Overview of the Knowledge Management Center for Illuminating the Druggable Genome.

The Knowledge Management Center (KMC) for the Illuminating the Druggable Genome (IDG) project aims to aggregate, update, and articulate protein-centric data knowledge for the entire human proteome, with emphasis on the understudied proteins from the three IDG protein families. KMC collates and analyzes data from over 70 resources to compile the Target Central Resource Database (TCRD), which is the web-based informatics platform (Pharos). These data include experimental, computational, and text-mined information on protein structures, compound interactions, and disease and phenotype associations. Based on this knowledge, proteins are classified into different Target Development Levels (TDLs) for identification of understudied targets. Additional work by the KMC focuses on enriching target knowledge and producing DrugCentral and other data visualization tools for expanding investigation of understudied targets.

Pharos 2023: an integrated resource for the understudied human proteome.

The Illuminating the Druggable Genome (IDG) project aims to improve our understanding of understudied proteins and our ability to study them in the context of disease biology by perturbing them with small molecules, biologics, or other therapeutic modalities. Two main products from the IDG effort are the Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/), which curates and aggregates information, and Pharos (https://pharos.nih.gov/), a web interface for fusers to extract and visualize data from TCRD. Since the 2021 release, TCRD/Pharos has focused on developing visualization and analysis tools that help reveal higher-level patterns in the underlying data. The current iterations of TCRD and Pharos enable users to perform enrichment calculations based on subsets of targets, diseases, or ligands and to create interactive heat maps and UpSet charts of many types of annotations. Using several examples, we show how to address disease biology and drug discovery questions through enrichment calculations and UpSet charts.

Getting Started with the IDG KMC Datasets and Tools.

The Illuminating the Druggable Genome (IDG) consortium is a National Institutes of Health (NIH) Common Fund program designed to enhance our knowledge of under-studied proteins, more specifically, proteins unannotated within the three most commonly drug-targeted protein families: G-protein coupled receptors, ion channels, and protein kinases. Since 2014, the IDG Knowledge Management Center (IDG-KMC) has generated several open-access datasets and resources that jointly serve as a highly translational machine-learning-ready knowledgebase focused on human protein-coding genes and their products. The goal of the IDG-KMC is to develop comprehensive integrated knowledge for the druggable genome to illuminate the uncharacterized or poorly annotated portion of the druggable genome. The tools derived from the IDG-KMC provide either user-friendly visualizations or ways to impute the knowledge about potential targets using machine learning strategies. In the following protocols, we describe how to use each web-based tool to accelerate illumination in under-studied proteins. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Interacting with the Pharos user interface Basic Protocol 2: Accessing the data in Harmonizome Basic Protocol 3: The ARCHS4 resource Basic Protocol 4: Making predictions about gene function with PrismExp Basic Protocol 5: Using Geneshot to illuminate knowledge about under-studied targets Basic Protocol 6: Exploring under-studied targets with TIN-X Basic Protocol 7: Interacting with the DrugCentral user interface Basic Protocol 8: Estimating Anti-SARS-CoV-2 activities with DrugCentral REDIAL-2020 Basic Protocol 9: Drug Set Enrichment Analysis using Drugmonizome Basic Protocol 10: The Drugmonizome-ML Appyter Basic Protocol 11: The Harmonizome-ML Appyter Basic Protocol 12: GWAS target illumination with TIGA Basic Protocol 13: Prioritizing kinases for lists of proteins and phosphoproteins with KEA3 Basic Protocol 14: Converting PubMed searches to drug sets with the DrugShot Appyter.

TCRD and Pharos 2021: mining the human proteome for disease biology.

In 2014, the National Institutes of Health (NIH) initiated the Illuminating the Druggable Genome (IDG) program to identify and improve our understanding of poorly characterized proteins that can potentially be modulated using small molecules or biologics. Two resources produced from these efforts are: The Target Central Resource Database (TCRD) (http://juniper.health.unm.edu/tcrd/) and Pharos (https://pharos.nih.gov/), a web interface to browse the TCRD. The ultimate goal of these resources is to highlight and facilitate research into currently understudied proteins, by aggregating a multitude of data sources, and ranking targets based on the amount of data available, and presenting data in machine learning ready format. Since the 2017 release, both TCRD and Pharos have produced two major releases, which have incorporated or expanded an additional 25 data sources. Recently incorporated data types include human and viral-human protein-protein interactions, protein-disease and protein-phenotype associations, and drug-induced gene signatures, among others. These aggregated data have enabled us to generate new visualizations and content sections in Pharos, in order to empower users to find new areas of study in the druggable genome.

Graded Coexpression of Ion Channel, Neurofilament, and Synaptic Genes in Fast-Spiking Vestibular Nucleus Neurons.

