Role of GDF-15 in diabetic nephropathy: mechanisms, diagnosis, and therapeutic potential.

PURPOSE: Growth differentiation factor 15 (GDF-15) is a cytokine involved in regulating homeostasis, and its expression is up-regulated in response to injury, stress, and inflammation. This study explored the role of GDF-15 in diabetic nephropathy (DN), a severe complication of diabetes mellitus, and its potential as a biomarker for disease progression. METHODS: As a member of the transforming growth factor-ß superfamily, GDF-15 exhibits its renal protective functions primarily through its anti-inflammatory effects and the up-regulation of other renal protective factors. This study evaluated the association between circulating GDF-15 levels and DN progression, examining the underlying mechanisms. RESULTS: Circulating GDF-15 levels are closely linked to the development and progression of DN. While existing research has yielded some consistent conclusions, a comprehensive understanding of the role of GDF-15 in DN pathogenesis is needed to identify new therapeutic targets and strategies. CONCLUSION: GDF-15 has the potential to be a prognostic and diagnostic biomarker for DN. It is crucial to establish appropriate reference ranges and explore their clinical utility in routine practice for validating the role of GDF-15 in DN management. Further interventional studies are required to confirm its clinical value in diagnosing and predicting the progression of DN.

The Transpeptidase Sortase A Binds Nucleic Acids and Mediates Mammalian Cell Labeling.

Wild-type sortase A is an important virulence factor displaying a diverse array of proteins on the surface of bacteria. This protein display relies on the transpeptidase activity of sortase A, which is widely engineered to allow protein ligation and protein engineering based on the interaction between sortase A and peptides. Here an unknown interaction is found between sortase A from Staphylococcus aureus and nucleic acids, in which exogenously expressed engineered sortase A binds oligonucleotides in vitro and is independent of its canonical transpeptidase activity. When incubated with mammalian cells, engineered sortase A further mediates oligonucleotide labeling to the cell surface, where sortase A attaches itself and is part of the labeled moiety. The labeling reaction can also be mediated by many classes of wild-type sortases as well. Cell surface GAG appears involved in sortase-mediated oligonucleotide cell labeling, as demonstrated by CRISPR screening. This interaction property is utilized to develop a technique called CellID to facilitate sample multiplexing for scRNA-seq and shows the potential of using sortases to label cells with diverse oligonucleotides. Together, the binding between sortase A and nucleic acids opens a new avenue to understanding the virulence of wild-type sortases and exploring the application of sortases in biotechnology.

Machine learning-based prediction models to guide the selection of Cas9 variants for efficient gene editing.

The increasing emergence of Cas9 variants has attracted broad interest, as these variants were designed to expand CRISPR applications. New Cas9 variants typically feature higher editing efficiency, improved editing specificity, or alternative PAM sequences. To select Cas9 variants and gRNAs for high-fidelity and efficient genome editing, it is crucial to systematically quantify the editing performances of gRNAs and develop prediction models based on high-quality datasets. Using synthetic gRNA-target paired libraries and next-generation sequencing, we compared the activity and specificity of gRNAs of four SpCas9 variants. The nucleotide composition in the PAM-distal region had more influence on the editing efficiency of HiFi Cas9 and LZ3 Cas9. We further developed machine learning models to predict the gRNA efficiency and specificity for the four Cas9 variants. To aid users from broad research areas, the machine learning models for the predictions of gRNA editing efficiency within human genome sites are available on our website.

A top variant identification pipeline for protein engineering.

Understanding the fitness of protein variants with combinatorial mutations is critical for effective protein engineering. In this issue of Cell Systems, Chu et al. present TopVIP, a top variant identification pipeline that enables accurate picking of the greatest number of best-performing protein variants with high-fitness leveraging zero-shot predictor and low-N iterative sampling.

Comparison of transcriptome-wide N6-methyladenosine profiles from healthy trio families reveals regulator-mediated methylation alterations.

