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Fulltext A reference genome of Commelinales provides insights into the commelinids evolution and global spread of water hyacinth (Pontederia crassipes)

Huang Y, Guo L, Xie L, Shang N, Wu D, Ye C, et al.

Gigascience, 2024 Jan 02;13.
PMID: 38486346 DOI: 10.1093/gigascience/giae006

Commelinales belongs to the commelinids clade, which also comprises Poales that includes the most important monocot species, such as rice, wheat, and maize. No reference genome of Commelinales is currently available. Water hyacinth (Pontederia crassipes or Eichhornia crassipes), a member of Commelinales, is one of the devastating aquatic weeds, although it is also grown as an ornamental and medical plant. Here, we present a chromosome-scale reference genome of the tetraploid water hyacinth with a total length of 1.22 Gb (over 95% of the estimated size) across 8 pseudochromosome pairs. With the representative genomes, we reconstructed a phylogeny of the commelinids, which supported Zingiberales and Commelinales being sister lineages of Arecales and shed lights on the controversial relationship of the orders. We also reconstructed ancestral karyotypes of the commelinids clade and confirmed the ancient commelinids genome having 8 chromosomes but not 5 as previously reported. Gene family analysis revealed contraction of disease-resistance genes during polyploidization of water hyacinth, likely a result of fitness requirement for its role as a weed. Genetic diversity analysis using 9 water hyacinth lines from 3 continents (South America, Asia, and Europe) revealed very closely related nuclear genomes and almost identical chloroplast genomes of the materials, as well as provided clues about the global dispersal of water hyacinth. The genomic resources of P. crassipes reported here contribute a crucial missing link of the commelinids species and offer novel insights into their phylogeny.
DeepPSP: A Global-Local Information-Based Deep Neural Network for the Prediction of Protein Phosphorylation Sites

Guo L, Wang Y, Xu X, Cheng KK, Long Y, Xu J, et al.

J Proteome Res, 2021 01 01;20(1):346-356.
PMID: 33241931 DOI: 10.1021/acs.jproteome.0c00431

Identification of phosphorylation sites is an important step in the function study and drug design of proteins. In recent years, there have been increasing applications of the computational method in the identification of phosphorylation sites because of its low cost and high speed. Most of the currently available methods focus on using local information around potential phosphorylation sites for prediction and do not take the global information of the protein sequence into consideration. Here, we demonstrated that the global information of protein sequences may be also critical for phosphorylation site prediction. In this paper, a new deep neural network model, called DeepPSP, was proposed for the prediction of protein phosphorylation sites. In the DeepPSP model, two parallel modules were introduced to extract both local and global features from protein sequences. Two squeeze-and-excitation blocks and one bidirectional long short-term memory block were introduced into each module to capture effective representations of the sequences. Comparative studies were carried out to evaluate the performance of DeepPSP, and four other prediction methods using public data sets The F1-score, area under receiver operating characteristic curves (AUROC), and area under precision-recall curves (AUPRC) of DeepPSP were found to be 0.4819, 0.82, and 0.50, respectively, for S/T general site prediction and 0.4206, 0.73, and 0.39, respectively, for Y general site prediction. Compared with the MusiteDeep method, the F1-score, AUROC, and AUPRC of DeepPSP were found to increase by 8.6, 2.5, and 8.7%, respectively, for S/T general site prediction and by 20.6, 5.8, and 18.2%, respectively, for Y general site prediction. Among the tested methods, the developed DeepPSP method was also found to produce best results for different kinase-specific site predictions including CDK, mitogen-activated protein kinase, CAMK, AGC, and CMGC. Taken together, the developed DeepPSP method may offer a more accurate phosphorylation site prediction by including global information. It may serve as an alternative model with better performance and interpretability for protein phosphorylation site prediction.
Multimodal Image Fusion Offers Better Spatial Resolution for Mass Spectrometry Imaging

Guo L, Zhu J, Wang K, Cheng KK, Xu J, Dong L, et al.

Anal Chem, 2023 Jun 27;95(25):9714-9721.
PMID: 37296503 DOI: 10.1021/acs.analchem.3c02002

High-resolution reconstruction has attracted increasing research interest in mass spectrometry imaging (MSI), but it remains a challenging ill-posed problem. In the present study, we proposed a deep learning model to fuse multimodal images to enhance the spatial resolution of MSI data, namely, DeepFERE. Hematoxylin and eosin (H&E) stain microscopy imaging was used to pose constraints in the process of high-resolution reconstruction to alleviate the ill-posedness. A novel model architecture was designed to achieve multi-task optimization by incorporating multi-modal image registration and fusion in a mutually reinforced framework. Experimental results demonstrated that the proposed DeepFERE model is able to produce high-resolution reconstruction images with rich chemical information and a detailed structure on both visual inspection and quantitative evaluation. In addition, our method was found to be able to improve the delimitation of the boundary between cancerous and para-cancerous regions in the MSI image. Furthermore, the reconstruction of low-resolution spatial transcriptomics data demonstrated that the developed DeepFERE model may find wider applications in biomedical fields.
iSegMSI: An Interactive Strategy to Improve Spatial Segmentation of Mass Spectrometry Imaging Data

Guo L, Liu X, Zhao C, Hu Z, Xu X, Cheng KK, et al.

