Hepatocyte growth factor promotes retinal pigment epithelium cell activity through MET/AKT signaling pathway.

AIM: To explore the effects of hepatocyte growth factor (HGF) on retinal pigment epithelium (RPE) cell behaviors. METHODS: The human adult retinal pigment epithelial cell line-19 (ARPE-19) were treated by HGF or mesenchymal-epithelial transition factor (MET) inhibitor SU11274 in vitro. Cell viability was detected by a Cell Counting Kit-8 assay. Cell proliferation and motility was detected by a bromodeoxyuridine incorporation assay and a wound healing assay, respectively. The expression levels of MET, phosphorylated MET, protein kinase B (AKT), and phosphorylated AKT proteins were determined by Western blot assay. The MET and phosphorylated MET proteins were also determined by immunofluorescence assay. RESULTS: HGF increased ARPE-19 cells' viability, proliferation and migration, and induced an increase of phosphorylated MET and phosphorylated AKT proteins. SU11274 significantly reduced cell viability, proliferation, and migration and decreased the expression of MET and AKT proteins. SU11274 suppressed HGF-induced increase of viability, proliferation, and migration in ARPE-19 cells. Additionally, SU11274 also blocked HGF-induced phosphorylation of MET and AKT proteins. CONCLUSION: HGF enhances cellular viability, proliferation, and migration in RPE cells through the MET/AKT signaling pathway, whereas this enhancement is suppressed by the MET inhibitor SU11274. HGF-induced MET/AKT signaling might be a vital contributor of RPE cells survival.

Circulating metabolite abundances associated with risks of bipolar disorder, schizophrenia, and depression: a Mendelian randomization study.

BACKGROUND: Preventive measures and treatments for psychiatric disorders are limited. Circulating metabolites are potential candidates for biomarker and therapeutic target identification, given their measurability and essential roles in biological processes. METHODS: Leveraging large-scale genome-wide association studies, we conducted Mendelian randomization (MR) analyses to assess the associations between circulating metabolite abundances and the risks of bipolar disorder, schizophrenia, and depression. Genetic instruments were selected for 94 metabolites measured in the Canadian Longitudinal Study of Aging (N=8,299) cohort. We repeated MR analyses based on the UK Biobank, INTERVAL, and EPIC-Norfolk studies. RESULTS: After validating MR assumptions and colocalization evidence, we found that a one standard deviation (SD) increase in genetically predicted circulating abundances of eicosapentaenoate (EPA) and docosapentaenoate (n3 DPA) was associated with odds ratios (ORs) of 0.72 (95% CI: 0.65-0.79) and 0.63 (95% CI: 0.55-0.72) for bipolar disorder, respectively. Genetically increased Ω-3 unsaturated fatty acids abundance and Ω-3-to-total fatty acids ratio, as well as genetically decreased Ω-6-to-Ω-3 ratio were negatively associated with the risk of bipolar disorder in the UK Biobank. Genetically increased circulating abundances of three N-acetyl-amino acids were associated with an increased risk of schizophrenia with a maximum OR of 1.31 (95% CI: 1.18-1.44) per one SD increase. Furthermore, a one SD increase in genetically predicted circulating abundance of hypotaurine was associated with an OR of 0.85 (95% CI: 0.78-0.93) for depression. CONCLUSIONS: The biological mechanisms underlying Ω-3 unsaturated fatty acids, NAT8-catalyzed N-acetyl-amino acids, and hypotaurine warrant exploration to identify new biomarkers and potential therapeutic targets.

Sustained IFN signaling is associated with delayed development of SARS-CoV-2-specific immunity.

Plasma RNAemia, delayed antibody responses and inflammation predict COVID-19 outcomes, but the mechanisms underlying these immunovirological patterns are poorly understood. We profile 782 longitudinal plasma samples from 318 hospitalized patients with COVID-19. Integrated analysis using k-means reveals four patient clusters in a discovery cohort: mechanically ventilated critically-ill cases are subdivided into good prognosis and high-fatality clusters (reproduced in a validation cohort), while non-critical survivors segregate into high and low early antibody responders. Only the high-fatality cluster is enriched for transcriptomic signatures associated with COVID-19 severity, and each cluster has distinct RBD-specific antibody elicitation kinetics. Both critical and non-critical clusters with delayed antibody responses exhibit sustained IFN signatures, which negatively correlate with contemporaneous RBD-specific IgG levels and absolute SARS-CoV-2-specific B and CD4+ T cell frequencies. These data suggest that the "Interferon paradox" previously described in murine LCMV models is operative in COVID-19, with excessive IFN signaling delaying development of adaptive virus-specific immunity.