The N6-methyladenosine (m6A) modification is a highly conserved RNA modification found in eukaryotic messenger RNAs (mRNAs). It plays a vital role in regulating various biological processes. Dysregulation of m6A modifications has been linked to a range of complex genetic diseases in humans. However, there has been a lack of comprehensive characterization and comparison of m6A modifications at the transcriptome-wide level within families. To address this gap, we profiled transcriptome-wide m6A methylation in 18 individuals across 6 Yoruba trio families. The m6A methylomes of these 18 individuals revealed that m6A modifications in children showed greater similarity to each other than to their parents. This suggests that m6A modifications are influenced by multiple factors rather than solely determined by genetic factors. Additionally, we found that mRNAs exhibiting m6A modifications specific to children were enriched in cell cycle control processes, while those with m6A modifications specific to parents were associated with chromatin modifications. Furthermore, our analysis on the interactions between differentially expressed m6A-related regulatory genes and age-related genes suggested that age might be one of the factors influencing m6A modifications. In summary, our study provided a valuable dataset that highlighted the differences and functional diversity of m6A modifications within and between trio families.

Multiobjective Multitasking Optimization With Decomposition-Based Transfer Selection.

Multiobjective multitasking optimization (MTO) needs to solve a set of multiobjective optimization problems simultaneously, and tries to speed up their solution by transferring useful search experiences across tasks. However, the quality of transfer solutions will significantly impact the transfer effect, which may even deteriorate the optimization performance with an improper selection of transfer solutions. To alleviate this issue, this article suggests a new multiobjective multitasking evolutionary algorithm (MMTEA) with decomposition-based transfer selection, called MMTEA-DTS. In this algorithm, all tasks are first decomposed into a set of subproblems, and then the transfer potential of each solution can be quantified based on the performance improvement ratio of its associated subproblem. Only high-potential solutions are selected to promote knowledge transfer. Moreover, to diversify the transfer of search experiences, a hybrid transfer evolution method is designed in this article. In this way, more diverse search experiences are transferred from high-potential solutions across different tasks to speed up their convergence. Three well-known benchmark suites suggested in the competition of evolutionary MTO and one real-world problem suite are used to verify the effectiveness of MMTEA-DTS. The experiments validate its advantages in solving most of the test problems when compared to five recently proposed MMTEAs.

Clinical Severity of SARS-CoV-2 Variants during COVID-19 Vaccination: A Systematic Review and Meta-Analysis.

Due to the variation in the SARS-CoV-2 virus, COVID-19 exhibits significant variability in severity. This presents challenges for governments in managing the allocation of healthcare resources and prioritizing health interventions. Clinical severity is also a critical statistical parameter for researchers to quantify the risks of infectious disease, model the transmission of COVID-19, and provide some targeted measures to control the pandemic. To obtain more accurate severity estimates, including confirmed case-hospitalization risk, confirmed case-fatality risk, hospitalization-fatality risk, and hospitalization-ICU risk, we conducted a systematic review and meta-analysis on the clinical severity (including hospitalization, ICU, and fatality risks) of different variants during the period of COVID-19 mass vaccination and provided pooled estimates for each clinical severity metric. All searches were carried out on 1 February 2022 in PubMed for articles published from 1 January 2020 to 1 February 2022. After identifying a total of 3536 studies and excluding 3523 irrelevant studies, 13 studies were included. The severity results show that the Delta and Omicron variants have the highest (6.56%, 0.46%, 19.63%, and 9.06%) and lowest severities (1.51%, 0.04%, 6.01%, and 3.18%), respectively, according to the four clinical severity metrics. Adults over 65 have higher severity levels for all four clinical severity metrics.

Identifying Phage Sequences From Metagenomic Data Using Deep Neural Network With Word Embedding and Attention Mechanism.

