Ubiquitin-Dependent Modification of Skeletal Muscle by the Parasitic Nematode, Trichinella spiralis

Echinococcus granulosus ubiquitin-conjugating enzymes (E2D2 and E2N) promote the formation of liver fibrosis in TGFβ1-induced LX-2 cells

BACKGROUND

Cystic echinococcosis (CE) is a widespread zoonosis caused by the infection with Echinococcus granulosus sensu lato (E. granulosus s.l.). CE cysts mainly develop in the liver of intermediate hosts, characterized by the fibrotic tissue that separates host organ from parasite. However, precise mechanism underlying the formation of fibrotic tissue in CE remains unclear.

METHODS

To investigate the potential impact of ubiquitin-conjugating enzymes on liver fibrosis formation in CE, two members of ubiquitin-conjugating (UBC) enzyme of Echinococcus granulosus (EgE2D2 and EgE2N) were recombinantly expressed in Escherichia coli and analyzed for bioinformatics, immunogenicity, localization, and enzyme activity. In addition, the secretory pathway and their effects on the formation of liver fibrosis were also explored.

RESULTS

Both rEgE2D2 and rEgE2N possess intact UBC domains and active sites, exhibiting classical ubiquitin binding activity and strong immunoreactivity. Additionally, EgE2D2 and EgE2N were widely distributed in protoscoleces and germinal layer, with differences observed in their distribution in 25-day strobilated worms. Further, these two enzymes were secreted to the hydatid fluid and CE-infected sheep liver tissues via a non-classical secretory pathway. Notably, TGFβ1-induced LX-2 cells exposed to rEgE2D2 and rEgE2N resulted in increasing expression of fibrosis-related genes, enhancing cell proliferation, and facilitating cell migration.

CONCLUSIONS

Our findings suggest that EgE2D2 and EgE2N could secrete into the liver and may interact with hepatic stellate cells, thereby promoting the formation of liver fibrosis.

Trichinella spiralis inhibits myoblast differentiation by targeting SQSTM1/p62 with a secreted E3 ubiquitin ligase

Trichinella spiralis infection is associated with the formation of cysts within host skeletal muscle cells, thereby enabling immune evasion and subsequent growth and development; however, the pathogenic factors involved in this process and their mechanisms remain elusive. Here, we found that Ts-RNF secreted by T. spiralis is required for its growth and development in host cells. Further study revealed that Ts-RNF functions as an E3 ubiquitin ligase that targets the UBA domain of SQSTM1/p62 by forming K63-type ubiquitin chains. This modification interferes with autophagic flux, leading to impaired mitochondrial clearance and abnormal myotube differentiation and fusion. Our results established that T. spiralis increases its escape by interfering with host autophagy via the secretion of an E3 ubiquitin ligase.

Quantitative label-free proteomic analysis of excretory-secretory proteins in different developmental stages of Trichinella spiralis

Trichinella spiralis (T. spiralis) is a zoonotic parasitic nematode with a unique life cycle, as all developmental stages are contained within a single host. Excretory-secretory (ES) proteins are the main targets of the interactions between T. spiralis and the host at different stages of development and are essential for parasite survival. However, the ES protein profiles of T. spiralis at different developmental stages have not been characterized. The proteomes of ES proteins from different developmental stages, namely, muscle larvae (ML), intestinal infective larvae (IIL), preadult (PA) 6 h, PA 30 h, adult (Ad) 3 days post-infection (dpi) and Ad 6 dpi, were characterized via label-free mass spectrometry analysis in combination with bioinformatics. A total of 1217 proteins were identified from 9341 unique peptides in all developmental stages, 590 of which were quantified and differentially expressed. GO classification and KEGG pathway analysis revealed that these proteins were important for the growth of the larvae and involved in energy metabolism. Moreover, the heat shock cognate 71 kDa protein was the centre of protein interactions at different developmental stages. The results of this study provide comprehensive proteomic data on ES proteins and reveal that these ES proteins were differentially expressed at different developmental stages. Differential proteins are associated with parasite survival and the host immune response and may be potential early diagnostic antigen or antiparasitic vaccine candidates.