Morinda Officinalis-derived extracellular vesicle-like particles: Anti-osteoporosis effect by regulating MAPK signaling pathway.

BACKGROUND: Postmenopausal osteoporosis (PMOP) is a systemic bone disease characterized by low bone mass and microstructural damage. Morinda Officinalis (MO) contains various components with anti-PMOP activities. Morinda Officinalis-derived extracellular vesicle-like particles (MOEVLPs) are new active components isolated from MO, and no relevant studies have investigated their anti-osteoporosis effect and mechanism. PURPOSE: To investigate the alleviating effect of MOEVLPs on PMOP and the underlying mechanism. METHODS: Differential centrifugation and ultracentrifugation were used to isolate MOEVLPs from MO. Transmission electron microscopy (TEM), flow nano analyzer, sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), agarose gel electrophoresis, and thin-layer chromatography were employed to characterize MOEVLPs. PMOP mouse models were utilized to examine the anti-PMOP effect of MOEVLPs. H&E and immunohistochemical staining were used for drug safety and osteogenic effect assessment. Mouse embryo osteoblast precursor cells (MC3T3-E1) were used in vitro experiments. CCK-8 kit, alizarin red staining, proteomic, bioinformatic analyses, and western blot were used to explore the mechanism of MOEVLPs. RESULTS: In this study, MOEVLPs from MO were successfully isolated and characterized. Animal experiments demonstrated that MOEVLPs exhibited specific femur targeting, were non-toxic to the heart, liver, spleen, lung, kidney, and aorta, and possessed anti-PMOP properties. The ability of MOEVLPs to strengthen bone formation was better than that of alendronate. In vitro experiments, results revealed that MOEVLPs did not significantly enhance osteogenic differentiation in MC3T3-E1 cells. Instead, MOEVLPs promoted the proliferation of MC3T3-E1 cells. Proteomic and bioinformatic analyses suggested that the proliferative effect of MOEVLPs was closely associated with the mitogen-activated protein kinase (MAPK) signaling pathway, particularly the altered expression of cAMP response element-binding protein (CREB) and ribosomal S6 kinase 1 (RSK1). Western blot results further confirmed these findings. CONCLUSION: Our studies successfully isolated high-quality MOEVLPs and demonstrated that MOEVLPs can alleviate PMOP by promoting osteoblast proliferation through the MAPK pathway. MOEVLPs have the potential to become a novel and natural anti-PMOP drug.

Deciphering the heterogeneity dominated by tumor-associated macrophages for survival prognostication and prediction of immunotherapy response in lung adenocarcinoma.

Tumor-associated macrophages (TAMs) are a specific subset of macrophages that reside inside the tumor microenvironment. The dynamic interplay between TAMs and tumor cells plays a crucial role in the treatment response and prognosis of lung adenocarcinoma (LUAD). The study aimed to examine the association between TAMs and LUAD to advance the development of targeted strategies and immunotherapeutic approaches for treating this type of lung cancer. The study employed single-cell mRNA sequencing data to characterize the immune cell composition of LUAD and delineate distinct subpopulations of TAMs. The "BayesPrism" and "Seurat" R packages were employed to examine the association between these subgroups and immunotherapy and clinical features to identify novel immunotherapy biomarkers. Furthermore, a predictive signature was generated to forecast patient prognosis by examining the gene expression profile of immunotherapy-associated TAMs subsets and using 104 machine-learning techniques. A comprehensive investigation has shown the existence of a hitherto unidentified subgroup of TAMs known as RGS1 + TAMs, which has been found to have a strong correlation with the efficacy of immunotherapy and the occurrence of tumor metastasis in LUAD patients. CD83 was identified CD83 as a distinct biomarker for the expression of RGS1 + TAMs, showcasing its potential utility as an indicator for immunotherapeutic interventions. Furthermore, the prognostic capacity of the RTMscore signature, encompassing three specific mRNA (NR4A2, MMP14, and NPC2), demonstrated enhanced robustness when contrasted against the comprehensive collection of 104 features outlined in the published study. CD83 has potential as an immunotherapeutic biomarker. Meanwhile, The RTMscore signature established in the present study might be beneficial for survival prognostication.

