Predictive Biophysical Neural Network Modeling of a Compendium of in vivo Transcription Factor DNA Binding Profiles for Escherichia coli.

The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global mapping of in vivo DNA binding for 139 E. coli TFs using ChIP-Seq. We used these data to train BoltzNet, a novel neural network that predicts TF binding energy from DNA sequence. BoltzNet mirrors a quantitative biophysical model and provides directly interpretable predictions genome-wide at nucleotide resolution. We used BoltzNet to quantitatively design novel binding sites, which we validated with biophysical experiments on purified protein. We have generated models for 125 TFs that provide insight into global features of TF binding, including clustering of sites, the role of accessory bases, the relevance of weak sites, and the background affinity of the genome. Our paper provides new paradigms for studying TF-DNA binding and for the development of biophysically motivated neural networks.

DPCD is a regulator of R2TP in ciliogenesis initiation through Akt signaling.

R2TP is a chaperone complex consisting of the AAA+ ATPases RUVBL1 and RUVBL2, as well as RPAP3 and PIH1D1 proteins. R2TP is responsible for the assembly of macromolecular complexes mainly acting through different adaptors. Using proximity-labeling mass spectrometry, we identified deleted in primary ciliary dyskinesia (DPCD) as an adaptor of R2TP. Here, we demonstrate that R2TP-DPCD influences ciliogenesis initiation through a unique mechanism by interaction with Akt kinase to regulate its phosphorylation levels rather than its stability. We further show that DPCD is a heart-shaped monomeric protein with two domains. A highly conserved region in the cysteine- and histidine-rich domains-containing proteins and SGT1 (CS) domain of DPCD interacts with the RUVBL2 DII domain with high affinity to form a stable R2TP-DPCD complex both in cellulo and in vitro. Considering that DPCD is one among several CS-domain-containing proteins found to associate with RUVBL1/2, we propose that RUVBL1/2 are CS-domain-binding proteins that regulate complex assembly and downstream signaling.

Wearable Devices: Implications for Precision Medicine and the Future of Health Care.

Wearable devices are integrated analytical units equipped with sensitive physical, chemical, and biological sensors capable of noninvasive and continuous monitoring of vital physiological parameters. Recent advances in disciplines including electronics, computation, and material science have resulted in affordable and highly sensitive wearable devices that are routinely used for tracking and managing health and well-being. Combined with longitudinal monitoring of physiological parameters, wearables are poised to transform the early detection, diagnosis, and treatment/management of a range of clinical conditions. Smartwatches are the most commonly used wearable devices and have already demonstrated valuable biomedical potential in detecting clinical conditions such as arrhythmias, Lyme disease, inflammation, and, more recently, COVID-19 infection. Despite significant clinical promise shown in research settings, there remain major hurdles in translating the medical uses of wearables to the clinic. There is a clear need for more effective collaboration among stakeholders, including users, data scientists, clinicians, payers, and governments, to improve device security, user privacy, data standardization, regulatory approval, and clinical validity. This review examines the potential of wearables to offer affordable and reliable measures of physiological status that are on par with FDA-approved specialized medical devices. We briefly examine studies where wearables proved critical for the early detection of acute and chronic clinical conditions with a particular focus on cardiovascular disease, viral infections, and mental health. Finally, we discuss current obstacles to the clinical implementation of wearables and provide perspectives on their potential to deliver increasingly personalized proactive health care across a wide variety of conditions.

Broad spectrum post-entry inhibitors of coronavirus replication: Cardiotonic steroids and monensin.