Pyruvate dehydrogenase B regulates myogenic differentiation via the FoxP1-Arih2 axis

BACKGROUND

Sarcopenia, the age-related decline in skeletal muscle mass and function, diminishes life quality in elderly people. Improving the capacity of skeletal muscle differentiation is expected to counteract sarcopenia. However, the mechanisms underlying skeletal muscle differentiation are complex, and effective therapeutic targets are largely unknown.

METHODS

The human Gene Expression Omnibus database, aged mice and primary skeletal muscle cells were used to assess the expression level of pyruvate dehydrogenase B (PDHB) in human and mouse aged state. d-Galactose (d-gal)-induced sarcopenia mouse model and two classic cell models (C2C12 and HSkMC) were used to assess the myogenic effect of PDHB and the underlying mechanisms via immunocytochemistry, western blotting, quantitative real-time polymerase chain reaction, RNA interference or overexpression, dual-luciferase reporter assay, RNA sequencing and untargeted metabolomics.

RESULTS

We identified that a novel target PDHB promoted myogenic differentiation. PDHB expression decreased in aged mouse muscle relative to the young state (-50% of mRNA level, P < 0.01) and increased during mouse and primary human muscle cell differentiation (+3.97-fold, P < 0.001 and +3.79-fold, P < 0.001). Knockdown or overexpression of PDHB modulated the expression of genes related to muscle differentiation, namely, myogenic factor 5 (Myf5) (-46%, P < 0.01 and -27%, P < 0.05; +1.8-fold, P < 0.01), myogenic differentiation (MyoD) (-55%, P < 0.001 and -34%, P < 0.01; +2.27-fold, P < 0.001), myogenin (MyoG) (-60%, P < 0.001 and -70%, P < 0.001; +5.46-fold, P < 0.001) and myosin heavy chain (MyHC) (-70%, P < 0.001 and -69%, P < 0.001; +3.44-fold, P < 0.001) in both C2C12 cells and HSkMC. Metabolomic and transcriptomic analyses revealed that PDHB knockdown suppressed pyruvate metabolism (P < 0.001) and up-regulated ariadne RBR E3 ubiquitin protein ligase 2 (Arih2) (+7.23-fold, P < 0.001) in cellular catabolic pathways. The role of forkhead box P1 (FoxP1) (+4.18-fold, P < 0.001)-mediated Arih2 transcription was the key downstream regulator of PDHB in muscle differentiation. PDHB overexpression improved d-gal-induced muscle atrophy in mice, which was characterized by significant increases in grip strength, muscle mass and mean muscle cross-sectional area (1.19-fold to 1.5-fold, P < 0.01, P < 0.05 and P < 0.001).

CONCLUSIONS

The comprehensive results show that PDHB plays a sarcoprotective role by suppressing the FoxP1-Arih2 axis and may serve as a therapeutic target in sarcopenia.

Using Graph-Based Signatures to Guide Rational Antibody Engineering

Antibodies are essential experimental and diagnostic tools and as biotherapeutics have significantly advanced our ability to treat a range of diseases. With recent innovations in computational tools to guide protein engineering, we can now rationally design better antibodies with improved efficacy, stability, and pharmacokinetics. Here, we describe the use of the mCSM web-based in silico suite, which uses graph-based signatures to rapidly identify the structural and functional consequences of mutations, to guide rational antibody engineering to improve stability, affinity, and specificity.

Clinical and epidemiological aspects of feline sporotrichosis caused by Sporothrix brasiliensis and in vitro antifungal susceptibility

Sporotrichosis is a subcutaneous mycosis resulting from the traumatic implantation of pathogenic Sporothrix species. In Brazil, zoonotic transmission plays an important role in the epidemiology of the disease, involving especially cats. The objective of this study was to isolate Sporothrix spp. from cats with signs of sporotrichosis, determining the causative species, clinical and epidemiological aspects, and the in vitro susceptibility profile of the isolates against antifungal drugs. From September 2017 to February 2019, 245 samples of lesions were collected from symptomatic cats in São José do Rio Preto, Brazil. Identification of the isolates was performed by morphophysiological parameters and species-specific polymerase chain reaction. The susceptibility profile of the isolates was determined for five drugs (amphotericin B, itraconazole, ketoconazole, potassium iodide and terbinafine), using the broth microdilution method. Clinical and epidemiological aspects were analyzed based on data contained on investigation forms filled by the veterinarians at moment of collection. Sporothrix spp. were isolated in 189 (77.2%) of the samples. Phenotypic and molecular analyses revealed S. brasiliensis as the only causative agent. In vitro susceptibility testing showed lower MIC values for terbinafine (MIC = 0.03-2 μg/ml), ketoconazole (MIC = 0.03-2 μg/ml), and itraconazole (MIC = 0.03-4 μg/ml). Most of the animals were male (73.5%), adults (96.3%), stray (53.5%), and uncastrated (69.8%). Our results show the expansion of the S. brasiliensis epidemic to an area nearly 840 km apart from the epicenter of the long-lasting outbreak of cat-transmitted sporotrichosis in Rio de Janeiro.

mCSM-membrane: predicting the effects of mutations on transmembrane proteins

Significant efforts have been invested into understanding and predicting the molecular consequences of mutations in protein coding regions, however nearly all approaches have been developed using globular, soluble proteins. These methods have been shown to poorly translate to studying the effects of mutations in membrane proteins. To fill this gap, here we report, mCSM-membrane, a user-friendly web server that can be used to analyse the impacts of mutations on membrane protein stability and the likelihood of them being disease associated. mCSM-membrane derives from our well-established mutation modelling approach that uses graph-based signatures to model protein geometry and physicochemical properties for supervised learning. Our stability predictor achieved correlations of up to 0.72 and 0.67 (on cross validation and blind tests, respectively), while our pathogenicity predictor achieved a Matthew's Correlation Coefficient (MCC) of up to 0.77 and 0.73, outperforming previously described methods in both predicting changes in stability and in identifying pathogenic variants. mCSM-membrane will be an invaluable and dedicated resource for investigating the effects of single-point mutations on membrane proteins through a freely available, user friendly web server at http://biosig.unimelb.edu.au/mcsm_membrane.

DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations

Predicting the effect of missense variations on protein stability and dynamics is important for understanding their role in diseases, and the link between protein structure and function. Approaches to estimate these changes have been proposed, but most only consider single‐point missense variants and a static state of the protein, with those that incorporate dynamics are computationally expensive. Here we present DynaMut2, a web server that combines Normal Mode Analysis (NMA) methods to capture protein motion and our graph‐based signatures to represent the wildtype environment to investigate the effects of single and multiple point mutations on protein stability and dynamics. DynaMut2 was able to accurately predict the effects of missense mutations on protein stability, achieving Pearson's correlation of up to 0.72 (RMSE: 1.02 kcal/mol) on a single point and 0.64 (RMSE: 1.80 kcal/mol) on multiple‐point missense mutations across 10‐fold cross‐validation and independent blind tests. For single‐point mutations, DynaMut2 achieved comparable performance with other methods when predicting variations in Gibbs Free Energy (ΔΔG) and in melting temperature (ΔT_m). We anticipate our tool to be a valuable suite for the study of protein flexibility analysis and the study of the role of variants in disease. DynaMut2 is freely available as a web server and API at http://biosig.unimelb.edu.au/dynamut2.

Identifying Genotype-Phenotype Correlations via Integrative Mutation Analysis

Mutations in protein-coding regions can lead to large biological changes and are associated with genetic conditions, including cancers and Mendelian diseases, as well as drug resistance. Although whole genome and exome sequencing help to elucidate potential genotype-phenotype correlations, there is a large gap between the identification of new variants and deciphering their molecular consequences. A comprehensive understanding of these mechanistic consequences is crucial to better understand and treat diseases in a more personalized and effective way. This is particularly relevant considering estimates that over 80% of mutations associated with a disease are incorrectly assumed to be causative. A thorough analysis of potential effects of mutations is required to correctly identify the molecular mechanisms of disease and enable the distinction between disease-causing and non-disease-causing variation within a gene. Here we present an overview of our integrative mutation analysis platform, which focuses on refining the current genotype-phenotype correlation methods by using the wealth of protein structural information.