Identifying therapeutic targets for cancer among 2074 circulating proteins and risk of nine cancers.

Circulating proteins can reveal key pathways to cancer and identify therapeutic targets for cancer prevention. We investigate 2,074 circulating proteins and risk of nine common cancers (bladder, breast, endometrium, head and neck, lung, ovary, pancreas, kidney, and malignant non-melanoma) using cis protein Mendelian randomisation and colocalization. We conduct additional analyses to identify adverse side-effects of altering risk proteins and map cancer risk proteins to drug targets. Here we find 40 proteins associated with common cancers, such as PLAUR and risk of breast cancer [odds ratio per standard deviation increment: 2.27, 1.88-2.74], and with high-mortality cancers, such as CTRB1 and pancreatic cancer [0.79, 0.73-0.85]. We also identify potential adverse effects of protein-altering interventions to reduce cancer risk, such as hypertension. Additionally, we report 18 proteins associated with cancer risk that map to existing drugs and 15 that are not currently under clinical investigation. In sum, we identify protein-cancer links that improve our understanding of cancer aetiology. We also demonstrate that the wider consequence of any protein-altering intervention on well-being and morbidity is required to interpret any utility of proteins as potential future targets for therapeutic prevention.

Integrative multi-omics analysis identifies genetically supported druggable targets and immune cell specificity for myasthenia gravis.

BACKGROUND: Myasthenia gravis (MG) is a chronic autoimmune disorder characterized by fluctuating muscle weakness. Despite the availability of established therapies, the management of MG symptoms remains suboptimal, partially attributed to lack of efficacy or intolerable side-effects. Therefore, new effective drugs are warranted for treatment of MG. METHODS: By employing an analytical framework that combines Mendelian randomization (MR) and colocalization analysis, we estimate the causal effects of blood druggable expression quantitative trait loci (eQTLs) and protein quantitative trait loci (pQTLs) on the susceptibility of MG. We subsequently investigated whether potential genetic effects exhibit cell-type specificity by utilizing genetic colocalization analysis to assess the interplay between immune-cell-specific eQTLs and MG risk. RESULTS: We identified significant MR results for four genes (CDC42BPB, CD226, PRSS36, and TNFSF12) using cis-eQTL genetic instruments and three proteins (CTSH, PRSS8, and CPN2) using cis-pQTL genetic instruments. Six of these loci demonstrated evidence of colocalization with MG susceptibility (posterior probability > 0.80). We next undertook genetic colocalization to investigate cell-type-specific effects at these loci. Notably, we identified robust evidence of colocalization, with a posterior probability of 0.854, linking CTSH expression in TH2 cells and MG risk. CONCLUSIONS: This study provides crucial insights into the genetic and molecular factors associated with MG susceptibility, singling out CTSH as a potential candidate for in-depth investigation and clinical consideration. It additionally sheds light on the immune-cell regulatory mechanisms related to the disease. However, further research is imperative to validate these targets and evaluate their feasibility for drug development.

Differentiation of Native Vertebral Osteomyelitis: A Comprehensive Review of Imaging Techniques and Future Applications.

Native vertebral osteomyelitis, also termed spondylodiscitis, is an antibiotic-resistant disease that requires long-term treatment. Without proper treatment, NVO can lead to severe nerve damage or even death. Therefore, it is important to accurately diagnose the cause of NVO, especially in spontaneous cases. Infectious NVO is characterized by the involvement of 2 adjacent vertebrae and intervertebral discs, and common infectious agents include Staphylococcus aureus, Mycobacterium tuberculosis, Brucella abortus, and fungi. Clinical symptoms are generally nonspecific, and early diagnosis and appropriate treatment can prevent irreversible sequelae. Advances in pathologic histologic imaging have led physicians to look more forward to being able to differentiate between tuberculous and septic spinal discitis. Therefore, research in identifying and differentiating the imaging features of these 4 common NVOs is essential. Due to the diagnostic difficulties, clinical and radiologic diagnosis is the mainstay of provisional diagnosis. With the advent of the big data era and the emergence of convolutional neural network algorithms for deep learning, the application of artificial intelligence (AI) technology in orthopedic imaging diagnosis has gradually increased. AI can assist physicians in imaging review, effectively reduce the workload of physicians, and improve diagnostic accuracy. Therefore, it is necessary to present the latest clinical research on NVO and the outlook for future AI applications.

