variable subclasses are shown within each class and total 1.00 (100%).

Cluster1           Cluster2           Cluster3

Cluster Size

0.5545             0.3304             0.1151

PAB1COD

1          0.6841             0.0383             0.0454

2          0.3134             0.6346             0.6662

3          0.0024             0.1781             0.1656

4          0.0001             0.1490             0.1227

ALB0COD

1          0.9491             0.4865             0.1013

2          0.0389             0.1445             0.0869

3          0.0117             0.3167             0.5497

4          0.0003             0.0523             0.2621

LCCOD

1          0.1229             0.0097             0.7600

2          0.3680             0.0687             0.2381

4          0.2297             0.2383             0.0016

5          0.2793             0.6832             0.0002

There are other aspects of informatics that are essential for educational design of the laboratory professional of the future.  These include preparation of powerpoint presentations, use of the internet to obtain current information, quality control designed into the process of handling laboratory testing, evaluating data from different correlated workstations, and instrument integration.   The integrated open architecture will be essential for financial management of the laboratory as well. The continued improvement of the technology base of the laboratory will become routine over the next few years.   The education of the CLS for a professional career in medical technology will require an individual who is adaptive and well prepared for a changing technology environment.   The next section of this document will describe the information structure needed just to carry out the day-to-day operations of the laboratory.

Cost linkages important to define value

Traditional accounting methods do not take into account the cost relationships that are essential for economic survival in a competitive environment so that the only items on the ledger are materials and supplies, labor and benefits, and indirect costs.   This is a description of the business as set forth by an NCCLS cost manual, but it is not sufficient to account for the dimensions of the business in relationship to its activities.   The emergence of spreadsheets, and even as importantly, the development of relational database structures, has transformed and is transforming how we can look at the costing of organizations in relationship to how individuals and groups within the organization carry out the business plan and realize the mission set forth by the governing body.   In this sense, the traditional model was incomplete because it only accounted for the costs incurred by departments in a structure that allocates resources to each department based on the assessed use of resources in providing services.   The model has to account for the allocation of resources to product lines of services (as a DRG model developed by Dr. Eleanor Travers).   A revised model has to take into account two new dimensions.   The first dimension is that of the allocation of resources to provide services that are distinct medical/clinical activities.   This means that in the laboratory service business there may be distinctive services as well as market sectors.   That is, health care organizations view their markets as defined by service Zip codes which delineate the lines drawn between their market and the competition (in the absence of clear overlap).

We have to keep in mind that there are service groups that were defined by John Thompson and Robert Fetter in the development of the DRGs (Diagnosis Related Groups) that have a real relationship to resource requirements for pediatric, geriatric, obstetrics, gynecology, hematology, oncology, cardiology, medical and surgical.   These groups are derived from bundles of ICDs (International Code of Diagnosis) that have comparable within group use of laboratory, radiology, nutrition, pharmacy and other resources.   There was an early concern that there was too much variability within DRGs, which was addressed by severity of illness adjustment (Susan Horn).   It is now clear that ICD’s don’t capture a significant content of the medical record. A method is being devised to correct this problem by Kaiser and Mayo using the SNOMED codes as a starting point.   The point is that it is essential that the activities, resources required, and payment be aligned for validity of the payment system.   Of some interest is the association of severity of illness with more than two comorbidities, and of an association with critical values of a few laboratory tests, e.g., albumin, sodium, potassium, hemoglobin, white cell count.   The actual linkages of these resources to cost of the ten or 20 most common diagnostic categories is only a recent event.   As a rule the top 25 categories account for a substantial volume of the costs that it is of great interest to control.   The improvement of database technology makes it conceivable that 100 categories of disease classification could be controlled without difficulty in the next ten years.

Quality cost synergism

What is traditionally described is only one dimension of the business of the operation.   It is the business of the organization, but it is only one-third of the description of the organization and the costs that drive it.   The second dimension of the organization’s cost profile is only obtained by cost accounting how the organization creates value.   Value is simply the ratio of outputs to inputs.   The traditional cost accounting model looks only at business value added.   The value generated by an organization is attributable to a service or good produced that a customer is willing to purchase.   We have to measure the value by measuring some variable that is highly correlated with the value created.   That measure is partly accounted for by transaction times.  We can borrow from the same model that is used in other industries.   The transportation business is an example.   A colleague has designed a surgical pathology information system on the premise that a report in the pathology office or a phone inquiry by a surgeon is a failure of the service.   This is analogous to the Southeast Airlines mission to have the lowest time on the ground in the industry.   The growing complexity of service needs, the capital requirements to support the needs, and the contractual requirements are driving redesign of services in a constantly changing environment.

 

Technology requirements

We have gone from predominantly batch and large scale production to predominantly random access and a growing point-of-care application with pneumatic tube delivery systems in the acute care setting in the last 15 years.   The emphasis on population-based health and increasing shift from acute care to ambulatory care has increased the pressure for point-of-care testing to reduce second visits for adjustment of medication.   The laboratory, radiology and imaging services, and pharmacy information have to be directed to a medical record that may be accessed in acute care or ambulatory setting.   We not only have the proposition that faster is better, but access is from anyplace and almost anytime – connectivity.

There has been a strategic discussion about configuration of information services that is resolving itself by the needs of the marketplace.   Large, self contained organizations are short-lived, and with the emergence of networked provider organizations there will be no compelling interest in having systems that are not tailored to the variety of applications and environments that are served.   The migration from minicomputer to microcomputer client-server networks will go rapidly to N-tiered systems with distributed object-oriented features.   The need for laboratory information systems as a separate application can be seriously challenged by the new paradigm.