A small molecule screen identified several cardiotonic steroids (digitoxin and ouabain) and the ionophore monensin as potent inhibitors of HCoV-229E, HCoV-OC43, and SARS-CoV-2 replication with EC50s in the low nM range. Subsequent tests confirmed antiviral activity in primary cell models including human nasal epithelial cells and lung organoids. Addition of digitoxin, ouabain, or monensin strongly reduced viral gene expression as measured by both viral protein and RNA accumulation. Furthermore, the compounds acted post virus entry. While the antiviral activity of digitoxin was dependent upon activation of the MEK and JNK signaling pathways but not signaling through GPCRs, the antiviral effect of monensin was reversed upon inhibition of several signaling pathways. Together, the data demonstrates the potent anti-coronavirus properties of two classes of FDA approved drugs that function by altering the properties of the infected cell, rendering it unable to support virus replication.

Co-fractionation-mass spectrometry to characterize native mitochondrial protein assemblies in mammalian neurons and brain.

Human mitochondrial (mt) protein assemblies are vital for neuronal and brain function, and their alteration contributes to many human disorders, e.g., neurodegenerative diseases resulting from abnormal protein-protein interactions (PPIs). Knowledge of the composition of mt protein complexes is, however, still limited. Affinity purification mass spectrometry (MS) and proximity-dependent biotinylation MS have defined protein partners of some mt proteins, but are too technically challenging and laborious to be practical for analyzing large numbers of samples at the proteome level, e.g., for the study of neuronal or brain-specific mt assemblies, as well as altered mtPPIs on a proteome-wide scale for a disease of interest in brain regions, disease tissues or neurons derived from patients. To address this challenge, we adapted a co-fractionation-MS platform to survey native mt assemblies in adult mouse brain and in human NTERA-2 embryonal carcinoma stem cells or differentiated neuronal-like cells. The workflow consists of orthogonal separations of mt extracts isolated from chemically cross-linked samples to stabilize PPIs, data-dependent acquisition MS to identify co-eluted mt protein profiles from collected fractions and a computational scoring pipeline to predict mtPPIs, followed by network partitioning to define complexes linked to mt functions as well as those essential for neuronal and brain physiological homeostasis. We developed an R/CRAN software package, Macromolecular Assemblies from Co-elution Profiles for automated scoring of co-fractionation-MS data to define complexes from mtPPI networks. Presently, the co-fractionation-MS procedure takes 1.5-3.5 d of proteomic sample preparation, 31 d of MS data acquisition and 8.5 d of data analyses to produce meaningful biological insights.

On a path toward a broad-spectrum anti-viral: inhibition of HIV-1 and coronavirus replication by SR kinase inhibitor harmine.

IMPORTANCE: This study highlights the crucial role RNA processing plays in regulating viral gene expression and replication. By targeting SR kinases, we identified harmine as a potent inhibitor of HIV-1 as well as coronavirus (HCoV-229E and multiple SARS-CoV-2 variants) replication. Harmine inhibits HIV-1 protein expression and reduces accumulation of HIV-1 RNAs in both cell lines and primary CD4+ T cells. Harmine also suppresses coronavirus replication post-viral entry by preferentially reducing coronavirus sub-genomic RNA accumulation. By focusing on host factors rather than viral targets, our study offers a novel approach to combating viral infections that is effective against a range of unrelated viruses. Moreover, at doses required to inhibit virus replication, harmine had limited toxicity and minimal effect on the host transcriptome. These findings support the viability of targeting host cellular processes as a means of developing broad-spectrum anti-virals.

HDL functionality is dependent on hepatocyte stress defense factors Nrf1 and Nrf2.

High density lipoproteins (HDL) promote homeostasis and counteract stressful tissue damage that underlie cardiovascular and other diseases by mediating reverse cholesterol transport, reducing inflammation, and abrogating oxidative damage. However, metabolically stressful conditions associated with atherosclerosis can impair these effects. Hepatocytes play a major role in the genesis and maturation of circulating HDL, and liver stress elicits marked regulatory changes to circulating HDL abundance and composition, which affect its functionality. The mechanisms linking liver stress to HDL function are incompletely understood. In this study, we sought to determine whether stress defending transcription factors nuclear factor erythroid 2 related factor-1 (Nrf1) and -2 (Nrf2) promote hepatocyte production of functional HDL. Using genetically engineered mice briefly fed a mild metabolically stressful diet, we investigated the effect of hepatocyte-specific deletion of Nrf1, Nrf2, or both on circulating HDL cholesterol, protein composition, and function. Combined deletion, but not single gene deletion, reduced HDL cholesterol and apolipoprotein A1 levels as well as the capacity of HDL to accept cholesterol undergoing efflux from cultured macrophages and to counteract tumor necrosis factor α-induced inflammatory effect on cultured endothelial cells. This coincided with substantial alteration to the HDL proteome, which correlated with liver gene expression profiles of corresponding proteins. Thus, our findings show complementary actions by hepatocyte Nrf1 and Nrf2 play a role in shaping HDL abundance and composition to promote production of functionally viable HDL. Consequently, our study illuminates the possibility that enhancing stress defense programming in the liver may improve atheroprotective and perhaps other health promoting actions of HDL.

Identification and characterization of protein interactions with the major Niemann-Pick type C disease protein in yeast reveals pathways of therapeutic potential.

Niemann-Pick type C (NP-C) disease is a rare lysosomal storage disease caused by mutations in NPC1 (95% cases) or NPC2 (5% cases). These proteins function together in cholesterol egress from the lysosome, whereby upon mutation, cholesterol and other lipids accumulate causing major pathologies. However, it is not fully understood how cholesterol is transported from NPC1 residing at the lysosomal membrane to the endoplasmic reticulum (ER) and plasma membrane. The yeast ortholog of NPC1, Niemann-Pick type C-related protein-1 (Ncr1), functions similarly to NPC1; when transfected into a mammalian cell lacking NPC1, Ncr1 rescues the diagnostic hallmarks of cholesterol and sphingolipid accumulation. Here, we aimed to identify and characterize protein-protein interactions (PPIs) with the yeast Ncr1 protein. A genome-wide split-ubiquitin membrane yeast two-hybrid (MYTH) protein interaction screen identified 11 ER membrane-localized, full-length proteins interacting with Ncr1 at the lysosomal/vacuolar membrane. These highlight the importance of ER-vacuole membrane interface and include PPIs with the Cyb5/Cbr1 electron transfer system, the ceramide synthase complex, and the Sec61/Sbh1 protein translocation complex. These PPIs were not detected in a sterol auxotrophy condition and thus depend on normal sterol metabolism. To provide biological context for the Ncr1-Cyb5 PPI, a yeast strain lacking this PPI (via gene deletions) exhibited altered levels of sterols and sphingolipids including increased levels of glucosylceramide that mimic NP-C disease. Overall, the results herein provide new physical and genetic interaction models to further use the yeast model of NP-C disease to better understand human NP-C disease.

Sanitation enzymes: Exquisite surveillance of the noncanonical nucleotide pool to safeguard the genetic blueprint.

Reactive oxygen species (ROS) are common products of normal cellular metabolism, but their elevated levels can result in nucleotide modifications. These modified or noncanonical nucleotides often integrate into nascent DNA during replication, causing lesions that trigger DNA repair mechanisms such as the mismatch repair machinery and base excision repair. Four superfamilies of sanitization enzymes can effectively hydrolyze noncanonical nucleotides from the precursor pool and eliminate their unintended incorporation into DNA. Notably, we focus on the representative MTH1 NUDIX hydrolase, whose enzymatic activity is ostensibly nonessential under normal physiological conditions. Yet, the sanitization attributes of MTH1 are more prevalent when ROS levels are abnormally high in cancer cells, rendering MTH1 an interesting target for developing anticancer treatments. We discuss multiple MTH1 inhibitory strategies that have emerged in recent years, and the potential of NUDIX hydrolases as plausible targets for the development of anticancer therapeutics.

E. coli allantoinase is activated by the downstream metabolic enzyme, glycerate kinase, and stabilizes the putative allantoin transporter by direct binding.