Protective role of madecassoside from Centella asiatica against protein L-isoaspartyl methyltransferase deficiency-induced neurodegeneration.

Protein L-isoaspartyl methyltransferase (PIMT/PCMT1) could repair l-isoaspartate (L-isoAsp) residues formed by deamidation of asparaginyl (Asn) residues or isomerization of aspartyl (Asp) residues in peptides and proteins during aging. Aside from abnormal accumulation of L-isoAsp, PIMT knockout (KO) mice mirrors some neuropathological hallmarks such as anxiety-like behaviors, impaired spatial memory and aberrant synaptic plasticity in the hippocampus of neurodegenerative diseases (NDs), including Alzheimer's disease (AD) and related dementias, and Parkinson's disease (PD). While some reports indicate the neuroprotective effect of madecassoside (MA) as a triterpenoid saponin component of Centella asiatica, its role against NDs-related anxiety and cognitive impairment remains unclear. Therefore, we investigated the effect of MA against anxiety-related behaviors in PIMT deficiency-induced mouse model of NDs. Results obtained from the elevated plus maze (EPM) test revealed that MA treatment alleviated anxiety-like behaviors in PIMT knockout mice. Furthermore, Real-time PCR, electroencephalogram (EEG) recordings, transmission electron microscopy analysis and ELISA were carried out to evaluate the expression of clock genes, sleep and synaptic function, respectively. The PIMT knockout mice were characterized by abnormal clock patterns, sleep disturbance and synaptic dysfunction, which could be improved by MA administration. Collectively, these findings suggest that MA exhibits neuroprotective effects associated with improved circadian rhythms sleep-wake cycle and synaptic plasticity in PIMT deficient mice, which could be translated to ameliorate anxiety-related symptoms and cognitive impairments in NDs.

HLA allele-calling using multi-ancestry whole-exome sequencing from the UK Biobank identifies 129 novel associations in 11 autoimmune diseases.

The human leukocyte antigen (HLA) region on chromosome 6 is strongly associated with many immune-mediated and infection-related diseases. Due to its highly polymorphic nature and complex linkage disequilibrium patterns, traditional genetic association studies of single nucleotide polymorphisms do not perform well in this region. Instead, the field has adopted the assessment of the association of HLA alleles (i.e., entire HLA gene haplotypes) with disease. Often based on genotyping arrays, these association studies impute HLA alleles, decreasing accuracy and thus statistical power for rare alleles and in non-European ancestries. Here, we use whole-exome sequencing (WES) from 454,824 UK Biobank (UKB) participants to directly call HLA alleles using the HLA-HD algorithm. We show this method is more accurate than imputing HLA alleles and harness the improved statistical power to identify 360 associations for 11 auto-immune phenotypes (at least 129 likely novel), leading to better insights into the specific coding polymorphisms that underlie these diseases. We show that HLA alleles with synonymous variants, often overlooked in HLA studies, can significantly influence these phenotypes. Lastly, we show that HLA sequencing may improve polygenic risk scores accuracy across ancestries. These findings allow better characterization of the role of the HLA region in human disease.

Advances in the Study of Plant-Derived Vesicle-Like Nanoparticles in Inflammatory Diseases.

All humans are universally affected by inflammatory diseases, and there is an urgent need to identify new anti-inflammatory drugs with good therapeutic benefits and minimal side effects to the organism. Recently, it has been found that plant-derived vesicle-like nanoparticles (PDVLNs) have good biocompatibility, with their active ingredients exhibiting good therapeutic effects on inflammation. They can also be used as drug carriers for targeted delivery of anti-inflammatory drugs. Therefore, PDVLNs represent a popular research area for novel anti-inflammatory drugs. This paper details the origin, biological functions, isolation and purification, and identification of PDVLNs, as well as the therapeutic effects of their intrinsic bioactive components on inflammatory diseases. It also introduces their targets as drug carriers to facilitate the development and application of PDVLNs anti-inflammatory drugs.