Phages are the functional viruses that infect bacteria and they play important roles in microbial communities and ecosystems. Phage research has attracted great attention due to the wide applications of phage therapy in treating bacterial infection in recent years. Metagenomics sequencing technique can sequence microbial communities directly from an environmental sample. Identifying phage sequences from metagenomic data is a vital step in the downstream of phage analysis. However, the existing methods for phage identification suffer from some limitations in the utilization of the phage feature for prediction, and therefore their prediction performance still need to be improved further. In this article, we propose a novel deep neural network (called MetaPhaPred) for identifying phages from metagenomic data. In MetaPhaPred, we first use a word embedding technique to encode the metagenomic sequences into word vectors, extracting the latent feature vectors of DNA words. Then, we design a deep neural network with a convolutional neural network (CNN) to capture the feature maps in sequences, and with a bi-directional long short-term memory network (Bi-LSTM) to capture the long-term dependencies between features from both forward and backward directions. The feature map consists of a set of feature patterns, each of which is the weighted feature extracted by a convolution filter with convolution kernels in the CNN slide along the input feature vectors. Next, an attention mechanism is used to enhance contributions of important features. Experimental results on both simulated and real metagenomic data with different lengths demonstrate the superiority of the proposed MetaPhaPred over the state-of-the-art methods in identifying phage sequences.

Achieved low-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio predicts the pathophysiological evolution of lipid-rich plaques in acute coronary syndromes: an optical coherence tomography study.

Background: As a novel lipoprotein ratio, baseline low-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (LHR) is closely related to the clinical outcomes of acute coronary syndromes (ACS) after percutaneous coronary intervention. However, the pathophysiological impact of achieved LHR (aLHR) on the evolution of non-culprit lipid-rich plaques has not been systematically explored. Methods: Between September 2013 and December 2018, ACS patients with both baseline and 1-year follow-up optical coherence tomography (OCT) examinations were included in current study. They were divided into two groups according to the median value of aLHR at 1 year. Results: Overall, 132 patients with 215 lipid-rich plaques were enrolled, with a median aLHR: 1.62. There were thinner fibrous cap thickness (FCT) (133.3 [70.0-180.0] µm vs. 160.0 [100.0-208.3] µm, p = 0.025) and higher prevalence of thin-cap fibroatheroma (TCFA) (24 [22.4%] vs. 13 [12.0%], p = 0.044) and CLIMA-defined high-risk plaques (12 [11.2%] vs. 3[2.8%], p = 0.015) in the high aLHR group at 1 year. Compared with other serum lipid indexes, aLHR showed the best robust correlation with the evolution of plaque vulnerability in both unadjusted and adjusted analyses. Cut-off value of aLHR to predict the progression of maximal lipid arc and FCT was 1.51. In the adjusted model, aLHR ≥1.51 was an independent predictor of TCFA [odds ratio (OR): 3.008, 95% CI: 1.370 to 6.605, p = 0.006] at 1 year. Conclusions: aLHR correlates well with the evolution of lipid-rich plaques and vulnerable phenotypes at 1-year follow-up, which might be an important and convenient serum indicator in the secondary prevention of ACS.

Optimized minimal genome-wide human sgRNA library.

Genome-wide clustered regularly interspaced short palindromic repeats (CRISPR)-based knockout screening is revolting the genetic analysis of a cellular or molecular phenotype in question but is challenged by the large size of single-guide RNA (sgRNA) library. Here we designed a minimal genome-wide human sgRNA library, H-mLib, which is composed of 21,159 sgRNA pairs assembled based on a dedicated selection strategy from all potential SpCas9/sgRNAs in the human genome. These sgRNA pairs were cloned into a dual-gRNA vector each targeting one gene, resulting in a compact library size nearly identical to the number of human protein-coding genes. The performance of the H-mLib was benchmarked to other CRISPR libraries in a proliferation screening conducted in K562 cells. We also identified groups of core essential genes and cell-type specific essential genes by comparing the screening results from the K562 and Jurkat cells. Together, the H-mLib exemplified high specificity and sensitivity in identifying essential genes while containing minimal library complexity, emphasizing its advantages and applications in CRISPR screening with limited cell numbers.

Deep sampling of gRNA in the human genome and deep-learning-informed prediction of gRNA activities.

Life science studies involving clustered regularly interspaced short palindromic repeat (CRISPR) editing generally apply the best-performing guide RNA (gRNA) for a gene of interest. Computational models are combined with massive experimental quantification on synthetic gRNA-target libraries to accurately predict gRNA activity and mutational patterns. However, the measurements are inconsistent between studies due to differences in the designs of the gRNA-target pair constructs, and there has not yet been an integrated investigation that concurrently focuses on multiple facets of gRNA capacity. In this study, we analyzed the DNA double-strand break (DSB)-induced repair outcomes and measured SpCas9/gRNA activities at both matched and mismatched locations using 926,476 gRNAs covering 19,111 protein-coding genes and 20,268 non-coding genes. We developed machine learning models to forecast the on-target cleavage efficiency (AIdit_ON), off-target cleavage specificity (AIdit_OFF), and mutational profiles (AIdit_DSB) of SpCas9/gRNA from a uniformly collected and processed dataset by deep sampling and massively quantifying gRNA capabilities in K562 cells. Each of these models exhibited superlative performance in predicting SpCas9/gRNA activities on independent datasets when benchmarked with previous models. A previous unknown parameter was also empirically determined regarding the "sweet spot" in the size of datasets used to establish an effective model to predict gRNA capabilities at a manageable experimental scale. In addition, we observed cell type-specific mutational profiles and were able to link nucleotidylexotransferase as the key factor driving these outcomes. These massive datasets and deep learning algorithms have been implemented into the user-friendly web service http://crispr-aidit.com to evaluate and rank gRNAs for life science studies.

Identification of SARS-CoV-2 PLpro and 3CLpro human proteome substrates using substrate phage display coupled with protein network analysis.

Viral proteases play key roles in viral replication, and they also facilitate immune escape by proteolyzing diverse target proteins. Deep profiling of viral protease substrates in host cells is beneficial for understanding viral pathogenesis and for antiviral drug discovery. Here, we utilized substrate phage display coupled with protein network analysis to identify human proteome substrates of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral proteases, including papain-like protease (PLpro) and 3C-like protease (3CLpro). We first performed peptide substrates selection of PLpro and 3CLpro, and we then used the top 24 preferred substrate sequences to identify a total of 290 putative protein substrates. Protein network analysis revealed that the top clusters of PLpro and 3CLpro substrate proteins contain ubiquitin-related proteins and cadherin-related proteins, respectively. We verified that cadherin-6 and cadherin-12 are novel substrates of 3CLpro, and CD177 is a novel substrate of PLpro using in vitro cleavage assays. We thus demonstrated that substrate phage display coupled with protein network analysis is a simple and high throughput method to identify human proteome substrates of SARS-CoV-2 viral proteases for further understanding of virus-host interactions.

Effect of Induction Therapy With Olamkicept vs Placebo on Clinical Response in Patients With Active Ulcerative Colitis: A Randomized Clinical Trial.