Allantoin is a good source of ammonium for many organisms, and in Escherichia coli it is utilized under anaerobic conditions. We provide evidence that allantoinase (AllB) is allosterically activated by direct binding of the allantoin catabolic enzyme, glycerate 2-kinase (GlxK) in the presence of glyoxylate. Glyoxylate is known to be an effector of the AllR repressor which regulates the allantoin utilization operons in E. coli. AllB has low affinity for allantoin, but its activation by GlxK leads to increased affinity for its substrate. We also show that the predicted allantoin transporter YbbW (re-named AllW) has allantoin specificity and the protein-protein interaction with AllB. Our results show that the AllB-dependent allantoin degradative pathway is subject to previously unrecognized regulatory mechanisms involving direct protein-protein interactions.

A Correlation between 3'-UTR of OXA1 Gene and Yeast Mitochondrial Translation.

Mitochondria possess their own DNA (mtDNA) and are capable of carrying out their transcription and translation. Although protein synthesis can take place in mitochondria, the majority of the proteins in mitochondria have nuclear origin. 3' and 5' untranslated regions of mRNAs (3'-UTR and 5'-UTR, respectively) are thought to play key roles in directing and regulating the activity of mitochondria mRNAs. Here we investigate the association between the presence of 3'-UTR from OXA1 gene on a prokaryotic reporter mRNA and mitochondrial translation in yeast. OXA1 is a nuclear gene that codes for mitochondrial inner membrane insertion protein and its 3'-UTR is shown to direct its mRNA toward mitochondria. It is not clear, however, if this mRNA may also be translated by mitochondria. In the current study, using a ß-galactosidase reporter gene, we provide genetic evidence for a correlation between the presence of 3'-UTR of OXA1 on an mRNA and mitochondrial translation in yeast.

Multi-Omics Profiling for Health.

The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.

Complementary gene regulation by NRF1 and NRF2 protects against hepatic cholesterol overload.

Hepatic cholesterol overload promotes steatohepatitis. Insufficient understanding of liver stress defense impedes therapy development. Here, we elucidate the role of stress defense transcription factors, nuclear factor erythroid 2 related factor-1 (NRF1) and -2 (NRF2), in counteracting cholesterol-linked liver stress. Using a diet that increases liver cholesterol storage, expression profiles and phenotypes of liver from mice with hepatocyte deficiency of NRF1, NRF2, or both are compared with controls, and chromatin immunoprecipitation sequencing is undertaken to identify target genes. Results show NRF1 and NRF2 co-regulate genes that eliminate cholesterol and mitigate inflammation and oxidative damage. Combined deficiency, but not deficiency of either alone, results in severe steatohepatitis, hepatic cholesterol overload and crystallization, altered bile acid metabolism, and decreased biliary cholesterol. Moreover, therapeutic effects of NRF2-activating drug bardoxolone require NRF1 and are supplemented by NRF1 overexpression. Thus, we discover complementary gene programming by NRF1 and NRF2 that counteract cholesterol-associated fatty liver disease progression.

ApoA-I Nanotherapy Rescues Postischemic Vascular Maladaptation by Modulating Endothelial Cell and Macrophage Phenotypes in Type 2 Diabetic Mice.

