Cancer-associated mutations in protein kinase C theta are loss-of-function.

The Ca2+-independent, but diacylglycerol-regulated, novel protein kinase C (PKC) theta (θ) is highly expressed in hematopoietic cells where it participates in immune signaling and platelet function. Mounting evidence suggests that PKCθ may be involved in cancer, particularly blood cancers, breast cancer, and gastrointestinal stromal tumors, yet how to target this kinase (as an oncogene or as a tumor suppressor) has not been established. Here, we examine the effect of four cancer-associated mutations, R145H/C in the autoinhibitory pseudosubstrate, E161K in the regulatory C1A domain, and R635W in the regulatory C-terminal tail, on the cellular activity and stability of PKCθ.

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.

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.

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.

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.

Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding.

The 534 protein kinases encoded in the human genome constitute a large druggable class of proteins that include both well-studied and understudied "dark" members. Accurate prediction of dark kinase functions is a major bioinformatics challenge. Here, we employ a graph mining approach that uses the evolutionary and functional context encoded in knowledge graphs (KGs) to predict protein and pathway associations for understudied kinases.

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.

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.

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.

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.

Detection of COVID-19 case clusters in Québec, May-October 2020.

Objectives: The Quebec Public Health Institute (INSPQ) was mandated to develop an automated tool for detecting space-time COVID-19 case clusters to assist regional public health authorities in identifying situations that require public health interventions. This article aims to describe the methodology used and to document the main outcomes achieved.

Methods: New COVID-19 cases are supplied by the "Trajectoire de santé publique" information system, geolocated to civic addresses and then aggregated by day and dissemination area.

Assessing Uncertainty and Reliability of Connective Field Estimations From Resting State fMRI Activity at 3T.

Connective Field (CF) modeling estimates the local spatial integration between signals in distinct cortical visual field areas. As we have shown previously using 7T data, CF can reveal the visuotopic organization of visual cortical areas even when applied to BOLD activity recorded in the absence of external stimulation. This indicates that CF modeling can be used to evaluate cortical processing in participants in which the visual input may be compromised.

Propagation of BOLD Activity Reveals Task-dependent Directed Interactions Across Human Visual Cortex.

It has recently been shown that large-scale propagation of blood-oxygen-level-dependent (BOLD) activity is constrained by anatomical connections and reflects transitions between behavioral states. It remains to be seen, however, if the propagation of BOLD activity can also relate to the brain's anatomical structure at a more local scale. Here, we hypothesized that BOLD propagation reflects structured neuronal activity across early visual field maps.

Latency analysis of resting-state BOLD-fMRI reveals traveling waves in visual cortex linking task-positive and task-negative networks.

Due to the low temporal resolution of BOLD-fMRI, imaging studies on human brain function have almost exclusively focused on instantaneous correlations within the data. Developments in hardware and acquisition protocols, however, are offering data with higher sampling rates that allow investigating the latency structure of BOLD-fMRI data. In this study we describe a method for analyzing the latency structure within BOLD-fMRI data and apply it to resting-state data of 94 participants from the Human Connectome Project.

Rigidity in Motor Behavior and Brain Functioning in Patients With Schizophrenia and High Levels of Apathy.

The aim of this study was to investigate whether apathy in schizophrenia is associated with rigidity in behavior and brain functioning. To this end, we studied associations between variability in dynamic functional connectivity (DFC) in relevant functional brain networks, apathy, and variability in physical activity in schizophrenia. Thirty-one patients with schizophrenia, scoring high on apathy, were included and wore an actigraph.

Finger tapping and pre-attentive sensorimotor timing in adults with ADHD.

Sensorimotor timing deficits are considered central to attention-deficit/hyperactivity disorder (ADHD). However, the tasks establishing timing impairments often involve interconnected processes, including low-level sensorimotor timing and higher level executive processes such as attention. Thus, the source of timing deficits in ADHD remains unclear.

Phase-synchronization-based parcellation of resting state fMRI signals reveals topographically organized clusters in early visual cortex.

Resting-state fMRI is widely used to study brain function and connectivity. However, interpreting patterns of resting state (RS) fMRI activity remains challenging as they may arise from different neuronal mechanisms than those triggered by exogenous events. Currently, this limits the use of RS-fMRI for understanding cortical function in health and disease.

Reliability of Sleep Measures from Four Personal Health Monitoring Devices Compared to Research-Based Actigraphy and Polysomnography.

Polysomnography (PSG) is the "gold standard" for monitoring sleep. Alternatives to PSG are of interest for clinical, research, and personal use. Wrist-worn actigraph devices have been utilized in research settings for measures of sleep for over two decades.

Eyes on emergence: Fast detection yet slow recognition of emerging images.

Visual object recognition occurs at the intersection of visual perception and visual cognition. It typically occurs very fast and it has therefore been difficult to disentangle its constituent processes. Recognition time can be extended when using images with emergent properties, suggesting they may help examining how visual recognition unfolds over time.

Cortical connective field estimates from resting state fMRI activity.

One way to study connectivity in visual cortical areas is by examining spontaneous neural activity. In the absence of visual input, such activity remains shaped by the underlying neural architecture and, presumably, may still reflect visuotopic organization. Here, we applied population connective field (CF) modeling to estimate the spatial profile of functional connectivity in the early visual cortex during resting state functional magnetic resonance imaging (RS-fMRI).

Comparison of the hemodynamic effects of sevoflurane anesthesia induction and maintenance vs TIVA in CABG surgery.

Purpose: To compare the hemodynamic effects of sevoflurane when used for induction and maintenance of anesthesia with a total intravenous technique in patients with known coronary artery disease (CAD).

Methods: Thirty patients undergoing elective coronary artery bypass graft (CABG) were randomly allocated to receive either sevoflurane (S group, n = 15) at a minimal concentration of 4% in oxygen for induction and at 0.5-2 MAC end-tidal concentration for maintenance, or a total intravenous technique (T group, n = 15) consisting of midazolam for induction and propofol for maintenance.