Identifying Rare Genetic Determinants for Improved Polygenic Risk Prediction of Bone Mineral Density and Fracture Risk.

Osteoporosis and fractures severely impact the elderly population. Polygenic risk scores for bone mineral density have demonstrated potential clinical utility. However, the value of rare genetic determinants in risk prediction has not been assessed. With whole-exome sequencing data from 436,824 UK Biobank participants, we assigned White British ancestry individuals into a training data set (n = 317,434) and a test data set (n = 74,825). In the training data set, we developed a common variant-based polygenic risk score for heel ultrasound speed of sound (SOS). Next, we performed burden testing to identify genes harboring rare determinants of bone mineral density, targeting influential rare variants with predicted high deleteriousness. We constructed a genetic risk score, called ggSOS, to incorporate influential rare variants in significant gene burden masks into the common variant-based polygenic risk score. We assessed the predictive performance of ggSOS in the White British test data set, as well as in populations of non-White British European (n = 18,885), African (n = 7165), East Asian (n = 2236), South Asian (n = 9829), and other admixed (n = 1481) ancestries. Twelve genes in pivotal regulatory pathways of bone homeostasis harbored influential rare variants associated with SOS (p < 5.5 × 10-7 ), including AHNAK, BMP5, CYP19A1, FAM20A, FBXW5, KDM5B, KREMEN1, LGR4, LRP5, SMAD6, SOST, and WNT1. Among 4013 (5.4%) individuals in the test data set carrying these variants, a one standard deviation decrease in ggSOS was associated with 1.35-fold (95% confidence interval [CI] 1.16-1.57) increased hazard of major osteoporotic fracture. However, compared with a common variant-based polygenic risk score (C-index = 0.641), ggSOS had only marginally improved prediction accuracy in identifying at-risk individuals (C-index = 0.644), with overlapping confidence intervals. Similarly, ggSOS did not demonstrate substantially improved predictive performance in non-European ancestry populations. In summary, modeling the effects of rare genetic determinants may assist polygenic prediction of fracture risk among carriers of influential rare variants. Nonetheless, improved clinical utility is not guaranteed for population-level risk screening. © 2023 The Authors. Journal of Bone and Mineral Research published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research (ASBMR).

Alternative splicing in lung influences COVID-19 severity and respiratory diseases.

Alternative splicing generates functional diversity in isoforms, impacting immune response to infection. Here, we evaluate the causal role of alternative splicing in COVID-19 severity and susceptibility by applying two-sample Mendelian randomization to cis-splicing quantitative trait loci and the results from COVID-19 Host Genetics Initiative. We identify that alternative splicing in lung, rather than total expression of OAS1, ATP11A, DPP9 and NPNT, is associated with COVID-19 severity. MUC1 and PMF1 splicing is associated with COVID-19 susceptibility. Colocalization analyses support a shared genetic mechanism between COVID-19 severity with idiopathic pulmonary fibrosis at the ATP11A and DPP9 loci, and with chronic obstructive lung diseases at the NPNT locus. Last, we show that ATP11A, DPP9, NPNT, and MUC1 are highly expressed in lung alveolar epithelial cells, both in COVID-19 uninfected and infected samples. These findings clarify the importance of alternative splicing in lung for COVID-19 and respiratory diseases, providing isoform-based targets for drug discovery.

Involvement of protein L-isoaspartyl methyltransferase in the physiopathology of neurodegenerative diseases: Possible substrates associated with synaptic function.

Synaptic dysfunction is a typical pathophysiologic change in neurodegenerative diseases (NDs) such as Alzheimer's disease (AD), Parkinson's disease (PD), Hintington's disease (HD) and amyotrophic lateral sclerosis (ALS), which involves protein post-translational modifications (PTMs) including L-isoaspartate (L-isoAsp) formed by isomerization of aspartate or deamidation of asparagine. The formation of L-isoAsp could be repaired by protein L-isoaspartyl methyltransferase (PIMT). Some synaptic proteins have been identified as PIMT potential substrates and play an essential role in ensuring synaptic function. In this review, we discuss the role of certain synaptic proteins as PIMT substrates in neurodegenerative disease, thus providing therapeutic synapse-centered targets for the treatment of NDs.

