Ian R. Kleckner

An introduction to NMR-based approaches for measuring protein dynamics

Proteins are inherently flexible at ambient temperature. At equilibrium, they are characterized by a set of conformations that undergo continuous exchange within a hierarchy of spatial and temporal scales ranging from nanometers to micrometers and femtoseconds to hours. Dynamic properties of proteins are essential for describing the structural bases of their biological functions including catalysis, binding, regulation and cellular structure. Nuclear magnetic resonance (NMR) spectroscopy represents a powerful technique for measuring these essential features of proteins. Here we provide an introduction to NMR-based approaches for studying protein dynamics, highlighting eight distinct methods with recent examples, contextualized within a common experimental and analytical framework. The selected methods are (1) Real-time NMR, (2) Exchange spectroscopy, (3) Lineshape analysis, (4) CPMG relaxation dispersion, (5) Rotating frame relaxation dispersion, (6) Nuclear spin relaxation, (7) Residual dipolar coupling, (8) Paramagnetic relaxation enhancement. This article is part of a Special Issue entitled: Protein Dynamics: Experimental and Computational Approaches.

Comparison of Pharmaceutical, Psychological, and Exercise Treatments for Cancer-Related Fatigue: A Meta-analysis

Importance Cancer-related fatigue (CRF) remains one of the most prevalent and troublesome adverse events experienced by patients with cancer during and after therapy. Objective To perform a meta-analysis to establish and compare the mean weighted effect sizes (WESs) of the 4 most commonly recommended treatments for CRF—exercise, psychological, combined exercise and psychological, and pharmaceutical—and to identify independent variables associated with treatment effectiveness. Data Sources PubMed, PsycINFO, CINAHL, EMBASE, and the Cochrane Library were searched from the inception of each database to May 31, 2016. Study Selection Randomized clinical trials in adults with cancer were selected. Inclusion criteria consisted of CRF severity as an outcome and testing of exercise, psychological, exercise plus psychological, or pharmaceutical interventions. Data Extraction and Synthesis Studies were independently reviewed by 12 raters in 3 groups using a systematic and blinded process for reconciling disagreement. Effect sizes (Cohen d) were calculated and inversely weighted by SE. Main Outcomes and Measures Severity of CRF was the primary outcome. Study quality was assessed using a modified 12-item version of the Physiotherapy Evidence-Based Database scale (range, 0-12, with 12 indicating best quality). Results From 17 033 references, 113 unique studies articles (11 525 unique participants; 78% female; mean age, 54 [range, 35-72] years) published from January 1, 1999, through May 31, 2016, had sufficient data. Studies were of good quality (mean Physiotherapy Evidence-Based Database scale score, 8.2; range, 5-12) with no evidence of publication bias. Exercise (WES, 0.30; 95% CI, 0.25-0.36; P < .001), psychological (WES, 0.27; 95% CI, 0.21-0.33; P < .001), and exercise plus psychological interventions (WES, 0.26; 95% CI, 0.13-0.38; P < .001) improved CRF during and after primary treatment, whereas pharmaceutical interventions did not (WES, 0.09; 95% CI, 0.00-0.19; P = .05). Results also suggest that CRF treatment effectiveness was associated with cancer stage, baseline treatment status, experimental treatment format, experimental treatment delivery mode, psychological mode, type of control condition, use of intention-to-treat analysis, and fatigue measures (WES range, −0.91 to 0.99). Results suggest that the effectiveness of behavioral interventions, specifically exercise and psychological interventions, is not attributable to time, attention, and education, and specific intervention modes may be more effective for treating CRF at different points in the cancer treatment trajectory (WES range, 0.09-0.22). Conclusions and Relevance Exercise and psychological interventions are effective for reducing CRF during and after cancer treatment, and they are significantly better than the available pharmaceutical options. Clinicians should prescribe exercise or psychological interventions as first-line treatments for CRF.

Tuning Riboswitch Regulation through Conformational Selection

The S(MK) box riboswitch, which represents one of three known classes of S-adenosylmethionine (SAM)-responsive riboswitches, regulates gene expression in bacteria at the level of translation initiation. In contrast to most riboswitches, which contain separate domains responsible for ligand recognition and gene regulation, the ligand-binding and regulatory domains of the S(MK) box riboswitch are coincident. This property was exploited to allow the first atomic-level characterization of a functionally intact riboswitch in both the ligand-bound state and the ligand-free state. NMR spectroscopy revealed distinct mutually exclusive RNA conformations that are differentially populated in the presence or in the absence of the effector metabolite. Isothermal titration calorimetry and in vivo reporter assay results revealed the thermodynamic and functional consequences of this conformational equilibrium. We present a comprehensive model of the structural, thermodynamic, and functional properties of this compact RNA regulatory element.

Cognitive Complaints in Survivors of Breast Cancer After Chemotherapy Compared With Age-Matched Controls: An Analysis From a Nationwide, Multicenter, Prospective Longitudinal Study.