BACKGROUND: Diabetes is a major risk factor for peripheral arterial disease. Clinical and preclinical studies suggest an impaired collateral remodeling and angiogenesis in response to atherosclerotic arterial occlusion in diabetic conditions, although the underlying mechanisms are poorly understood. OBJECTIVE: To clarify the cellular and molecular mechanisms underlying impaired postischemic adaptive vascular responses and to evaluate rHDL (reconstituted HDL)-ApoA-I nanotherapy to rescue the defect in type 2 diabetic mouse model of hindlimb ischemia. METHODS AND RESULTS: Hindlimb ischemia was induced by unilateral femoral artery ligation. Collateral and capillary parameters together with blood flow recovery were analyzed from normoxic adductor and ischemic gastrocnemius muscles, respectively, at day 3 and 7 post-ligation. In response to femoral artery ligation, collateral lumen area was significantly reduced in normoxic adductor muscles. Distally, ischemic gastrocnemius muscles displayed impaired perfusion recovery and angiogenesis paralleled with persistent inflammation. Muscle-specific mRNA sequencing revealed differential expression of genes critical for smooth muscle proliferation and sprouting angiogenesis in normoxic adductor and ischemic gastrocnemius, respectively, at day 7 post-ligation. Genes typical for macrophage (Mϕ) subsets were differentially expressed across both muscle types. Cell-specific gene expression, flow cytometry, and immunohistochemistry revealed persistent IFN-I response gene upregulation in arterial endothelial cells, ECs and Mϕs from T2DM mice associated with impaired collateral remodeling, angiogenesis and perfusion recovery. Furthermore, rHDL nanotherapy rescued impaired collateral remodeling and angiogenesis through dampening EC and Mϕ inflammation in T2DM mice. CONCLUSIONS: Our results suggest that an impaired collateral remodeling and sprouting angiogenesis in T2DM mice is associated with persistent IFN-I response in ECs and Mϕs. Dampening persistent inflammation and skewing ECs and Mϕ phenotype toward less inflammatory ones using rHDL nanotherapy may serve as a potential therapeutic target for T2DM peripheral arterial disease.

Insights into SACS pathological attributes in autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS)☆.

Autosomal recessive spastic ataxia of Charlevoix-Saguenay (ARSACS) is a rare early-onset neurodegenerative disease caused by mutations in the SACS gene, encoding Sacsin. Initial functional annotation of Sacsin was based on sequence homology, with subsequent experiments revealing the Sacsin requirement for regulating mitochondrial dynamics, along with its domains involved in promoting neurofilament assembly or resolving their bundling accumulations. ARSACS phenotypes associated with SACS loss-of-function are discussed, and how advancements in ARSACS disease models and quantitative omics approaches can improve our understanding of ARSACS pathological attributes. Lastly in the perspectives section, we address gene correction strategies for monogenic disorders such as ARSACS, along with their common delivery methods, representing a hopeful area for ARSACS therapeutics development.

A computational approach to rapidly design peptides that detect SARS-CoV-2 surface protein S.

The coronavirus disease 19 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) prompted the development of diagnostic and therapeutic frameworks for timely containment of this pandemic. Here, we utilized our non-conventional computational algorithm, InSiPS, to rapidly design and experimentally validate peptides that bind to SARS-CoV-2 spike (S) surface protein. We previously showed that this method can be used to develop peptides against yeast proteins, however, the applicability of this method to design peptides against other proteins has not been investigated. In the current study, we demonstrate that two sets of peptides developed using InSiPS method can detect purified SARS-CoV-2 S protein via ELISA and Surface Plasmon Resonance (SPR) approaches, suggesting the utility of our strategy in real time COVID-19 diagnostics. Mass spectrometry-based salivary peptidomics shortlist top SARS-CoV-2 peptides detected in COVID-19 patients' saliva, rendering them attractive SARS-CoV-2 diagnostic targets that, when subjected to our computational platform, can streamline the development of potent peptide diagnostics of SARS-CoV-2 variants of concern. Our approach can be rapidly implicated in diagnosing other communicable diseases of immediate threat.

In vitro toxicity screening of amorphous silica nanoparticles using mitochondrial fraction exposure followed by MS-based proteomic analysis.

Silica nanoparticles (SiNPs) are used in consumer products, engineering and medical technologies. Attractive properties of SiNPs (e.g. size/surface-modification) enhance usage and thus the likelihood of environmental/human exposures. The assessment of health risks associated with exposures to SiNPs requires information on their relative potencies and toxicity mechanisms. In this work, phagocytic J774 cells were exposed to amorphous pristine (15, 30, 75 nm) and surface-modified (-NH2, -C3COOH, -C11COOH, -PEG) SiNP variants, and internalization was assessed by transmission electron microscopy (TEM), while cellular ATP was measured as a cytotoxicity endpoint. Furthermore, mitochondrial fractions from J774 cells were exposed to these SiNP variants (5, 15 µg mL-1), as well as two reference particles (SiNP 12 nm and TiO2), and proteomic changes were analyzed by mass spectrometry. Ingenuity Pathway Analysis was used to identify toxicity pathways. TEM analyses showed SiNP internalization and distribution along with some changes in mitochondrial structure. SiNP size- and surface-modification and chemical composition-related changes in mitochondrial proteins, including key proteins of the respiratory complex and oxidative stress, were evident based on high content mass spectrometry data. In addition, the dose-related decrease in cellular ATP levels in SiNP-exposed cells was consistent with related mitochondrial protein profiles. These findings suggest that physicochemical properties can be determinants of SiNP exposure-related mitochondrial effects, and mitochondrial exposures combined with proteomic analysis can be valuable as a new approach methodology in the toxicity screening of SiNPs for risk assessment, with added insight into related toxicity mechanisms.

Auxotrophic and prototrophic conditional genetic networks reveal the rewiring of transcription factors in Escherichia coli.

Bacterial transcription factors (TFs) are widely studied in Escherichia coli. Yet it remains unclear how individual genes in the underlying pathways of TF machinery operate together during environmental challenge. Here, we address this by applying an unbiased, quantitative synthetic genetic interaction (GI) approach to measure pairwise GIs among all TF genes in E. coli under auxotrophic (rich medium) and prototrophic (minimal medium) static growth conditions. The resulting static and differential GI networks reveal condition-dependent GIs, widespread changes among TF genes in metabolism, and new roles for uncharacterized TFs (yjdC, yneJ, ydiP) as regulators of cell division, putrescine utilization pathway, and cold shock adaptation. Pan-bacterial conservation suggests TF genes with GIs are co-conserved in evolution. Together, our results illuminate the global organization of E. coli TFs, and remodeling of genetic backup systems for TFs under environmental change, which is essential for controlling the bacterial transcriptional regulatory circuits.

HPiP: an R/Bioconductor package for predicting host-pathogen protein-protein interactions from protein sequences using ensemble machine learning approach.

Motivation: Despite arduous and time-consuming experimental efforts, protein-protein interactions (PPIs) for many pathogenic microbes with their human host are still unknown, limiting our understanding of the intricate interactions during infection and the identification of therapeutic targets. Since computational tools offer a promising alternative, we developed an R/Bioconductor package, HPiP (Host-Pathogen Interaction Prediction) software with a series of amino acid sequence property descriptors and an ensemble machine learning classifiers to predict the yet unmapped interactions between pathogen and host proteins. Results: Using severe acute respiratory syndrome coronavirus 1 (SARS-CoV-1) or the novel SARS-CoV-2 coronavirus-human PPI training sets as a case study, we show that HPiP achieves a good performance with PPI predictions between SARS-CoV-2 and human proteins, which we confirmed experimentally in human monocyte THP-1 cells, and with several quality control metrics. HPiP also exhibited strong performance in accurately predicting the previously reported PPIs when tested against the sequences of pathogenic bacteria, Mycobacterium tuberculosis and human proteins. Collectively, our fully documented HPiP software will hasten the exploration of PPIs for a systems-level understanding of many understudied pathogens and uncover molecular targets for repurposing existing drugs. Availability and implementation: HPiP is released as an open-source code under the MIT license that is freely available on GitHub (https://github.com/BabuLab-UofR/HPiP) as well as on Bioconductor (http://bioconductor.org/packages/devel/bioc/html/HPiP.html). Supplementary information: Supplementary data are available at Bioinformatics Advances online.