 

Utilization and Cost Linkages

Laboratory utilization has to be looked at from more than one perspective in relationship to costs and revenues.   The redefinition of panels cuts the marginal added cost to produce an additional test, but it doesn’t cut the largest cost in obtaining and processing the specimen.   Unfortunately, there is a fixed cost of the operations that has to be achieved, which also drives the formation of laboratory consolidations to have sufficient volume.    If one looks at the capital requirements and labor to support a minimum volume of testing, the marginal cost of added tests decreases with large volume.   The problem with the consolidation argument is that one has to remove testing from the local site in order to increase the volume with an anticipated effect on cycle time for processing.   There is also a significant resource cost for courier service, specimen handling and reporting.   Lets look at the reverse.   What is the effect of decreasing utilization?   One increases the marginal added cost per unit of testing on specimens or accessions.   There is the same basic fixed cost, and if the volume of testing needed to break even is met, the advantage of additional volume is lost.   Fixing the expected cost per patient or per accession becomes problematic if there is a requirement to reduce utilization.

The key volume for processing in the service sense is the number of specimens processed, which has an enormous impact on the processing requirements (number of tests adds to reagent costs and turnaround time per accession).   The result is that one might consider the reduction of testing that is done to monitor critical patients’ status more frequently than is needed.   One can examine the frequency of the CBC, PT/APTT, panels, electrolytes, glucose, and blood gases in the ICUs.   The use of the laboratory is expected to be more intense, reflecting severity of illness, in this setting.   On the other hand, excess redundancy may reflect testing that makes no meaningful contribution to patient care.   This may be suggested by repeated testing with no significant variation in the lab results.

Intangible elements

Competitive advantage may have marginal costs with enormous value enhancement.   This is in the manner of reporting the results.   My colleagues have proposed the importance of a scale-free representation of the laboratory data for presentation to the provider and the patients.   This can be extended further by the scaling of the normalized data into intervals associated with expected risks for outcomes.   This would move the laboratory into the domain of assisting in the management of population adjusted health outcomes.

Blume P. Design of a clinical laboratory computer system. Laboratory and  Hospital Information Systems. In Clinics Lab Med 1991;11:83-104.

Didner RS. Back-to-front systems design: a guns and butter approach. Proc Intl Ergonomics Assoc 1982;–

Didner RS, Butler KA. Information requirements for user decision support: designing systems from back to front. Proc Intl Conf on Cybernetics and Society. IEEE. 1982;–:415-419.

Bernstein LH. An LIS is not all pluses. MLO 1986;18:75-80.

Bernstein LH, Sachs B. Selecting an automated chemistry analyzer: cost analysis. Amer Clin Prod Rev 1988;–:16-19.

Bernstein L, Sachs E, Stapleton V, Gorton J. Replacement of a laboratory instrument system based on workflow design. Amer Clin Prod Rev 1988; –: 22-24.

Bernstein LH. Computer-assisted restructuring services. Amer Clin Prod Rev1986;9:–

Bernstein LH, Sachs B, Stapleton V, Gorton J, Lardas O. Implementing a laboratory information management system and verifying its performance. Informatics in Pathol 1986;1:224-233.

Bernstein LH. Selecting a laboratory computer system: the importance of auditing laboratory performance. Amer Clin Prod Rev 1985;–:30-33.

Castaneda-Mendez K, Bernstein LH. Linking costs and quality improvement to clinical outcomes through added value. J Healthcare Qual 1997;19:11-16.

Bernstein LH. The contribution of laboratory information systems to quality assurance. Amer Clin Prod Rev 1987;18:10-15.

Bernstein LH. Predicting the costs of laboratory testing. Pathologist 1985;39:–

Bernstein LH, Davis G, Pelton T. Managing and reducing lab costs. MLO 1984;16:53-56.

Bernstein LH, Brouillette R. The negative impact of untimely data in the diagnosis of acute myocardial infarction. Amer Clin Lab 1990;__:38-40.

Bernstein LH, Spiekerman AM, Qamar A, Babb J. Effective resource management using a clinical and laboratory algorithm for chest pain triage. Clin Lab Management Rev 1996;–:143-152.

Shaw-Stiffel TA, Zarny LA, Pleban WE, Rosman DD, Rudolph RA, Bernstein LH. Effect of nutrition status and other factors on length of hospital stay after major gastrointestinal surgery. Nutrition (Intl) 1993;9:140-145.

Bernstein LH. Relationship of nutritional markers to length of hospital stay. Nutrition (Intl)(suppl) 1995;11:205-209.

Bernstein LH, Coles M, Granata A. The BridgeportHospital experience with autologous transfusion in orthopedic surgery. Orthopedics 1997;20:677-680.

Bernstein LH. Realization of the projected impact of a chemistry workflow management system at BridgeportHospital. In Quality and Statistics: Total Quality Management. Kowalewski MJ, Ed. 1994; 120-133 ASTM: STP 1209. Phila, PA.

Bernstein LH, Kleinman GM, Davis GL, Chiga M. Part A reimbursement: what is your role in medical quality assurance? Pathologist 1986;40:–.

Bernstein LH. What constitutes a laboratory quality monitoring program? Amer J Qual Util Rev 1990;5:95-99.

Mozes B, Easterling J, Sheiner LB, Melmon KL, Kline R, Goldman ES, Brown AN. Case-mix adjustment using objective measures of severity: the case for laboratory data. Health Serv Res 1994;28:689711.

            Bernstein LH, Shaw-Stiffel T, Zarny L, Pleban W. An informational approach to likelihood of malnutrition. Nutr (Intl) 1996;12:772-226.

Read Full Post »

%d bloggers like this: