Redox Regulation of Brain Selective Kinases BRSK1/2: Implications for Dynamic Control of the Eukaryotic AMPK family through Cys-based mechanisms

In eukaryotes, protein kinase signaling is regulated by a diverse array of post-translational modifications (PTMs), including phosphorylation of Ser/Thr residues and oxidation of cysteine (Cys) residues. While regulation by activation segment phosphorylation of Ser/Thr residues is well understood, relatively little is known about how oxidation of cysteine residues modulate catalysis. In this study, we investigate redox regulation of the AMPK-related Brain-selective kinases (BRSK) 1 and 2, and detail how broad catalytic activity is directly regulated through reversible oxidation and reduction of evolutionarily conserved Cys residues within the catalytic domain. We show that redox-dependent control of BRSKs is a dynamic and multilayered process involving oxidative modifications of several Cys residues, including the formation of intramolecular disulfide bonds involving a pair of Cys residues near the catalytic HRD motif and a highly conserved T-Loop Cys with a BRSK-specific Cys within an unusual CPE motif at the end of the activation segment. Consistently, mutation of the CPE-Cys increases catalytic activity in vitro and drives phosphorylation of the BRSK substrate Tau in cells. Molecular modeling and molecular dynamics simulations indicate that oxidation of the CPE-Cys destabilizes a conserved salt bridge network critical for allosteric activation. The occurrence of spatially proximal Cys amino acids in diverse Ser/Thr protein kinase families suggests that disulfide mediated control of catalytic activity may be a prevalent mechanism for regulation within the broader AMPK family.

Informatic challenges and advances in illuminating the druggable proteome

The understudied members of the druggable proteomes offer promising prospects for drug discovery efforts. While large-scale initiatives have generated valuable functional information on understudied members of the druggable gene families, translating this information into actionable knowledge for drug discovery requires specialized informatics tools and resources. Here, we review the unique informatics challenges and advances in annotating understudied members of the druggable proteome. We demonstrate the application of statistical evolutionary inference tools, knowledge graph mining approaches, and protein language models in illuminating understudied protein kinases, pseudokinases, and ion channels.

Using explainable machine learning to uncover the kinase-substrate interaction landscape

MOTIVATION

Phosphorylation, a post-translational modification regulated by protein kinase enzymes, plays an essential role in almost all cellular processes. Understanding how each of the nearly 500 human protein kinases selectively phosphorylates their substrates is a foundational challenge in bioinformatics and cell signaling. Although deep learning models have been a popular means to predict kinase-substrate relationships, existing models often lack interpretability and are trained on datasets skewed toward a subset of well-studied kinases.

RESULTS

Here we leverage recent peptide library datasets generated to determine substrate specificity profiles of 300 serine/threonine kinases to develop an explainable Transformer model for kinase-peptide interaction prediction. The model, trained solely on primary sequences, achieved state-of-the-art performance. Its unique multitask learning paradigm built within the model enables predictions on virtually any kinase-peptide pair, including predictions on 139 kinases not used in peptide library screens. Furthermore, we employed explainable machine learning methods to elucidate the model's inner workings. Through analysis of learned embeddings at different training stages, we demonstrate that the model employs a unique strategy of substrate prediction considering both substrate motif patterns and kinase evolutionary features. SHapley Additive exPlanation (SHAP) analysis reveals key specificity determining residues in the peptide sequence. Finally, we provide a web interface for predicting kinase-substrate associations for user-defined sequences and a resource for visualizing the learned kinase-substrate associations.

AVAILABILITY AND IMPLEMENTATION

All code and data are available at https://github.com/esbgkannan/Phosformer-ST. Web server is available at https://phosformer.netlify.app.

Dark kinase annotation, mining, and visualization using the Protein Kinase Ontology

The Protein Kinase Ontology (ProKinO) is an integrated knowledge graph that conceptualizes the complex relationships among protein kinase sequence, structure, function, and disease in a human and machine-readable format. In this study, we have significantly expanded ProKinO by incorporating additional data on expression patterns and drug interactions. Furthermore, we have developed a completely new browser from the ground up to render the knowledge graph visible and interactive on the web. We have enriched ProKinO with new classes and relationships that capture information on kinase ligand binding sites, expression patterns, and functional features. These additions extend ProKinO's capabilities as a discovery tool, enabling it to uncover novel insights about understudied members of the protein kinase family. We next demonstrate the application of ProKinO. Specifically, through graph mining and aggregate SPARQL queries, we identify the p21-activated protein kinase 5 (PAK5) as one of the most frequently mutated dark kinases in human cancers with abnormal expression in multiple cancers, including a previously unappreciated role in acute myeloid leukemia. We have identified recurrent oncogenic mutations in the PAK5 activation loop predicted to alter substrate binding and phosphorylation. Additionally, we have identified common ligand/drug binding residues in PAK family kinases, underscoring ProKinO's potential application in drug discovery. The updated ontology browser and the addition of a web component, ProtVista, which enables interactive mining of kinase sequence annotations in 3D structures and Alphafold models, provide a valuable resource for the signaling community. The updated ProKinO database is accessible at https://prokino.uga.edu.

Mechanistic and evolutionary insights into isoform-specific 'supercharging' in DCLK family kinases

Catalytic signaling outputs of protein kinases are dynamically regulated by an array of structural mechanisms, including allosteric interactions mediated by intrinsically disordered segments flanking the conserved catalytic domain. The doublecortin-like kinases (DCLKs) are a family of microtubule-associated proteins characterized by a flexible C-terminal autoregulatory 'tail' segment that varies in length across the various human DCLK isoforms. However, the mechanism whereby these isoform-specific variations contribute to unique modes of autoregulation is not well understood. Here, we employ a combination of statistical sequence analysis, molecular dynamics simulations, and in vitro mutational analysis to define hallmarks of DCLK family evolutionary divergence, including analysis of splice variants within the DCLK1 sub-family, which arise through alternative codon usage and serve to 'supercharge' the inhibitory potential of the DCLK1 C-tail. We identify co-conserved motifs that readily distinguish DCLKs from all other calcium calmodulin kinases (CAMKs), and a 'Swiss Army' assembly of distinct motifs that tether the C-terminal tail to conserved ATP and substrate-binding regions of the catalytic domain to generate a scaffold for autoregulation through C-tail dynamics. Consistently, deletions and mutations that alter C-terminal tail length or interfere with co-conserved interactions within the catalytic domain alter intrinsic protein stability, nucleotide/inhibitor binding, and catalytic activity, suggesting isoform-specific regulation of activity through alternative splicing. Our studies provide a detailed framework for investigating kinome-wide regulation of catalytic output through cis-regulatory events mediated by intrinsically disordered segments, opening new avenues for the design of mechanistically divergent DCLK1 modulators, stabilizers, or degraders.

An allosteric switch between the activation loop and a c-terminal palindromic phospho-motif controls c-Src function

Autophosphorylation controls the transition between discrete functional and conformational states in protein kinases, yet the structural and molecular determinants underlying this fundamental process remain unclear. Here we show that c-terminal Tyr 530 is a de facto c-Src autophosphorylation site with slow time-resolution kinetics and a strong intermolecular component. On the contrary, activation-loop Tyr 419 undergoes faster kinetics and a cis-to-trans phosphorylation switch that controls c-terminal Tyr 530 autophosphorylation, enzyme specificity, and strikingly, c-Src non-catalytic function as a substrate. In line with this, we visualize by X-ray crystallography a snapshot of Tyr 530 intermolecular autophosphorylation. In an asymmetric arrangement of both catalytic domains, a c-terminal palindromic phospho-motif flanking Tyr 530 on the substrate molecule engages the G-loop of the active kinase adopting a position ready for entry into the catalytic cleft. Perturbation of the phospho-motif accounts for c-Src dysfunction as indicated by viral and colorectal cancer (CRC)-associated c-terminal deleted variants. We show that c-terminal residues 531 to 536 are required for c-Src Tyr 530 autophosphorylation, and such a detrimental effect is caused by the substrate molecule inhibiting allosterically the active kinase. Our work reveals a crosstalk between the activation and c-terminal segments that control the allosteric interplay between substrate- and enzyme-acting kinases during autophosphorylation.

Mechanistic and evolutionary insights into isoform-specific 'supercharging' in DCLK family kinases

Catalytic signaling outputs of protein kinases are dynamically regulated by an array of structural mechanisms, including allosteric interactions mediated by intrinsically disordered segments flanking the conserved catalytic domain. The Doublecortin Like Kinases (DCLKs) are a family of microtubule-associated proteins characterized by a flexible C-terminal autoregulatory 'tail' segment that varies in length across the various human DCLK isoforms. However, the mechanism whereby these isoform-specific variations contribute to unique modes of autoregulation is not well understood. Here, we employ a combination of statistical sequence analysis, molecular dynamics simulations and in vitro mutational analysis to define hallmarks of DCLK family evolutionary divergence, including analysis of splice variants within the DCLK1 sub-family, which arise through alternative codon usage and serve to 'supercharge' the inhibitory potential of the DCLK1 C-tail. We identify co-conserved motifs that readily distinguish DCLKs from all other Calcium Calmodulin Kinases (CAMKs), and a 'Swiss-army' assembly of distinct motifs that tether the C-terminal tail to conserved ATP and substrate-binding regions of the catalytic domain to generate a scaffold for auto-regulation through C-tail dynamics. Consistently, deletions and mutations that alter C-terminal tail length or interfere with co-conserved interactions within the catalytic domain alter intrinsic protein stability, nucleotide/inhibitor-binding, and catalytic activity, suggesting isoform-specific regulation of activity through alternative splicing. Our studies provide a detailed framework for investigating kinome-wide regulation of catalytic output through cis-regulatory events mediated by intrinsically disordered segments, opening new avenues for the design of mechanistically-divergent DCLK1 modulators, stabilizers or degraders.

A comprehensive in vivo screen of yeast farnesyltransferase activity reveals broad reactivity across a majority of CXXX sequences

The current understanding of farnesyltransferase (FTase) specificity was pioneered through investigations of reporters like Ras and Ras-related proteins that possess a C-terminal CaaX motif that consists of 4 amino acid residues: cysteine-aliphatic1-aliphatic2-variable (X). These studies led to the finding that proteins with the CaaX motif are subject to a 3-step post-translational modification pathway involving farnesylation, proteolysis, and carboxylmethylation. Emerging evidence indicates, however, that FTase can farnesylate sequences outside the CaaX motif and that these sequences do not undergo the canonical 3-step pathway. In this work, we report a comprehensive evaluation of all possible CXXX sequences as FTase targets using the reporter Ydj1, an Hsp40 chaperone that only requires farnesylation for its activity. Our genetic and high-throughput sequencing approach reveals an unprecedented profile of sequences that yeast FTase can recognize in vivo, which effectively expands the potential target space of FTase within the yeast proteome. We also document that yeast FTase specificity is majorly influenced by restrictive amino acids at a2 and X positions as opposed to the resemblance of CaaX motif as previously regarded. This first complete evaluation of CXXX space expands the complexity of protein isoprenylation and marks a key step forward in understanding the potential scope of targets for this isoprenylation pathway.

Phosformer: an explainable transformer model for protein kinase-specific phosphorylation predictions

MOTIVATION

The human genome encodes over 500 distinct protein kinases which regulate nearly all cellular processes by the specific phosphorylation of protein substrates. While advances in mass spectrometry and proteomics studies have identified thousands of phosphorylation sites across species, information on the specific kinases that phosphorylate these sites is currently lacking for the vast majority of phosphosites. Recently, there has been a major focus on the development of computational models for predicting kinase-substrate associations. However, most current models only allow predictions on a subset of well-studied kinases. Furthermore, the utilization of hand-curated features and imbalances in training and testing datasets pose unique challenges in the development of accurate predictive models for kinase-specific phosphorylation prediction. Motivated by the recent development of universal protein language models which automatically generate context-aware features from primary sequence information, we sought to develop a unified framework for kinase-specific phosphosite prediction, allowing for greater investigative utility and enabling substrate predictions at the whole kinome level.

RESULTS

We present a deep learning model for kinase-specific phosphosite prediction, termed Phosformer, which predicts the probability of phosphorylation given an arbitrary pair of unaligned kinase and substrate peptide sequences. We demonstrate that Phosformer implicitly learns evolutionary and functional features during training, removing the need for feature curation and engineering. Further analyses reveal that Phosformer also learns substrate specificity motifs and is able to distinguish between functionally distinct kinase families. Benchmarks indicate that Phosformer exhibits significant improvements compared to the state-of-the-art models, while also presenting a more generalized, unified, and interpretable predictive framework.

AVAILABILITY AND IMPLEMENTATION

Code and data are available at https://github.com/esbgkannan/phosformer.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Alignment-free estimation of sequence conservation for identifying functional sites using protein sequence embeddings

Protein language modeling is a fast-emerging deep learning method in bioinformatics with diverse applications such as structure prediction and protein design. However, application toward estimating sequence conservation for functional site prediction has not been systematically explored. Here, we present a method for the alignment-free estimation of sequence conservation using sequence embeddings generated from protein language models. Comprehensive benchmarks across publicly available protein language models reveal that ESM2 models provide the best performance to computational cost ratio for conservation estimation. Applying our method to full-length protein sequences, we demonstrate that embedding-based methods are not sensitive to the order of conserved elements-conservation scores can be calculated for multidomain proteins in a single run, without the need to separate individual domains. Our method can also identify conserved functional sites within fast-evolving sequence regions (such as domain inserts), which we demonstrate through the identification of conserved phosphorylation motifs in variable insert segments in protein kinases. Overall, embedding-based conservation analysis is a broadly applicable method for identifying potential functional sites in any full-length protein sequence and estimating conservation in an alignment-free manner. To run this on your protein sequence of interest, try our scripts at https://github.com/esbgkannan/kibby.

Tree visualizations of protein sequence embedding space enable improved functional clustering of diverse protein superfamilies

Protein language models, trained on millions of biologically observed sequences, generate feature-rich numerical representations of protein sequences. These representations, called sequence embeddings, can infer structure-functional properties, despite protein language models being trained on primary sequence alone. While sequence embeddings have been applied toward tasks such as structure and function prediction, applications toward alignment-free sequence classification have been hindered by the lack of studies to derive, quantify and evaluate relationships between protein sequence embeddings. Here, we develop workflows and visualization methods for the classification of protein families using sequence embedding derived from protein language models. A benchmark of manifold visualization methods reveals that Neighbor Joining (NJ) embedding trees are highly effective in capturing global structure while achieving similar performance in capturing local structure compared with popular dimensionality reduction techniques such as t-SNE and UMAP. The statistical significance of hierarchical clusters on a tree is evaluated by resampling embeddings using a variational autoencoder (VAE). We demonstrate the application of our methods in the classification of two well-studied enzyme superfamilies, phosphatases and protein kinases. Our embedding-based classifications remain consistent with and extend upon previously published sequence alignment-based classifications. We also propose a new hierarchical classification for the S-Adenosyl-L-Methionine (SAM) enzyme superfamily which has been difficult to classify using traditional alignment-based approaches. Beyond applications in sequence classification, our results further suggest NJ trees are a promising general method for visualizing high-dimensional data sets.

Modularity of the hydrophobic core and evolution of functional diversity in fold A glycosyltransferases

Hydrophobic cores are fundamental structural properties of proteins typically associated with protein folding and stability; however, how the hydrophobic core shapes protein evolution and function is poorly understood. Here, we investigated the role of conserved hydrophobic cores in fold-A glycosyltransferases (GT-As), a large superfamily of enzymes that catalyze formation of glycosidic linkages between diverse donor and acceptor substrates through distinct catalytic mechanisms (inverting versus retaining). Using hidden Markov models and protein structural alignments, we identify similarities in the phosphate-binding cassette (PBC) of GT-As and unrelated nucleotide-binding proteins, such as UDP-sugar pyrophosphorylases. We demonstrate that GT-As have diverged from other nucleotide-binding proteins through structural elaboration of the PBC and its unique hydrophobic tethering to the F-helix, which harbors the catalytic base (xED-Asp). While the hydrophobic tethering is conserved across diverse GT-A fold enzymes, some families, such as B3GNT2, display variations in tethering interactions and core packing. We evaluated the structural and functional impact of these core variations through experimental mutational analysis and molecular dynamics simulations and find that some of the core mutations (T336I in B3GNT2) increase catalytic efficiency by modulating the conformational occupancy of the catalytic base between “D-in” and acceptor-accessible “D-out” conformation. Taken together, our studies support a model of evolution in which the GT-A core evolved progressively through elaboration upon an ancient PBC found in diverse nucleotide-binding proteins, and malleability of this core provided the structural framework for evolving new catalytic and substrate-binding functions in extant GT-A fold enzymes.

Functional classification and validation of yeast prenylation motifs using machine learning and genetic reporters

Protein prenylation by farnesyltransferase (FTase) is often described as the targeting of a cysteine-containing motif (CaaX) that is enriched for aliphatic amino acids at the a₁ and a₂ positions, while quite flexible at the X position. Prenylation prediction methods often rely on these features despite emerging evidence that FTase has broader target specificity than previously considered. Using a machine learning approach and training sets based on canonical (prenylated, proteolyzed, and carboxymethylated) and recently identified shunted motifs (prenylation only), this study aims to improve prenylation predictions with the goal of determining the full scope of prenylation potential among the 8000 possible Cxxx sequence combinations. Further, this study aims to subdivide the prenylated sequences as either shunted (i.e., uncleaved) or cleaved (i.e., canonical). Predictions were determined for Saccharomyces cerevisiae FTase and compared to results derived using currently available prenylation prediction methods. In silico predictions were further evaluated using in vivo methods coupled to two yeast reporters, the yeast mating pheromone a-factor and Hsp40 Ydj1p, that represent proteins with canonical and shunted CaaX motifs, respectively. Our machine learning-based approach expands the repertoire of predicted FTase targets and provides a framework for functional classification.

Mapping the glycosyltransferase fold landscape using interpretable deep learning

Glycosyltransferases (GTs) play fundamental roles in nearly all cellular processes through the biosynthesis of complex carbohydrates and glycosylation of diverse protein and small molecule substrates. The extensive structural and functional diversification of GTs presents a major challenge in mapping the relationships connecting sequence, structure, fold and function using traditional bioinformatics approaches. Here, we present a convolutional neural network with attention (CNN-attention) based deep learning model that leverages simple secondary structure representations generated from primary sequences to provide GT fold prediction with high accuracy. The model learns distinguishing secondary structure features free of primary sequence alignment constraints and is highly interpretable. It delineates sequence and structural features characteristic of individual fold types, while classifying them into distinct clusters that group evolutionarily divergent families based on shared secondary structural features. We further extend our model to classify GT families of unknown folds and variants of known folds. By identifying families that are likely to adopt novel folds such as GT91, GT96 and GT97, our studies expand the GT fold landscape and prioritize targets for future structural studies.

KinOrtho: a method for mapping human kinase orthologs across the tree of life and illuminating understudied kinases

BACKGROUND

Protein kinases are among the largest druggable family of signaling proteins, involved in various human diseases, including cancers and neurodegenerative disorders. Despite their clinical relevance, nearly 30% of the 545 human protein kinases remain highly understudied. Comparative genomics is a powerful approach for predicting and investigating the functions of understudied kinases. However, an incomplete knowledge of kinase orthologs across fully sequenced kinomes severely limits the application of comparative genomics approaches for illuminating understudied kinases. Here, we introduce KinOrtho, a query- and graph-based orthology inference method that combines full-length and domain-based approaches to map one-to-one kinase orthologs across 17 thousand species.

RESULTS

Using multiple metrics, we show that KinOrtho performed better than existing methods in identifying kinase orthologs across evolutionarily divergent species and eliminated potential false positives by flagging sequences without a proper kinase domain for further evaluation. We demonstrate the advantage of using domain-based approaches for identifying domain fusion events, highlighting a case between an understudied serine/threonine kinase TAOK1 and a metabolic kinase PIK3C2A with high co-expression in human cells. We also identify evolutionary fission events involving the understudied OBSCN kinase domains, further highlighting the value of domain-based orthology inference approaches. Using KinOrtho-defined orthologs, Gene Ontology annotations, and machine learning, we propose putative biological functions of several understudied kinases, including the role of TP53RK in cell cycle checkpoint(s), the involvement of TSSK3 and TSSK6 in acrosomal vesicle localization, and potential functions for the ULK4 pseudokinase in neuronal development.

CONCLUSIONS

In sum, KinOrtho presents a novel query-based tool to identify one-to-one orthologous relationships across thousands of proteomes that can be applied to any protein family of interest. We exploit KinOrtho here to identify kinase orthologs and show that its well-curated kinome ortholog set can serve as a valuable resource for illuminating understudied kinases, and the KinOrtho framework can be extended to any protein-family of interest.

Evolution of functional diversity in the holozoan tyrosine kinome

The emergence of multicellularity is strongly correlated with the expansion of tyrosine kinases, a conserved family of signaling enzymes that regulates pathways essential for cell-to-cell communication. Although tyrosine kinases have been classified from several model organisms, a molecular-level understanding of tyrosine kinase evolution across all holozoans is currently lacking. Using a hierarchical sequence constraint-based classification of diverse holozoan tyrosine kinases, we construct a new phylogenetic tree that identifies two ancient clades of cytoplasmic and receptor tyrosine kinases separated by the presence of an extended insert segment in the kinase domain connecting the D and E-helices. Present in nearly all receptor tyrosine kinases, this fast-evolving insertion imparts diverse functionalities, such as post-translational modification sites and regulatory interactions. Eph and EGFR receptor tyrosine kinases are two exceptions which lack this insert, each forming an independent lineage characterized by unique functional features. We also identify common constraints shared across multiple tyrosine kinase families which warrant the designation of three new subgroups: Src module (SrcM), insulin receptor kinase-like (IRKL), and fibroblast, platelet-derived, vascular, and growth factor receptors (FPVR). Subgroup-specific constraints reflect shared autoinhibitory interactions involved in kinase conformational regulation. Conservation analyses describe how diverse tyrosine kinase signaling functions arose through the addition of family-specific motifs upon subgroup-specific features and coevolving protein domains. We propose the oldest tyrosine kinases, IRKL, SrcM, and Csk, originated from unicellular premetazoans and were coopted for complex multicellular functions. The increased frequency of oncogenic variants in more recent tyrosine kinases suggests that lineage-specific functionalities are selectively altered in human cancers.

GTXplorer: A portal to navigate and visualize the evolutionary information encoded in fold a glycosyltransferases

Glycosyltransferases (GTs) play a central role in sustaining all forms of life through the biosynthesis of complex carbohydrates. Despite significant strides made in recent years to establish computational resources, databases, and tools to understand the nature and role of carbohydrates and related glycoenzymes, a data analytics framework that connects the sequence-structure-function relationships to the evolution of GTs is currently lacking. This hinders the characterization of understudied GTs and the synthetic design of GTs for medical and biotechnology applications. Here, we present GTXplorer as an integrated platform that presents evolutionary information of GTs adopting a GT-A fold in an intuitive format enabling in silico investigation through comparative sequence analysis to derive informed hypotheses about their function. The tree view mode provides an overview of the evolutionary relationships of GT-A families and allows users to select phylogenetically relevant families for comparisons. The selected families can then be compared in the alignment view at the residue level using annotated weblogo stacks of the GT-A core specific to the selected clade, family, or subfamily. All data are easily accessible and can be downloaded for further analysis. GTXplorer can be accessed at https://vulcan.cs.uga.edu/gtxplorer/ or from GitHub at https://github.com/esbgkannan/GTxplorer to deploy locally. By packaging multiple data streams into an accessible, user-friendly format, GTXplorer presents the first evolutionary data analytics platform for comparative glycomics.

mTORC2 controls the activity of PKC and Akt by phosphorylating a conserved TOR interaction motif

The complex mTORC2 is accepted to be the kinase that controls the phosphorylation of the hydrophobic motif, a key regulatory switch for AGC kinases, although whether mTOR directly phosphorylates this motif remains controversial. Here, we identified an mTOR-mediated phosphorylation site that we termed the TOR interaction motif (TIM; F-x3-F-pT), which controls the phosphorylation of the hydrophobic motif of PKC and Akt and the activity of these kinases. The TIM is invariant in mTORC2-dependent AGC kinases, is evolutionarily conserved, and coevolved with mTORC2 components. Mutation of this motif in Akt1 and PKCβII abolished cellular kinase activity by impairing activation loop and hydrophobic motif phosphorylation. mTORC2 directly phosphorylated the PKC TIM in vitro, and this phosphorylation event was detected in mouse brain. Overexpression of PDK1 in mTORC2-deficient cells rescued hydrophobic motif phosphorylation of PKC and Akt by a mechanism dependent on their intrinsic catalytic activity, revealing that mTORC2 facilitates the PDK1 phosphorylation step, which, in turn, enables autophosphorylation. Structural analysis revealed that PKC homodimerization is driven by a TIM-containing helix, and biophysical proximity assays showed that newly synthesized, unphosphorylated PKC dimerizes in cells. Furthermore, disruption of the dimer interface by stapled peptides promoted hydrophobic motif phosphorylation. Our data support a model in which mTORC2 relieves nascent PKC dimerization through TIM phosphorylation, recruiting PDK1 to phosphorylate the activation loop and triggering intramolecular hydrophobic motif autophosphorylation. Identification of TIM phosphorylation and its role in the regulation of PKC provides the basis for AGC kinase regulation by mTORC2.

Conformational interconversion of MLKL and disengagement from RIPK3 precede cell death by necroptosis

Phosphorylation of the MLKL pseudokinase by the RIPK3 kinase leads to MLKL oligomerization, translocation to, and permeabilization of, the plasma membrane to induce necroptotic cell death. The precise choreography of MLKL activation remains incompletely understood. Here, we report Monobodies, synthetic binding proteins, that bind the pseudokinase domain of MLKL within human cells and their crystal structures in complex with the human MLKL pseudokinase domain. While Monobody-32 constitutively binds the MLKL hinge region, Monobody-27 binds MLKL via an epitope that overlaps the RIPK3 binding site and is only exposed after phosphorylated MLKL disengages from RIPK3 following necroptotic stimulation. The crystal structures identified two distinct conformations of the MLKL pseudokinase domain, supporting the idea that a conformational transition accompanies MLKL disengagement from RIPK3. These studies provide further evidence that MLKL undergoes a large conformational change upon activation, and identify MLKL disengagement from RIPK3 as a key regulatory step in the necroptosis pathway.

Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) model for protein kinase inhibitor response prediction

BACKGROUND

Protein kinases are a large family of druggable proteins that are genomically and proteomically altered in many human cancers. Kinase-targeted drugs are emerging as promising avenues for personalized medicine because of the differential response shown by altered kinases to drug treatment in patients and cell-based assays. However, an incomplete understanding of the relationships connecting genome, proteome and drug sensitivity profiles present a major bottleneck in targeting kinases for personalized medicine.

RESULTS

In this study, we propose a multi-component Quantitative Structure-Mutation-Activity Relationship Tests (QSMART) model and neural networks framework for providing explainable models of protein kinase inhibition and drug response ([Formula: see text]) profiles in cell lines. Using non-small cell lung cancer as a case study, we show that interaction terms that capture associations between drugs, pathways, and mutant kinases quantitatively contribute to the response of two EGFR inhibitors (afatinib and lapatinib). In particular, protein-protein interactions associated with the JNK apoptotic pathway, associations between lung development and axon extension, and interaction terms connecting drug substructures and the volume/charge of mutant residues at specific structural locations contribute significantly to the observed [Formula: see text] values in cell-based assays.

CONCLUSIONS

By integrating multi-omics data in the QSMART model, we not only predict drug responses in cancer cell lines with high accuracy but also identify features and explainable interaction terms contributing to the accuracy. Although we have tested our multi-component explainable framework on protein kinase inhibitors, it can be extended across the proteome to investigate the complex relationships connecting genotypes and drug sensitivity profiles.

Mutual antagonism between Hippo signaling and cyclin E drives intracellular pattern formation

Not much is known about how organelles organize into patterns. In ciliates, the cortical pattern is propagated during "tandem duplication," a cell division that remodels the parental cell into two daughter cells. A key step is the formation of the division boundary along the cell's equator. In Tetrahymena thermophila, the cdaA alleles prevent the formation of the division boundary. We find that the CDAA gene encodes a cyclin E that accumulates in the posterior cell half, concurrently with accumulation of CdaI, a Hippo/Mst kinase, in the anterior cell half. The division boundary forms between the margins of expression of CdaI and CdaA, which exclude each other from their own cortical domains. The activities of CdaA and CdaI must be balanced to initiate the division boundary and to position it along the cell's equator. CdaA and CdaI cooperate to position organelles near the new cell ends. Our data point to an intracellular positioning mechanism involving antagonistic Hippo signaling and cyclin E.

Distinct pseudokinase domain conformations underlie divergent activation mechanisms among vertebrate MLKL orthologues

The MLKL pseudokinase is the terminal effector in the necroptosis cell death pathway. Phosphorylation by its upstream regulator, RIPK3, triggers MLKL's conversion from a dormant cytoplasmic protein into oligomers that translocate to, and permeabilize, the plasma membrane to kill cells. The precise mechanisms underlying these processes are incompletely understood, and were proposed to differ between mouse and human cells. Here, we examine the divergence of activation mechanisms among nine vertebrate MLKL orthologues, revealing remarkable specificity of mouse and human RIPK3 for MLKL orthologues. Pig MLKL can restore necroptotic signaling in human cells; while horse and pig, but not rat, MLKL can reconstitute the mouse pathway. This selectivity can be rationalized from the distinct conformations observed in the crystal structures of horse and rat MLKL pseudokinase domains. These studies identify important differences in necroptotic signaling between species, and suggest that, more broadly, divergent regulatory mechanisms may exist among orthologous pseudoenzymes.

Deep evolutionary analysis reveals the design principles of fold A glycosyltransferases

Glycosyltransferases (GTs) are prevalent across the tree of life and regulate nearly all aspects of cellular functions. The evolutionary basis for their complex and diverse modes of catalytic functions remain enigmatic. Here, based on deep mining of over half million GT-A fold sequences, we define a minimal core component shared among functionally diverse enzymes. We find that variations in the common core and emergence of hypervariable loops extending from the core contributed to GT-A diversity. We provide a phylogenetic framework relating diverse GT-A fold families for the first time and show that inverting and retaining mechanisms emerged multiple times independently during evolution. Using evolutionary information encoded in primary sequences, we trained a machine learning classifier to predict donor specificity with nearly 90% accuracy and deployed it for the annotation of understudied GTs. Our studies provide an evolutionary framework for investigating complex relationships connecting GT-A fold sequence, structure, function and regulation.

Carbohydrates are one of the major groups of large biological molecules that regulate nearly all aspects of life. Yet, unlike DNA or proteins, carbohydrates are made without a template to follow. Instead, these molecules are built from a set of sugar-based building blocks by the intricate activities of a large and diverse family of enzymes known as glycosyltransferases.

An incomplete understanding of how glycosyltransferases recognize and build diverse carbohydrates presents a major bottleneck in developing therapeutic strategies for diseases associated with abnormalities in these enzymes. It also limits efforts to engineer these enzymes for biotechnology applications and biofuel production.

Taujale et al. have now used evolutionary approaches to map the evolution of a major subset of glycosyltransferases from species across the tree of life to understand how these enzymes evolved such precise mechanisms to build diverse carbohydrates. First, a minimal structural unit was defined based on being shared among a group of over half a million unique glycosyltransferase enzymes with different activities. Further analysis then showed that the diverse activities of these enzymes evolved through the accumulation of mutations within this structural unit, as well as in much more variable regions in the enzyme that extend from the minimal unit.

Taujale et al. then built an extended family tree for this collection of glycosyltransferases and details of the evolutionary relationships between the enzymes helped them to create a machine learning framework that could predict which sugar-containing molecules were the raw materials for a given glycosyltransferase. This framework could make predictions with nearly 90% accuracy based only on information that can be deciphered from the gene for that enzyme.

These findings will provide scientists with new hypotheses for investigating the complex relationships connecting the genetic information about glycosyltransferases with their structures and activities. Further refinement of the machine learning framework may eventually enable the design of enzymes with properties that are desirable for applications in biotechnology.

Emerging roles of the αC-β4 loop in protein kinase structure, function, evolution, and disease

The faithful propagation of cellular signals in most organisms relies on the coordinated functions of a large family of protein kinases that share a conserved catalytic domain. The catalytic domain is a dynamic scaffold that undergoes large conformational changes upon activation. Most of these conformational changes, such as movement of the regulatory αC-helix from an "out" to "in" conformation, hinge on a conserved, but understudied, loop termed the αC-β4 loop, which mediates conserved interactions to tether flexible structural elements to the kinase core. We previously showed that the αC-β4 loop is a unique feature of eukaryotic protein kinases. Here, we review the emerging roles of this loop in kinase structure, function, regulation, and diseases. Through a kinome-wide analysis, we define the boundaries of the loop for the first time and show that sequence and structural variation in the loop correlate with conformational and regulatory variation. Many recurrent disease mutations map to the αC-β4 loop and contribute to drug resistance and abnormal kinase activation by relieving key auto-inhibitory interactions associated with αC-helix and inter-lobe movement. The αC-β4 loop is a hotspot for post-translational modifications, protein-protein interaction, and Hsp90 mediated folding. Our kinome-wide analysis provides insights for hypothesis-driven characterization of understudied kinases and the development of allosteric protein kinase inhibitors.

Virtual multiplex immunohistochemistry: Application on cell block of effusion and aspiration cytology

BACKGROUND

With the continuous development of new antibodies, the use of immunohistochemistry (IHC) is becoming more often a requirement. IHC is frequently necessary for establishing cancer diagnosis and making therapeutic decisions. However, cytology specimens such as effusion fluid and fine-needle aspiration are highly variable in cellularity and contain background inflammatory and mesothelial cells. Compared to biopsy or excision specimens, tissue exhaustion and levels of sections not matching are more commonly encountered. We present a method of integrating whole-slide cell block image of multiple antibodies by digital image processing and reconstructing a virtual multiplex IHC image for enhanced interpretation.

METHODS

Historic archived cell block preparations of carcinomas (n = 19) and melanoma (n = 1) and IHC performed with 3,3'-diaminobenzidine chromogen were reviewed. The slides were digitized by a whole-slide image scanner. Using ImageJ and self-developed code, the slides were aligned by image registration, layered, and recolored to reconstruct a virtual multiplex image, simulating a multiplex preparation with multiple chromogens. To quantity the performance of the image registration, the mean distance between the same cell clusters in aligned images were measured.

RESULTS

All 20 cases were successfully registered. The mean distance between cell clusters after image registration was 8.40 +/- 5.52 μm. The reconstructed images were able to demonstrate coexpression of membranous, cytoplasmic, and nuclear antibodies.

CONCLUSION

With virtual multiplex IHC, we were able to visualize coexpression of multiple antibodies without the added cost of multiplex IHC. Routine and historic slides, without additional tissue consumption, can be retrieved for image reconstruction. This technique is a low-cost adjunct to diagnosis in cytology for more efficient and accurate assessment of antibody coexpression and histochemical stains.

Evolutionary Outcomes of Diversely Functionalized Aptamers Isolated from in Vitro Evolution

Expanding the chemical diversity of aptamers remains an important thrust in the field in order to increase their functional potential. Previously, our group developed LOOPER, which enables the incorporation of up to 16 unique modifications throughout a ssDNA sequence, and applied it to the in vitro evolution of thrombin binders. As LOOPER-derived highly modified nucleic acids polymers are governed by two interrelated evolutionary variables, namely, functional modifications and sequence, the evolution of this polymer contrasts with that of canonical DNA. Herein we provide in-depth analysis of the evolution, including structure-activity relationships, mapping of evolutionary pressures on the library, and analysis of plausible evolutionary pathways that resulted in the first LOOPER-derived aptamer, TBL1. A detailed picture of how TBL1 interacts with thrombin and how it may mimic known peptide binders of thrombin is also proposed. Structural modeling and folding studies afford insights into how the aptamer displays critical modifications and also how modifications enhance the structural stability of the aptamer. A discussion of benefits and potential limitations of LOOPER during in vitro evolution is provided, which will serve to guide future evolutions of this highly modified class of aptamers.

The crystal structure of the protein kinase HIPK2 reveals a unique architecture of its CMGC-insert region

The homeodomain-interacting protein kinase (HIPK) family is comprised of four nuclear protein kinases, HIPK1-4. HIPK proteins phosphorylate a diverse range of transcription factors involved in cell proliferation, differentiation, and apoptosis. HIPK2, thus far the best-characterized member of this largely understudied family of protein kinases, plays a role in the activation of p53 in response to DNA damage. Despite this tumor-suppressor function, HIPK2 is also found overexpressed in several cancers, and its hyperactivation causes chronic fibrosis. There are currently no structures of HIPK2 or of any other HIPK kinase. Here, we report the crystal structure of HIPK2's kinase domain bound to CX-4945, a casein kinase 2α (CK2α) inhibitor currently in clinical trials against several cancers. The structure, determined at 2.2 Å resolution, revealed that CX-4945 engages the HIPK2 active site in a hybrid binding mode between that seen in structures of CK2α and Pim1 kinases. The HIPK2 kinase domain crystallized in the active conformation, which was stabilized by phosphorylation of the activation loop. We noted that the overall kinase domain fold of HIPK2 closely resembles that of evolutionarily related dual-specificity tyrosine-regulated kinases (DYRKs). Most significant structural differences between HIPK2 and DYRKs included an absence of the regulatory N-terminal domain and a unique conformation of the CMGC-insert region and of a newly defined insert segment in the αC-β4 loop. This first crystal structure of HIPK2 paves the way for characterizing the understudied members of the HIPK family and for developing HIPK2-directed therapies for managing cancer and fibrosis.

Tracing the origin and evolution of pseudokinases across the tree of life

Protein phosphorylation by eukaryotic protein kinases (ePKs) is a fundamental mechanism of cell signaling in all organisms. In model vertebrates, ~10% of ePKs are classified as pseudokinases, which have amino acid changes within the catalytic machinery of the kinase domain that distinguish them from their canonical kinase counterparts. However, pseudokinases still regulate various signaling pathways, usually doing so in the absence of their own catalytic output. To investigate the prevalence, evolutionary relationships, and biological diversity of these pseudoenzymes, we performed a comprehensive analysis of putative pseudokinase sequences in available eukaryotic, bacterial, and archaeal proteomes. We found that pseudokinases are present across all domains of life, and we classified nearly 30,000 eukaryotic, 1500 bacterial, and 20 archaeal pseudokinase sequences into 86 pseudokinase families, including ~30 families that were previously unknown. We uncovered a rich variety of pseudokinases with notable expansions not only in animals but also in plants, fungi, and bacteria, where pseudokinases have previously received cursory attention. These expansions are accompanied by domain shuffling, which suggests roles for pseudokinases in plant innate immunity, plant-fungal interactions, and bacterial signaling. Mechanistically, the ancestral kinase fold has diverged in many distinct ways through the enrichment of unique sequence motifs to generate new families of pseudokinases in which the kinase domain is repurposed for noncanonical nucleotide binding or to stabilize unique, inactive kinase conformations. We further provide a collection of annotated pseudokinase sequences in the Protein Kinase Ontology (ProKinO) as a new mineable resource for the signaling community.

Covalent inhibitors of EGFR family protein kinases induce degradation of human Tribbles 2 (TRIB2) pseudokinase in cancer cells

A major challenge associated with biochemical and cellular analysis of pseudokinases is a lack of target-validated small-molecule compounds with which to probe function. Tribbles 2 (TRIB2) is a cancer-associated pseudokinase with a diverse interactome, including the canonical AKT signaling module. There is substantial evidence that human TRIB2 promotes survival and drug resistance in solid tumors and blood cancers and therefore is of interest as a therapeutic target. The unusual TRIB2 pseudokinase domain contains a unique cysteine-rich C-helix and interacts with a conserved peptide motif in its own carboxyl-terminal tail, which also supports its interaction with E3 ubiquitin ligases. We found that TRIB2 is a target of previously described small-molecule protein kinase inhibitors, which were originally designed to inhibit the canonical kinase domains of epidermal growth factor receptor tyrosine kinase family members. Using a thermal shift assay, we discovered TRIB2-binding compounds within the Published Kinase Inhibitor Set (PKIS) and used a drug repurposing approach to classify compounds that either stabilized or destabilized TRIB2 in vitro. TRIB2 destabilizing agents, including the covalent drug afatinib, led to rapid TRIB2 degradation in human AML cancer cells, eliciting tractable effects on signaling and survival. Our data reveal new drug leads for the development of TRIB2-degrading compounds, which will also be invaluable for unraveling the cellular mechanisms of TRIB2-based signaling. Our study highlights that small molecule-induced protein down-regulation through drug "off-targets" might be relevant for other inhibitors that serendipitously target pseudokinases.

Radiological "Teddy Bear" Sign on CT Imaging to Aid Internal Carotid Artery Localization in Transsphenoidal Pituitary and Anterior Skull Base Surgery

Objectives  Internal carotid artery (ICA) injury remains a rare but potentially fatal complication of transsphenoidal pituitary or anterior skull base surgery. Preoperative imaging must be scrutinized to minimize risk. On axial computed tomography (CT), the protrusions of the ICAs into the sphenoid resemble a "teddy bear." This article aims to describe the sign, its grading system (0-2) and quantify its presence. Design  Retrospective review of preoperative CT imaging. Setting  Tertiary referral center in the United Kingdom. Participants  One hundred patients who underwent endoscopic transsphenoidal surgery for pituitary disease were enrolled. Main Outcome Measure  The presence and grading of the "teddy bear" sign were assessed on preoperative CT imaging. Results  A grade 2 (strongly positive) "teddy bear" sign was identified in 40% at the level of the superior pituitary fossa, 78% at the inferior pituitary fossa, and 59% at the clivus. A grade 1 (intermediate) sign was seen in 23.5, 7.5, and 10% of cases, respectively. In 5% of cases, the sign was grade 0 at all levels-indicating poor intraoperative localization of the ICA. Conclusion  The "teddy bear" sign is a useful preoperative tool for identification of anatomy predisposing patients to a higher risk of ICA injury. Those patients who have an absent or grade 0 "teddy bear" sign require extra care to ensure intraoperative localization of the ICAs which may include the use of neuronavigation or a Doppler probe. A grade 2 sign predicts good intraoperative localization of the ICA intraoperatively to inform the safe lateral limit of sellar bone resection.

A study of human neutrophil antigen genotype frequencies in Hong Kong

BACKGROUND

Alloantibodies against human neutrophil antigens (HNA) are associated with a variety of clinical conditions. Over the past decade, the allelic and genotypic frequencies of the five HNA systems have been evaluated. Although the HNA system is less polymorphic than human leukocyte antigens (HLA), significant differences in the genotypic and allele frequencies still exist in different populations, even those living in close proximity.

OBJECTIVES

To delineate HNA genotypic and allele frequencies to provide vital information on estimating the risk of HNA-associated diseases for our local population.

METHODS

Using a validated, in-house-developed assay, genotyping for HNA-1, HNA-3, HLA-4 and HNA-5 was performed on 300 samples from Chinese blood donors from Hong Kong. In addition, the frequency of the HNA-2 c.843A > T allele was also determined.

RESULTS

The allele frequencies of HNA-1a, -1b and -1c alleles were 67·8, 31·5 and 0%, respectively, whereas the frequencies of HNA-3a and HNA-3b were 71·0 and 29·0%, respectively. The frequencies of HNA-4a and -4b alleles were 99·5 and 0·5%, respectively, and for HNA-5a and -5b, alleles were 85·2 and 14·8%, respectively. Homozygotes for the HNA-2 c.843 TT variant were absent in our population, whereas only <4% of the population were c.843AT heterozygote carriers.

CONCLUSIONS

This is the first study to define HNA genotype and allele frequencies using a validated modified in-house PCR-SSP method in the Hong Kong Chinese blood donor population. Our approach provides a cost-effective assay for conducting routine HNA typing and facilitates the incorporation of these assays into routine clinical service. Our results are comparable with those reported in the Guangzhou Chinese population, but the allele frequencies in our Hong Kong Chinese population are significantly different from the reported European frequencies, confirming that a geographical difference exists for HNA allele frequencies.

In Vitro Selection of Diversely Functionalized Aptamers

We describe the application of T4 DNA ligase-catalyzed DNA templated oligonucleotide polymerization toward the evolution of a diversely functionalized nucleic acid aptamer for human α-thrombin. Using a 256-membered ANNNN comonomer library comprising 16 sublibraries modified with different functional groups, a highly functionalized aptamer for thrombin was raised with a dissociation constant of 1.6 nM. The aptamer was found to be selective for thrombin and required the modifications for binding affinity. This study demonstrates the most differentially functionalized nucleic acid aptamer discovered by in vitro selection and should enable the future exploration of functional group dependence during the evolution of nucleic acid polymer activity.

Enzymatic Synthesis of Sequence-Defined Synthetic Nucleic Acid Polymers with Diverse Functional Groups

The development and in-depth analysis of T4 DNA ligase-catalyzed DNA templated oligonucleotide polymerization toward the generation of diversely functionalized nucleic acid polymers is described. The NNNNT codon set enables low codon bias, high fidelity, and high efficiency for the polymerization of ANNNN libraries comprising various functional groups. The robustness of the method was highlighted in the copolymerization of a 256-membered ANNNN library comprising 16 sub-libraries modified with different functional groups. This enabled the generation of diversely functionalized synthetic nucleic acid polymer libraries with 93.8 % fidelity. This process should find ready application in DNA nanotechnology, DNA computing, and in vitro evolution of functional nucleic acid polymers.

Generation of Synthetic Copolymer Libraries by Combinatorial Assembly on Nucleic Acid Templates

Recent advances in nucleic acid-templated copolymerization have expanded the scope of sequence-controlled synthetic copolymers beyond the molecular architectures witnessed in nature. This has enabled the power of molecular evolution to be applied to synthetic copolymer libraries to evolve molecular function ranging from molecular recognition to catalysis. This Review seeks to summarize different approaches available to generate sequence-defined monodispersed synthetic copolymer libraries using nucleic acid-templated polymerization. Key concepts and principles governing nucleic acid-templated polymerization, as well as the fidelity of various copolymerization technologies, will be described. The Review will focus on methods that enable the combinatorial generation of copolymer libraries and their molecular evolution for desired function.

Hedgehog signaling induces osteosarcoma development through Yap1 and H19 overexpression

Osteosarcoma is one of the most common bone tumors. However, the genetic basis for its pathogenesis remains elusive. Here, we investigated the roles of Hedgehog (Hh) signaling in osteosarcoma development. Genetically-engineered mice with ubiquitous upregulated Hh signaling specifically in mature osteoblasts develop focal bone overgrowth, which greatly resembles the early stage of osteosarcoma. However, these mice die within three months, which prohibits further analysis of tumor progression. We therefore generated a mouse model with partial upregulated Hh signaling in mature osteoblasts and crossed it into a p53 heterozygous background to potentiate tumor development. We found that these mutant mice developed malignant osteosarcoma with high penetrance. Isolated primary tumor cells were mainly osteoblastic and highly proliferative with many characteristics of human osteosarcomas. Allograft transplantation into immunocompromised mice displayed high tumorigenic potential. More importantly, both human and mouse tumor tissues express high level of yes-associated protein 1 (Yap1), a potent oncogene that is amplified in various cancers. We show that inhibition of Hh signaling reduces Yap1 expression and knockdown of Yap1 significantly inhibits tumor progression. Moreover, long non-coding RNA H19 is aberrantly expressed and induced by upregulated Hh signaling and Yap1 overexpression. Our results demonstrate that aberrant Hh signaling in mature osteoblasts is responsible for the pathogenesis of osteoblastic osteosarcoma through Yap1 and H19 overexpression.

A new microparticle size calibration standard for use in measuring smaller microparticles using a new flow cytometer

BACKGROUND

 Microparticle size measurements are often calibrated on flow cytometers using polystyrene microspheres that forward scatter more light vs. particle diameter than cellular microparticles.

METHODS

 We compared theoretical with measured forward angle light scattering on the LSRII, FC500 and Apogee A40 using polystyrene and silica microspheres vs. synthetic lipid vesicles and platelets, then compared plasma microparticle counts using different calibration strategies.

RESULTS

 Polystyrene and silica microspheres with higher refractive indices forward scattered more light with a wavelength of 488 nm for a given size microparticle than did lipid vesicles or platelets. The LSRII and FC500 did not count many, and were unable to separate by size, polystyrene microspheres <0.5 μm in diameter. On the Apogee A40, polystyrene microspheres could be separated by size down to 0.2 μm, and a polystyrene microsphere 0.4 μm in diameter produced the same forward scatter relative intensity as a 1-μm lipid or cellular microparticle. Using the new calibrator, the Apogee A40 found 80 000-4 000 000 μL(-1) total microparticles, 11 000-350 000 μL(-1) annexin V positive microparticles and 6000-350 000 μL(-1) platelet microparticles <1 μm in plasma samples.

CONCLUSIONS

 The Apogee A40 was able to resolve size differences in polystyrene microspheres down to 0.2 μm and microparticles down to 0.4 μm. On the Apogee A40 we propose using a 0.4-μm polystyrene microsphere as equivalent to a 1-μm cellular microparticle for size calibration. Using this calibrator, the Apogee A40 detected higher numbers of total, platelet and annexin V positive microparticles than were found using a Megamix gate.

Pathways from emotional adjustment to glycemic control in youths with diabetes in Hong Kong

OBJECTIVE

To examine factors that influence emotional adjustment, adherence to diabetic care, and glycemic control in Hong Kong youths with insulin-dependent diabetes mellitus (IDDM).

METHODS

Seventy youths, their mothers, and matched controls provided information on health beliefs, authoritarian parenting style, parent-child conflict, emotional adjustment, and adherence to medical regimen. Glycosylated hemoglobin levels were obtained to measure glycemic control.

RESULTS

Predictors explained 34% of the variance in emotional adjustment and 39% of the variance in glycemic control. The data supported a pathway from emotional adjustment to self-efficacy to adherence behaviors to glycemic control. In contrast to Western culture and consistent with prediction, parenting style did not associate with negative outcomes, and even relatively low levels of parent-child conflict correlated negatively with emotional adjustment in this culture.

CONCLUSIONS

Management of conflict and self-efficacy enhancing interactions are suggested interventions to enhance adherence to diabetic care in Hong Kong youths with IDDM.

Chinese herbal medicine and acupuncture. How do patients who consult family physicians use these therapies?

OBJECTIVE

To determine how a population of Chinese patients consulting family physicians in Vancouver use traditional Chinese medicine (TCM), specifically Chinese herbal medicine and acupuncture.

DESIGN

Bilingual survey (English and Chinese).

SETTING

Four family practices with predominantly Chinese patients in metropolitan Vancouver.

PARTICIPANTS

The 932 patients or family members who visited one of the practices.

MAIN OUTCOME MEASURES

Demographic characteristics; frequency and reason for visiting a family physician, Chinese herbalist, or acupuncturist; choice of practitioner if affected by one of 16 common conditions.

RESULTS

The study population was mostly Chinese and immigrant to Canada. Chinese herbal medicine was currently used by 28% (262/930) of respondents (more than one visit in the last year), and another 18% (172/930) were past users. Acupuncture was currently used by 7% (64/927) and had been used in the past by another 8% (71/927). Use of Chinese herbal medicine varied significantly (P < .01) according to age, sex, immigrant status, and ethnicity. Acupuncture use varied significantly only by age. The main reasons for consulting Chinese herbalists were infection (41%, 157/382), respiratory problems (11%, 42/382), and rheumatologic problems (10%, 38/382), whereas acupuncturists were consulted almost exclusively for rheumatologic problems (80%, 45/56).

CONCLUSIONS

Using TCM in conjunction with visiting family physicians was very popular among this predominantly Chinese study population. Patients with acute conditions, such as influenza, consulted both their family physicians and Chinese herbalists in quick succession. On the other hand, those suffering from more chronic conditions, such as rheumatologic diseases, were more likely to start using TCM after repeated visits to their family physicians.