Progress in understanding and treating idiopathic pulmonary fibrosis: recent insights and emerging therapies.

Idiopathic pulmonary fibrosis (IPF) is a long-lasting, continuously advancing, and irrevocable interstitial lung disorder with an obscure origin and inadequately comprehended pathological mechanisms. Despite the intricate and uncharted causes and pathways of IPF, the scholarly consensus upholds that the transformation of fibroblasts into myofibroblasts-instigated by injury to the alveolar epithelial cells-and the disproportionate accumulation of extracellular matrix (ECM) components, such as collagen, are integral to IPF's progression. The introduction of two novel anti-fibrotic medications, pirfenidone and nintedanib, have exhibited efficacy in decelerating the ongoing degradation of lung function, lessening hospitalization risk, and postponing exacerbations among IPF patients. Nonetheless, these pharmacological interventions do not present a definitive solution to IPF, positioning lung transplantation as the solitary potential curative measure in contemporary medical practice. A host of innovative therapeutic strategies are presently under rigorous scrutiny. This comprehensive review encapsulates the recent advancements in IPF research, spanning from diagnosis and etiology to pathological mechanisms, and introduces a discussion on nascent therapeutic methodologies currently in the pipeline.

From target discovery to clinical drug development with human genetics.

The substantial investments in human genetics and genomics made over the past three decades were anticipated to result in many innovative therapies. Here we investigate the extent to which these expectations have been met, excluding cancer treatments. In our search, we identified 40 germline genetic observations that led directly to new targets and subsequently to novel approved therapies for 36 rare and 4 common conditions. The median time between genetic target discovery and drug approval was 25 years. Most of the genetically driven therapies for rare diseases compensate for disease-causing loss-of-function mutations. The therapies approved for common conditions are all inhibitors designed to pharmacologically mimic the natural, disease-protective effects of rare loss-of-function variants. Large biobank-based genetic studies have the power to identify and validate a large number of new drug targets. Genetics can also assist in the clinical development phase of drugs-for example, by selecting individuals who are most likely to respond to investigational therapies. This approach to drug development requires investments into large, diverse cohorts of deeply phenotyped individuals with appropriate consent for genetically assisted trials. A robust framework that facilitates responsible, sustainable benefit sharing will be required to capture the full potential of human genetics and genomics and bring effective and safe innovative therapies to patients quickly.

Converging evidence from exome sequencing and common variants implicates target genes for osteoporosis.

In this study, we leveraged the combined evidence of rare coding variants and common alleles to identify therapeutic targets for osteoporosis. We undertook a large-scale multiancestry exome-wide association study for estimated bone mineral density, which showed that the burden of rare coding alleles in 19 genes was associated with estimated bone mineral density (P < 3.6 × 10-7). These genes were highly enriched for a set of known causal genes for osteoporosis (65-fold; P = 2.5 × 10-5). Exome-wide significant genes had 96-fold increased odds of being the top ranked effector gene at a given GWAS locus (P = 1.8 × 10-10). By integrating proteomics Mendelian randomization evidence, we prioritized CD109 (cluster of differentiation 109) as a gene for which heterozygous loss of function is associated with higher bone density. CRISPR-Cas9 editing of CD109 in SaOS-2 osteoblast-like cell lines showed that partial CD109 knockdown led to increased mineralization. This study demonstrates that the convergence of common and rare variants, proteomics and CRISPR can highlight new bone biology to guide therapeutic development.

Colocalization of expression transcripts with COVID-19 outcomes is rare across cell states, cell types and organs.