Importance: Olamkicept, a soluble gp130-Fc-fusion-protein, selectively inhibits interleukin 6 (IL-6) trans-signaling by binding the soluble IL-6 receptor/IL-6 complex. It has anti-inflammatory activities in inflammatory murine models without immune suppression. Objective: To assess the effect of olamkicept as induction therapy in patients with active ulcerative colitis. Design, Setting, and Participants: Randomized, double-blind, placebo-controlled phase 2 trial of olamkicept in 91 adults with active ulcerative colitis (full Mayo score ≥5, rectal bleeding score ≥1, endoscopy score ≥2) and an inadequate response to conventional therapy. The study was conducted at 22 clinical study sites in East Asia. Patients were recruited beginning in February 2018. Final follow-up occurred in December 2020. Interventions: Eligible patients were randomized 1:1:1 to receive a biweekly intravenous infusion of olamkicept 600 mg (n = 30) or 300 mg (n = 31) or placebo (n = 30) for 12 weeks. Main Outcomes and Measures: The primary end point was clinical response at week 12 (defined as ≥3 and ≥30% decrease from baseline total Mayo score; range, 0-12 [worst] with ≥1 decrease and ≤1 in rectal bleeding [range, 0-3 {worst}]). There were 25 secondary efficacy outcomes, including clinical remission and mucosal healing at week 12. Results: Ninety-one patients (mean age, 41 years; 25 women [27.5%]) were randomized; 79 (86.8%) completed the trial. At week 12, more patients receiving olamkicept 600 mg (17/29 [58.6%]) or 300 mg (13/30 [43.3%]) achieved clinical response than placebo (10/29 [34.5%]), with adjusted difference vs placebo of 26.6% (90% CI, 6.2% to 47.1%; P = .03) for 600 mg and 8.3% (90% CI, -12.6% to 29.1%; P = .52) for 300 mg. Among patients randomized to receive 600 mg olamkicept, 16 of 25 secondary outcomes were statistically significant compared with placebo. Among patients randomized to receive 300 mg, 6 of 25 secondary outcomes were statistically significant compared with placebo. Treatment-related adverse events occurred in 53.3% (16/30) of patients receiving 600 mg olamkicept, 58.1% (18/31) receiving 300 mg olamkicept, and 50% (15/30) receiving placebo. The most common drug-related adverse events were bilirubin presence in the urine, hyperuricemia, and increased aspartate aminotransferase levels, and all were more common in the olamkicept groups compared with placebo. Conclusions and Relevance: Among patients with active ulcerative colitis, biweekly infusion of olamkicept 600 mg, but not 300 mg, resulted in a greater likelihood of clinical response at 12 weeks compared with placebo. Further research is needed for replication and to assess longer-term efficacy and safety. Trial Registration: ClinicalTrials.gov Identifier: NCT03235752.

PEAC-seq adopts Prime Editor to detect CRISPR off-target and DNA translocation.

CRISPR technology holds significant promise for biological studies and gene therapies because of its high flexibility and efficiency when applied in mammalian cells. But endonuclease (e.g., Cas9) potentially generates undesired edits; thus, there is an urgent need to comprehensively identify off-target sites so that the genotoxicities can be accurately assessed. To date, it is still challenging to streamline the entire process to specifically label and efficiently enrich the cleavage sites from unknown genomic locations. Here we develop PEAC-seq, in which we adopt the Prime Editor to insert a sequence-optimized tag to the editing sites and enrich the tagged regions with site-specific primers for high throughput sequencing. Moreover, we demonstrate that PEAC-seq could identify DNA translocations, which are more genotoxic but usually overlooked by other off-target detection methods. As PEAC-seq does not rely on exogenous oligodeoxynucleotides to label the editing site, we also conduct in vivo off-target identification as proof of concept. In summary, PEAC-seq provides a comprehensive and streamlined strategy to identify CRISPR off-targeting sites in vitro and in vivo, as well as DNA translocation events. This technique further diversified the toolkit to evaluate the genotoxicity of CRISPR applications in research and clinics.

Automatic assessment of calcified plaque and nodule by optical coherence tomography adopting deep learning model.

Optical coherence tomography (OCT) has become the best imaging tool to assess calcified plaque and nodule. However, every OCT pullback has numerous images, and artificial analysis requires too much time and energy. Thus, it is unsuitable for clinical application. This study aimed to develop and validate an automatic assessment of calcified plaque and nodule by OCT using deep-learning model. The OCT images of calcified plaque and nodule were labeled by two expert readers based on the consensus. A deep-learning model with a MultiScale and MultiTask u-net network (MS-MT u-net) was developed. Then, with the ground truth labeled by expert readers as reference, the diagnostic accuracy and agreement of the model was validated. For the pixelwise evaluation of calcified plaque, the model had a high performance with precision (93.95%), recall (88.95%), and F1 score (91.38%). For the lesion-level evaluation of calcified plaque, the quantitative metrics by the model excellently correlated with the ground truth (calcium score, r = 0.90, p < 0.01; calcified volume, r = 0.99, p < 0.01). For calcified nodules, the model showed excellent diagnostic performance including sensitivity (91.7%), specificity (89.3%), and accuracy (91.0%). We developed a novel deep-learning model to identify the attributes of calcified plaque and nodule. This model provided excellent diagnostic accuracy and agreement with the ground truth, thereby reducing the subjectivity of manual measurements and substantially saving time. These findings can help practitioners efficiently adopt appropriate therapeutic strategies to treat calcified lesions.

Tissue-Characteristic Expression of Mouse Proteome.

The mouse is a valuable model organism for biomedical research. Here, we established a comprehensive spectral library and the data-independent acquisition-based quantitative proteome maps for 41 mouse organs, including some rarely reported organs such as the cornea, retina, and nine paired organs. The mouse spectral library contained 178,304 peptides from 12,320 proteins, including 1678 proteins not reported in previous mouse spectral libraries. Our data suggested that organs from the nervous system and immune system expressed the most distinct proteome compared with other organs. We also found characteristic protein expression of immune-privileged organs, which may help understanding possible immune rejection after organ transplantation. Each tissue type expressed characteristic high-abundance proteins related to its physiological functions. We also uncovered some tissue-specific proteins which have not been reported previously. The testis expressed highest number of tissue-specific proteins. By comparison of nine paired organs including kidneys, testes, and adrenal glands, we found left organs exhibited higher levels of antioxidant enzymes. We also observed expression asymmetry for proteins related to the apoptotic process, tumor suppression, and organ functions between the left and right sides. This study provides a comprehensive spectral library and a quantitative proteome resource for mouse studies.

An in-library ligation strategy and its application in CRISPR/Cas9 screening of high-order gRNA combinations.

Simultaneous targeting multiple genes is a big advantage of CRISPR (clustered regularly interspaced short palindromic repeats) genome editing but challenging to achieve in CRISPR screening. The crosstalk among genes or gene products is a common and fundamental mechanism to ensure cellular stability and functional diversity. However, the screening approach to map high-order gene combinations to the interesting phenotype is still lacking. Here, we developed a universal in-library ligation strategy and applied it to generate multiplexed CRISPR library, which could perturb four pre-designed targets in a cell. We conducted in vivo CRISPR screening for potential guide RNA (gRNA) combinations inducing anti-tumor immune responses. Simultaneously disturbing a combination of three checkpoints in CD8+ T cells was demonstrated to be more effective than disturbing Pdcd1 only for T cell activation in the tumor environment. This study developed a novel in-library ligation strategy to facilitate the multiplexed CRISPR screening, which could extend our ability to explore the combinatorial outcomes from coordinated gene behaviors.

Reproduction Number of the Omicron Variant Triples That of the Delta Variant.

COVID-19 remains a persistent threat, especially with the predominant Omicron variant emerging in early 2022, presenting with high transmissibility, immune escape, and waning. There is a need to rapidly ramp up global vaccine coverage while enhancing public health and social measures. Timely and reliable estimation of the reproduction number throughout a pandemic is critical for assessing the impact of mitigation efforts and the potential need to adjust for control measures. We conducted a systematic review on the reproduction numbers of the Omicron variant and gave the pooled estimates. We identified six studies by searching PubMed, Embase, Web of Science, and Google Scholar for articles published between 1 January 2020 and 6 March 2022. We estimate that the effective reproduction number ranges from 2.43 to 5.11, with a pooled estimate of 4.20 (95% CI: 2.05, 6.35). The Omicron variant has an effective reproduction number which is triple (2.71 (95% CI: 1.86, 3.56)) that of the Delta variant.