De novo Transcriptome Reveals Gene Changes in the Development of the Endosperm Chalazal Haustorium in Taxillus chinensis (DC.) Danser

Loranthus (Taxillus chinensis) is a facultative, hemiparasite and stem parasitic plant that attacks other plants for living. Transcriptome sequencing and bioinformatics analysis were applied in this study to identify the gene expression profiles of fresh seeds (CK), baby (FB), and adult haustoria tissues (FD). We assembled 160,571 loranthus genes, of which 64,926, 35,417, and 47,249 were aligned to NR, GO, and KEGG pathway databases, respectively. We identified 14,295, 15,921, and 16,402 genes in CK, FB, and FD, respectively. We next identified 5,480 differentially expressed genes (DEGs) in the process, of which 258, 174, 81, and 94 were encoding ribosomal proteins (RP), transcription factors (TF), ubiquitin, and disease resistance proteins, respectively. Some DEGs were identified to be upregulated along with the haustoria development (e.g., 68 RP and 26 ubiquitin genes). Notably, 36 RP DEGs peak at FB; 10 ER, 5 WRKY, 6 bHLH, and 4 MYB TF genes upregulated only in FD. Further, we identified 4 out of 32 microRNA genes dysregulated in the loranthus haustoria development. This is the first haustoria transcriptome of loranthus, and our findings will improve our understanding of the molecular mechanism of haustoria.

A Comprehensive Computational Platform to Guide Drug Development Using Graph-Based Signature Methods

High-throughput computational techniques have become invaluable tools to help increase the overall success, process efficiency, and associated costs of drug development. By designing ligands tailored to specific protein structures in a disease of interest, an understanding of molecular interactions and ways to optimize them can be achieved prior to chemical synthesis. This understanding can help direct crucial chemical and biological experiments by maximizing available resources on higher quality leads. Moreover, predicting molecular binding affinity within specific biological contexts, as well as ligand pharmacokinetics and toxicities, can aid in filtering out redundant leads early on within the process. We describe a set of computational tools which can aid in drug discovery at different stages, from hit identification (EasyVS) to lead optimization and candidate selection (CSM-lig, mCSM-lig, Arpeggio, pkCSM). Incorporating these tools along the drug development process can help ensure that candidate leads are chemically and biologically feasible to become successful and tractable drugs.

Arih2 gene influences immune response and tissue development in chicken

Ariadne homolog 2 (ARIH2), as an E3 ubiquitin ligase, is one of the important factors involved in regulating biological functions, such as inflammation and skeletal muscle degeneration. In the present study, the full-length coding sequence of Arih2 gene was cloned from Hy-Line Brown chicken. The tissue transcriptional profiles of Arih2 gene at different developmental stages were detected using quantitative real-time PCR (qRT-PCR), and the Arih2 functional characteristics in immune response were analyzed. The results showed that the full-length coding sequence of Arih2 gene was 1473 bp, encoding 490 amino acids, and conservative between different species. The Arih2 gene was transcribed in various tissues at different developmental stages, and its transcriptional activities varied significantly between multiple tissues. With the development of chicken, Arih2 gene was basically up-regulated in heart, liver, kidney, skeletal muscle and glandular stomach, but fluctuated significantly in large intestine. In immune response, the transcriptional activities of Arih2 gene exhibited significant changes in the bursa, thymus and blood (P<0.05). The results showed that Arih2 might be a multifunctional gene involved in tissue development and immune response in chicken, and have a potential possible application as diagnostic marker for identifying immune response.

mCSM-NA: predicting the effects of mutations on protein-nucleic acids interactions

Over the past two decades, several computational methods have been proposed to predict how missense mutations can affect protein structure and function, either by altering protein stability or interactions with its partners, shedding light into potential molecular mechanisms giving rise to different phenotypes. Effectively and efficiently predicting consequences of mutations on protein–nucleic acid interactions, however, remained until recently a great and unmet challenge. Here we report an updated webserver for mCSM–NA, the only scalable method we are aware of capable of quantitatively predicting the effects of mutations in protein coding regions on nucleic acid binding affinities. We have significantly enhanced the original method by including a pharmacophore modelling and information of nucleic acid properties into our graph-based signatures, considering the reverse mutation and by using a refined, more reliable data set, based on a new release of the ProNIT database, which has significantly improved the reliability and applicability of the methodology. Our new predictive model was capable of achieving a correlation coefficient of up to 0.70 on cross-validation and 0.68 on blind-tests, outperforming its previous version. The server is freely available via a user-friendly web interface at: http://structure.bioc.cam.ac.uk/mcsm_na.

SDM: a server for predicting effects of mutations on protein stability

Here, we report a webserver for the improved SDM, used for predicting the effects of mutations on protein stability. As a pioneering knowledge-based approach, SDM has been highlighted as the most appropriate method to use in combination with many other approaches. We have updated the environment-specific amino-acid substitution tables based on the current expanded PDB (a 5-fold increase in information), and introduced new residue-conformation and interaction parameters, including packing density and residue depth. The updated server has been extensively tested using a benchmark containing 2690 point mutations from 132 different protein structures. The revised method correlates well against the hypothetical reverse mutations, better than comparable methods built using machine-learning approaches, highlighting the strength of our knowledge-based approach for identifying stabilising mutations. Given a PDB file (a Protein Data Bank file format containing the 3D coordinates of the protein atoms), and a point mutation, the server calculates the stability difference score between the wildtype and mutant protein. The server is available at http://structure.bioc.cam.ac.uk/sdm2

Accumulation of α-2,6-sialyoglycoproteins in the Muscle Sarcoplasm Due to Trichinella Sp. Invasion

The sialylation of the glycoproteins in skeletal muscle tissue is not well investigated, even though the essential role of the sialic acids for the proper muscular function has been proven by many researchers. The invasion of the parasitic nematode Trichinella spiralis in the muscles with subsequent formation of Nurse cell-parasite complex initiates increased accumulation of sialylated glycoproteins within the affected area of the muscle fiber. The aim of this study is to describe some details of the α-2,6-sialylation in invaded muscle cells. Asynchronous invasion with infectious T. spiralis larvae was experimentally induced in mice. The areas of the occupied sarcoplasm were reactive towards α-2,6-sialic acid specific Sambucus nigra agglutinin during the whole process of transformation to a Nurse cell.The cytoplasm of the developing Nurse cell reacted with Helix pomatia agglutinin, Arachis hypogea agglutinin and Vicia villosa lectin-B4 after neuraminidase pretreatment.Up-regulation of the enzyme ST6GalNAc1 and down-regulation of the enzyme ST6GalNAc3 were detected throughout the course of this study. The results from our study assumed accumulation of sialyl-Tn-Ag, 6`-sialyl lactosamine, SiA-α-2,6-Gal-β-1,3-GalNAc-α-Ser/Thr and Gal-β-1,3-GalNAc(SiA-α-2,6-)-α-1-Ser/Thr oligosaccharide structures into the occupied sarcoplasm. Further investigations in this domain will develop the understanding about the amazing adaptive capabilities of skeletal muscle tissue.

Exploring Protein Supersecondary Structure Through Changes in Protein Folding, Stability, and Flexibility

The ability to predict how mutations affect protein structure, folding, and flexibility can elucidate the molecular mechanisms leading to disruption of supersecondary structures, the emergence of phenotypes, as well guiding rational protein engineering. The advent of fast and accurate computational tools has enabled us to comprehensively explore the landscape of mutation effects on protein structures, prioritizing mutations for rational experimental validation.Here we describe the use of two complementary web-based in silico methods, DUET and DynaMut, developed to infer the effects of mutations on folding, stability, and flexibility and how they can be used to explore and interpret these effects on protein supersecondary structures.

DynaMut: predicting the impact of mutations on protein conformation, flexibility and stability

Proteins are highly dynamic molecules, whose function is intrinsically linked to their molecular motions. Despite the pivotal role of protein dynamics, their computational simulation cost has led to most structure-based approaches for assessing the impact of mutations on protein structure and function relying upon static structures. Here we present DynaMut, a web server implementing two distinct, well established normal mode approaches, which can be used to analyze and visualize protein dynamics by sampling conformations and assess the impact of mutations on protein dynamics and stability resulting from vibrational entropy changes. DynaMut integrates our graph-based signatures along with normal mode dynamics to generate a consensus prediction of the impact of a mutation on protein stability. We demonstrate our approach outperforms alternative approaches to predict the effects of mutations on protein stability and flexibility (P-value < 0.001), achieving a correlation of up to 0.70 on blind tests. DynaMut also provides a comprehensive suite for protein motion and flexibility analysis and visualization via a freely available, user friendly web server at http://biosig.unimelb.edu.au/dynamut/.