Identifying causal genes at GWAS loci can help pinpoint targets for therapeutic interventions. Expression studies can disentangle such loci but signals from expression quantitative trait loci (eQTLs) often fail to colocalize-which means that the genetic control of measured expression is not shared with the genetic control of disease risk. This may be because gene expression is measured in the wrong cell type, physiological state, or organ. We tested whether Mendelian randomization (MR) could identify genes at loci influencing COVID-19 outcomes and whether the colocalization of genetic control of expression and COVID-19 outcomes was influenced by cell type, cell stimulation, and organ. We conducted MR of cis-eQTLs from single cell (scRNA-seq) and bulk RNA sequencing. We then tested variables that could influence colocalization, including cell type, cell stimulation, RNA sequencing modality, organ, symptoms of COVID-19, and SARS-CoV-2 status among individuals with symptoms of COVID-19. The outcomes used to test colocalization were COVID-19 severity and susceptibility as assessed in the Host Genetics Initiative release 7. Most transcripts identified using MR did not colocalize when tested across cell types, cell state and in different organs. Most that did colocalize likely represented false positives due to linkage disequilibrium. In general, colocalization was highly variable and at times inconsistent for the same transcript across cell type, cell stimulation and organ. While we identified factors that influenced colocalization for select transcripts, identifying 33 that mediate COVID-19 outcomes, our study suggests that colocalization of expression with COVID-19 outcomes is partially due to noisy signals even after following quality control and sensitivity testing. These findings illustrate the present difficulty of linking expression transcripts to disease outcomes and the need for skepticism when observing eQTL MR results, even accounting for cell types, stimulation state and different organs.

Effects of Endurance Exercise and Vitamin D Supplementation on Insulin Resistance and Plasma Lipidome in Middle-Aged Adults with Type 2 Diabetes.

(1) Background: We investigated the effects of a 12-week exercise intervention with or without vitamin D supplementation on insulin resistance and the plasma lipidome of participants with type 2 diabetes. We further explored whether the effects of the intervention on glycemic parameters could be associated with the baseline lipidome. (2) Methods: Sixty-one participants were randomly allocated to control (Con), exercise (EX), vitamin D (VD), and EX + VD groups. Multiple glycemic and anthropometric parameters were evaluated before and after intervention. The homeostasis model assessment of insulin resistance (HOMA-IR) was the primary outcome. The plasma lipidome was analyzed before, after, and at an additional 12-week follow-up. Machine learning was applied to establish prediction models for responsiveness of glycemic control. (3) Results: Our interventions failed to improve the HOMA-IR index while fasting glucose was reduced in the EX + VD group (change%, -11.9%; effect size, 0.65; p < 0.05). Both EX and VD interventions altered the plasma lipidome, with EX + VD intervention considerably affecting levels of lyso-phosphatidylcholines and triglycerols containing long-chain unsaturated fatty acids. Such effects could last until 12 weeks after intervention. Notably, there was high inter-individual variability in glycemic parameters including HOMA-IR in response to the interventions, which could be predicted with great accuracy using an optimal panel of baseline lipid predictors alone or in combination with clinical indices, as assessed by an area under the receiver operating characteristic curve value of over 0.9. (4) Conclusions: Although substantial alterations were observed in the plasma lipidome related to glycemic control, our intervention failed to improve HOMA-IR scores, which may have been predominately due to the large inter-individual variability in responses. Basal plasma lipid levels could potentially predict an individual's response to intervention, highlighting the necessity of personalized nutrition.

Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection.

BACKGROUND: Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. METHODS: Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N = 437), we identified 413 higher plasma abundances of protein targets and 30 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p < 0.05). Of these, 62 proteins were validated in an external cohort (p < 0.05, N = 261). RESULTS: We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p < 0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. CONCLUSIONS: Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Acute kidney injury (AKI) is a sudden, sometimes fatal, episode of kidney failure or damage. It is a known complication of COVID-19, albeit through unclear mechanisms. COVID-19 is also associated with kidney dysfunction in the long term, or chronic kidney disease (CKD). There is a need to better understand which patients with COVID-19 are at risk of AKI or CKD. We measure levels of several thousand proteins in the blood of hospitalized COVID-19 patients. We discover and validate sets of proteins associated with severe AKI and CKD in these patients. The markers identified suggest that kidney injury in COVID-19 patients involves damage to kidney cells that reabsorb fluid from urine and reduced blood flow to the heart, causing damage to heart muscles. Our findings might help clinicians to predict kidney injury in patients with COVID-19, and to understand its mechanisms.