Purpose Cancer-related cognitive impairment is an important problem for patients with breast cancer, yet its trajectory is not fully understood. Some previous cancer-related cognitive impairment research is limited by heterogeneous populations, small samples, lack of prechemotherapy and longitudinal assessments, use of normative data, and lack of generalizability. We addressed these limitations in a large prospective, longitudinal, nationwide study. Patients and Methods Patients with breast cancer from community oncology clinics and age-matched noncancer controls completed the Functional Assessment of Cancer Therapy-Cognitive Function (FACT-Cog) at prechemotherapy and postchemotherapy and at a 6-month follow-up as an a priori exploratory aim. Longitudinal models compared FACT-Cog scores between patients and controls at the three assessments and adjusted for age, education, race, menopausal status, and baseline reading ability, anxiety, and depressive symptoms. A minimal clinically important difference cutoff determined percentages of impairment over time. Results Of patients, 581 patients with breast cancer (mean age, 53 years; 48% anthracycline-based regimens) and 364 controls (mean age, 53 years) were assessed. Patients reported significantly greater cognitive difficulties on the FACT-Cog total score and four subscales from prechemotherapy to postchemotherapy compared with controls as well as from prechemotherapy to 6-month follow-up (all P < .001). Increased baseline anxiety, depression, and decreased cognitive reserve were significantly associated with lower FACT-Cog total scores. Treatment regimen, hormone, or radiation therapy was not significantly associated with FACT-Cog total scores in patients from postchemotherapy to 6-month follow-up. Patients were more likely to report a clinically significant decline in self-reported cognitive function than were controls from prechemotherapy to postchemotherapy (45.2% v 10.4%) and from prechemotherapy to 6-month follow-up (36.5% v 13.6%). Conclusion Patients with breast cancer who were treated in community oncology clinics report substantially more cognitive difficulties up to 6 months after treatment with chemotherapy than do age-matched noncancer controls.

Methodological recommendations for a heartbeat detection-based measure of interoceptive sensitivity.

Heartbeat detection tasks are often used to measure cardiac interoceptive sensitivity-the ability to detect sensations from ones heart. However, there is little work to guide decisions on the optimum number of trials to use, which should balance reliability and power against task duration and participant burden. Here, 174 participants completed 100 trials of a widely used heartbeat detection task where participants attempt to detect whether presented tones occurred synchronously or asynchronously with their heartbeats. First, we quantified measurement reliability of the participants accuracy derived from differing numbers of trials of the task using a correlation metric; we found that at least 40-60 trials were required to yield sufficient reliability. Next, we quantified power by simulating how the number of trials influenced the ability to detect a correlation between cardiac interoceptive sensitivity and other variables that differ across participants, including a variable measured from our sample (body mass index) as well as simulated variables of varying effect sizes. Using these simulations, we quantified the trade-offs between sample size, effect size, and number of trials in the heartbeat detection task such that a researcher can easily determine any one of these variables at given values of the other two variables. We conclude that using fewer than 40 trials is typically insufficient due to poor reliability and low power in estimating an effect size, although the optimal number of trials can differ by study.

Gene regulation by substoichiometric heterocomplex formation of undecameric TRAP and trimeric anti-TRAP

Significance Noncovalent interactions between proteins modulate their functions and occur widely in biological regulation. A large proportion of such regulatory proteins are homo-oligomeric, with multiple copies of a single polypeptide assembled into higher-order quaternary structures. Understanding the regulatory interactions between homo-oligomeric proteins is difficult because their periodic structural configuration may allow different modes of interaction with differing functions. We apply a powerful combination of analytical techniques to study the interaction between TRAP (trp RNA-binding attenuation protein), an 11-mer that regulates tryptophan metabolism by binding RNA, and its trimeric inhibitor protein anti-TRAP. We show that anti-TRAP condenses multiple TRAP oligomers into heterocomplexes, thereby blocking TRAP’s RNA-binding sites. These findings and our approach may have broad implications for other oligomeric regulatory proteins. The control of tryptophan production in Bacillus is a paradigmatic example of gene regulation involving the interplay of multiple protein and nucleic acid components. Central to this combinatorial mechanism are the homo-oligomeric proteins TRAP (trp RNA-binding attenuation protein) and anti-TRAP (AT). TRAP forms undecameric rings, and AT assembles into triskelion-shaped trimers. Upon activation by tryptophan, the outer circumference of the TRAP ring binds specifically to a series of tandem sequences present in the 5′ UTR of RNA transcripts encoding several tryptophan metabolism genes, leading to their silencing. AT, whose expression is up-regulated upon tryptophan depletion to concentrations not exceeding a ratio of one AT trimer per TRAP 11-mer, restores tryptophan production by binding activated TRAP and preventing RNA binding. How the smaller AT inhibitor prevents RNA binding at such low stoichiometries has remained a puzzle, in part because of the large RNA-binding surface on the tryptophan-activated TRAP ring and its high affinity for RNA. Using X-ray scattering, hydrodynamic, and mass spectrometric data, we show that the polydentate action of AT trimers can condense multiple intact TRAP rings into large heterocomplexes, effectively reducing the available contiguous RNA-binding surfaces. This finding reveals an unprecedented mechanism for substoichiometric inhibition of a gene-regulatory protein, which may be a widespread but underappreciated regulatory mechanism in pathways that involve homo-oligomeric or polyvalent components.