Frequent transmission of the Mycobacterium tuberculosis Beijing lineage and positive selection for the EsxW Beijing variant in Vietnam

To examine the transmission dynamics of Mycobacterium tuberculosis (Mtb) isolated from tuberculosis patients in Ho Chi Minh City, Vietnam, we sequenced the whole genomes of 1,635 isolates and compared these with 3,144 isolates from elsewhere. The data identify an underlying burden of disease caused by the endemic Mtb lineage 1 associated with the activation of long-term latent infection, and a threefold higher burden associated with the more recently introduced Beijing lineage and lineage 4 Mtb strains. We find that Beijing lineage Mtb is frequently transferred between Vietnam and other countries, and detect higher levels of transmission of Beijing lineage strains within this host population than the endemic lineage 1 Mtb. Screening for parallel evolution of Beijing lineage-associated SNPs in other Mtb lineages as a signal of positive selection, we identify an alteration in the ESX-5 type VII-secreted protein EsxW, which could potentially contribute to the enhanced transmission of Beijing lineage Mtb in Vietnamese and other host populations.

Frequent transmission of the Mycobacterium tuberculosis Beijing lineage and positive selection for the EsxW Beijing variant in Vietnam

To examine the transmission dynamics of Mycobacterium tuberculosis (Mtb) isolated from tuberculosis patients in Ho Chi Minh City, Vietnam, we sequenced the whole genomes of 1,635 isolates and compared these with 3,144 isolates from elsewhere. The data identify an underlying burden of disease caused by the endemic Mtb lineage 1 associated with the activation of long-term latent infection, and a threefold higher burden associated with the more recently introduced Beijing lineage and lineage 4 Mtb strains. We find that Beijing lineage Mtb is frequently transferred between Vietnam and other countries, and detect higher levels of transmission of Beijing lineage strains within this host population than the endemic lineage 1 Mtb. Screening for parallel evolution of Beijing lineage-associated SNPs in other Mtb lineages as a signal of positive selection, we identify an alteration in the ESX-5 type VII-secreted protein EsxW, which could potentially contribute to the enhanced transmission of Beijing lineage Mtb in Vietnamese and other host populations.

Trichinella spiralis muscle larvae excretory-secretory products induce changes in cytoskeletal and myogenic transcription factors in primary myoblast cultures

Trichinella spiralis infection in skeletal muscle culminates with nurse cell formation. The participation of excretory-secretory products of the muscle larvae has been implicated in this process through different studies performed in infected muscle and the muscle cell line C2C12. In this work, we developed primary myoblast cultures to analyse the changes induced by excretory-secretory products of the muscle larvae in muscle cells. Microarray analyses revealed expression changes in muscle cell differentiation, proliferation, cytoskeleton organisation, cell motion, transcription, cell cycle, apoptosis and signalling pathways such as MAPK, Jak-STAT, Wnt and PI3K-Akt. Some of these changes were further evaluated by other methodologies such as quantitative real-time PCR (qRT-PCR) and western blot, confirming that excretory-secretory products of the muscle larvae treated primary mouse myoblasts undergo increased proliferation, decreased expression of MHC and up-regulation of α-actin. In addition, changes in relevant muscle transcription factors (Pax7, Myf5 and Mef2c) were observed. Taken together, these results provide new information about how T. spiralis could alter the normal process of skeletal muscle repair after ML invasion to accomplish nurse cell formation.

Combating mutations in genetic disease and drug resistance: understanding molecular mechanisms to guide drug design

INTRODUCTION

Mutations introduce diversity into genomes, leading to selective changes and driving evolution. These changes have contributed to the emergence of many of the current major health concerns of the 21st century, from the development of genetic diseases and cancers to the rise and spread of drug resistance. The experimental systematic testing of all mutations in a system of interest is impractical and not cost-effective, which has created interest in the development of computational tools to understand the molecular consequences of mutations to aid and guide rational experimentation. Areas covered: Here, the authors discuss the recent development of computational methods to understand the effects of coding mutations to protein function and interactions, particularly in the context of the 3D structure of the protein. Expert opinion: While significant progress has been made in terms of innovative tools to understand and quantify the different range of effects in which a mutation or a set of mutations can give rise to a phenotype, a great gap still exists when integrating these predictions and drawing causality conclusions linking variants. This often requires a detailed understanding of the system being perturbed. However, as part of the drug development process it can be used preemptively in a similar fashion to pharmacokinetics predictions, to guide development of therapeutics to help guide the design and analysis of clinical trials, patient treatment and public health policy strategies.