Computations that require speed and temporal precision are implemented throughout the nervous system by neurons capable of firing at very high rates, rapidly encoding and transmitting a rich amount of information, but with substantial metabolic and physical costs. For economical fast spiking and high throughput information processing, neurons need to optimize multiple biophysical properties in parallel, but the mechanisms of this coordination remain unknown. We hypothesized that coordinated gene expression may underlie the coordinated tuning of the biophysical properties required for rapid firing and signal transmission. Taking advantage of the diversity of fast-spiking cell types in the medial vestibular nucleus of mice of both sexes, we examined the relationship between gene expression, ionic currents, and neuronal firing capacity. Across excitatory and inhibitory cell types, genes encoding voltage-gated ion channels responsible for depolarizing and repolarizing the action potential were tightly coexpressed, and their absolute expression levels increased with maximal firing rate. Remarkably, this coordinated gene expression extended to neurofilaments and specific presynaptic molecules, providing a mechanism for coregulating axon caliber and transmitter release to match firing capacity. These findings suggest the presence of a module of genes, which is coexpressed in a graded manner and jointly tunes multiple biophysical properties for economical differentiation of firing capacity. The graded tuning of fast-spiking capacity by the absolute expression levels of specific ion channels provides a counterexample to the widely held assumption that cell-type-specific firing patterns can be achieved via a vast combination of different ion channels.SIGNIFICANCE STATEMENT Although essential roles of fast-spiking neurons in various neural circuits have been widely recognized, it remains unclear how neurons efficiently coordinate the multiple biophysical properties required to maintain high rates of action potential firing and transmitter release. Taking advantage of diverse fast-firing capacities among medial vestibular nucleus neurons of mice, we identify a group of ion channel, synaptic, and structural genes that exhibit mutually correlated expression levels, which covary with firing capacity. Coexpression of this fast-spiking gene module may be a basic strategy for neurons to efficiently and coordinately tune the speed of action potential generation and propagation and transmitter release at presynaptic terminals.

Fast spatiotemporal smoothing of calcium measurements in dendritic trees.

We discuss methods for fast spatiotemporal smoothing of calcium signals in dendritic trees, given single-trial, spatially localized imaging data obtained via multi-photon microscopy. By analyzing the dynamics of calcium binding to probe molecules and the effects of the imaging procedure, we show that calcium concentration can be estimated up to an affine transformation, i.e., an additive and multiplicative constant. To obtain a full spatiotemporal estimate, we model calcium dynamics within the cell using a functional approach. The evolution of calcium concentration is represented through a smaller set of hidden variables that incorporate fast transients due to backpropagating action potentials (bAPs), or other forms of stimulation. Because of the resulting state space structure, inference can be done in linear time using forward-backward maximum-a-posteriori methods. Non-negativity constraints on the calcium concentration can also be incorporated using a log-barrier method that does not affect the computational scaling. Moreover, by exploiting the neuronal tree structure we show that the cost of the algorithm is also linear in the size of the dendritic tree, making the approach applicable to arbitrarily large trees. We apply this algorithm to data obtained from hippocampal CA1 pyramidal cells with experimentally evoked bAPs, some of which were paired with excitatory postsynaptic potentials (EPSPs). The algorithm recovers the timing of the bAPs and provides an estimate of the induced calcium transient throughout the tree. The proposed methods could be used to further understand the interplay between bAPs and EPSPs in synaptic strength modification. More generally, this approach allows us to infer the concentration on intracellular calcium across the dendritic tree from noisy observations at a discrete set of points in space.

Three-dimensional random access multiphoton microscopy for functional imaging of neuronal activity.

The dynamic ability of neuronal dendrites to shape and integrate synaptic responses is the hallmark of information processing in the brain. Effectively studying this phenomenon requires concurrent measurements at multiple sites on live neurons. Substantial progress has been made by optical imaging systems that combine confocal and multiphoton microscopy with inertia-free laser scanning. However, all of the systems developed so far restrict fast imaging to two dimensions. This severely limits the extent to which neurons can be studied, as they represent complex three-dimensional structures. Here we present a new imaging system that utilizes a unique arrangement of acousto-optic deflectors to steer a focused, ultra-fast laser beam to arbitrary locations in three-dimensional space without moving the objective lens. As we demonstrate, this highly versatile random-access multiphoton microscope supports functional imaging of complex three-dimensional cellular structures such as neuronal dendrites or neural populations at acquisition rates on the order of tens of kilohertz.

Patterned networks of mouse hippocampal neurons on peptide-coated gold surfaces.

Patterned networks of hippocampal neurons were generated on peptide-coated gold substrates prepared by microscope projection photolithography and microcontact printing. A 19 amino acid peptide fragment of laminin A (PA22-2) that includes the IKVAV cell adhesion domain was used to direct patterns of cell adhesion in primary culture. Microscale grid patterns of peptide were deposited on gold-coated glass cover slips by soft lithography using "stamps" fashioned from polydimethylsiloxane. Strong coordination bonding between gold atoms on the surface and the sulfur atoms of the N-terminal cysteine residues supported stable adhesion of the peptide, which was confirmed by immunofluorescence using anti-IKVAV antiserum. Dispersed hippocampal cells isolated from neonatal mouse pups were grown on peptide-patterned gold substrates for 7 days. Neurons preferentially adhered to peptide-coated regions of the gold surface and restricted their processes to the peptide patterns. Whole cell recordings of neurons grown in patterned arrays revealed an average membrane potential of -50 mV, as well as the presence of voltage-gated ion conductances. Peptide-modified gold surfaces serve as convenient and effective substrates for growing ordered neural networks that are compatible with existing multi-electrode array recording technology.