Artifact detection in electrodermal activity using sparse recovery

Electrodermal Activity (EDA) – a peripheral index of sympathetic nervous system activity - is a primary measure used in psychophysiology. EDA is widely accepted as an indicator of physiological arousal, and it has been shown to reveal when psychologically novel events occur. Traditionally, EDA data is collected in controlled laboratory experiments. However, recent developments in wireless biosensing have led to an increase in out-of-lab studies. This transition to ambulatory data collection has introduced challenges. In particular, artifacts such as wearer motion, changes in temperature, and electrical interference can be misidentified as true EDA responses. The inability to distinguish artifact from signal hinders analyses of ambulatory EDA data. Though manual procedures for identifying and removing EDA artifacts exist, they are time consuming – which is problematic for the types of longitudinal data sets represented in modern ambulatory studies. This manuscript presents a novel technique to automatically identify and remove artifacts in EDA data using curve fitting and sparse recovery methods. Our method was evaluated using labeled data to determine the accuracy of artifact identification. Procedures, results, conclusions, and future directions are presented.

Social Support, Insomnia, and Adherence to Cognitive Behavioral Therapy for Insomnia After Cancer Treatment

ABSTRACT Objective/Background: While cognitive-behavioral therapy for insomnia (CBT-I) has been shown to be efficacious in treating cancer survivors’ insomnia, 30–60% of individuals have difficulty adhering to intervention components. Psychosocial predictors of adherence and response to CBT-I, such as social support, have not been examined in intervention studies for cancer survivors. Participants: Data from a randomized placebo-controlled 2 x 2 trial of CBT-I and armodafinil (a wakefulness promoting agent) were used to assess adherence. Ninety-six cancer survivors participated in the trial (mean age 56, 86% female, 68% breast cancer). Methods: CBT-I and armodafinil were administered over the course of seven weeks, and participants were assessed at baseline, during intervention, postintervention, and at a three-month follow-up. Social support was assessed using a Functional Assessment of Chronic Illness Therapy subscale, insomnia severity was assessed using the Insomnia Severity Index, and adherence was measured based on CBT-I sleep prescriptions. Results: At baseline, social support was negatively correlated with insomnia severity (r = –0.30, p = 0.002) and associations between social support, CBT-I, and insomnia were maintained through the three-month follow-up. Social support was positively associated with adherence to CBT-I during intervention weeks 3, 4, and 5, and with overall intervention adherence. At postintervention, both social support and treatment with CBT-I independently predicted decreased insomnia severity (p < 0.01) when controlling for baseline insomnia severity. Conclusions: Higher social support is associated with better intervention adherence and improved sleep independent of CBT-I. Additional research is needed to determine whether social support can be leveraged to improve adherence and response to CBT-I.

Dictionary learning and sparse recovery for electrodermal activity analysis

Measures of electrodermal activity (EDA) have advanced research in a wide variety of areas including psychophysiology; however, the majority of this research is typically undertaken in laboratory settings. To extend the ecological validity of laboratory assessments, researchers are taking advantage of advances in wireless biosensors to gather EDA data in ambulatory settings, such as in school classrooms. While measuring EDA in naturalistic contexts may enhance ecological validity, it also introduces analytical challenges that current techniques cannot address. One limitation is the limited efficiency and automation of analysis techniques. Many groups either analyze their data by hand, reviewing each individual record, or use computationally inefficient software that limits timely analysis of large data sets. To address this limitation, we developed a method to accurately and automatically identify SCRs using curve fitting methods. Curve fitting has been shown to improve the accuracy of SCR amplitude and location estimations, but have not yet been used to reduce computational complexity. In this paper, sparse recovery and dictionary learning methods are combined to improve computational efficiency of analysis and decrease run time, while maintaining a high degree of accuracy in detecting SCRs. Here, a dictionary is first created using curve fitting methods for a standard SCR shape. Then, orthogonal matching pursuit (OMP) is used to detect SCRs within a dataset using the dictionary to complete sparse recovery. Evaluation of our method, including a comparison to for speed and accuracy with existing software, showed an accuracy of 80% and a reduced run time.

Yoga for the Management of Cancer Treatment-Related Toxicities

Purpose of ReviewTo (1) explain what yoga is, (2) summarize published literature on the efficacy of yoga for managing cancer treatment-related toxicities, (3) provide clinical recommendations on the use of yoga for oncology professionals, and (4) suggest promising areas for future research.Recent FindingsBased on a total of 24 phase II and one phase III clinical trials, low-intensity forms of yoga, specifically gentle hatha and restorative, are feasible, safe, and effective for treating sleep disruption, cancer-related fatigue, cognitive impairment, psychosocial distress, and musculoskeletal symptoms in cancer patients receiving chemotherapy and radiation and cancer survivors.SummaryClinicians should consider prescribing yoga for their patients suffering with these toxicities by referring them to qualified yoga professionals. More definitive phase III clinical trials are needed to confirm these findings and to investigate other types, doses, and delivery modes of yoga for treating cancer-related toxicities in patients and survivors.