Anal Chem, 2022 Oct 25;94(42):14522-14529.
PMID: 36223650 DOI: 10.1021/acs.analchem.2c01456

Spatial segmentation is a critical procedure in mass spectrometry imaging (MSI)-based biochemical analysis. However, the commonly used unsupervised MSI segmentation methods may lead to inappropriate segmentation results as the MSI data is characterized by high dimensionality and low signal-to-noise ratio. This process can be improved by the incorporation of precise prior knowledge, which is hard to obtain in most cases. In this study, we show that the incorporation of partial or coarse prior knowledge from different sources such as reference images or biological knowledge may also help to improve MSI segmentation results. Here, we propose a novel interactive segmentation strategy for MSI data called iSegMSI, which incorporates prior information in the form of scribble-regularization of the unsupervised model to fine-tune the segmentation results. By using two typical MSI data sets (including a whole-body mouse fetus and human thyroid cancer), the present results demonstrate the effectiveness of the iSegMSI strategy in improving the MSI segmentations. Specifically, the method can be used to subdivide a region into several subregions specified by the user-defined scribbles or to merge several subregions into a single region. Additionally, these fine-tuned results are highly tolerant to the imprecision of the scribbles. Our results suggest that the proposed iSegMSI method may be an effective preprocessing strategy to facilitate the analysis of MSI data.
Evidence for the Higgs Boson Decay to a Z Boson and a Photon at the LHC

Aad G, Abbott B, Abeling K, Abicht NJ, Abidi SH, Aboulhorma A, et al.

Phys Rev Lett, 2024 Jan 12;132(2):021803.
PMID: 38277607 DOI: 10.1103/PhysRevLett.132.021803

The first evidence for the Higgs boson decay to a Z boson and a photon is presented, with a statistical significance of 3.4 standard deviations. The result is derived from a combined analysis of the searches performed by the ATLAS and CMS Collaborations with proton-proton collision datasets collected at the CERN Large Hadron Collider (LHC) from 2015 to 2018. These correspond to integrated luminosities of around 140 fb^{-1} for each experiment, at a center-of-mass energy of 13 TeV. The measured signal yield is 2.2±0.7 times the standard model prediction, and agrees with the theoretical expectation within 1.9 standard deviations.
Fulltext A systematic-search-and-review of registered pharmacological therapies investigated to improve neuro-recovery after a stroke

Lee TH, Uchiyama S, Kusuma Y, Chiu HC, Navarro JC, Tan KS, et al.

Front Neurol, 2024;15:1346177.
PMID: 38356890 DOI: 10.3389/fneur.2024.1346177

BACKGROUND: Stroke burden is largely due to long-term impairments requiring prolonged care with loss of productivity. We aimed to identify and assess studies of different registered pharmacological therapies as treatments to improve post-stroke impairments and/or disabilities.
METHODS: We performed a systematic-search-and-review of treatments that have been investigated as recovery-enhancing or recovery-promoting therapies in adult patients with stroke. The treatment must have received registration or market authorization in any country regardless of primary indication. Outcomes included in the review were neurological impairments and functional/disability assessments. "The best available studies" based on study design, study size, and/or date of publication were selected and graded for level of evidence (LOE) by consensus.
RESULTS: Our systematic search yielded 7,801 citations, and we reviewed 665 full-text papers. Fifty-eight publications were selected as "the best studies" across 25 pharmacological classes: 31 on ischemic stroke, 21 on ischemic or hemorrhagic stroke, 4 on intracerebral hemorrhage, and 2 on subarachnoid hemorrhage (SAH). Twenty-six were systematic reviews/meta-analyses, 29 were randomized clinical trials (RCTs), and three were cohort studies. Only nimodipine for SAH had LOE A of benefit (systematic review and network meta-analysis). Many studies, some of which showed treatment effects, were assessed as LOE C-LD, mainly due to small sample sizes or poor quality. Seven interventions had LOE B-R (systematic review/meta-analysis or RCT) of treatment effects.
CONCLUSION: Only one commercially available treatment has LOE A for routine use in stroke. Further studies of putative neuroprotective drugs as adjunctive treatment to revascularization procedures and more confirmatory trials on recovery-promoting therapies will enhance the certainty of their benefit. The decision on their use must be guided by the clinical profile, neurological impairments, and target outcomes based on the available evidence.
SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=376973, PROSPERO, CRD42022376973.