Molecular Candidates Research Articles

Molecular diagnoses and candidate gene identification in the congenital heart disease cohorts of the 100,000 genomes project.

Congenital heart disease (CHD) describes a structural cardiac defect present from birth. A cohort of participants recruited to the 100,000 Genomes Project (100 kGP) with syndromic CHD (286 probands) and familial CHD (262 probands) were identified. "Tiering" following genome sequencing data analysis prioritised variants in gene panels linked to participant phenotype. To improve diagnostic rates in the CHD cohorts, we implemented an agnostic de novo Gene Discovery Pipeline (GDP). We assessed de novo variants (DNV) for unsolved CHD participants following filtering to select variants of interest in OMIM-morbid genes, as well as novel candidate genes. The 100kGP CHD cohorts had low rates of pathogenic diagnoses reported (combined CHD "solved" 5.11% (n = 28/548)). Our GDP provided diagnostic uplift of nearly one third (1.28% uplift; 5.11% vs. 6.39%), with a new or potential diagnosis for 9 additional participants with CHD. When a filtered DNV occurred within a non-morbid gene, our GDP prioritised biologically-plausible candidate CHD genes (n = 79). Candidate variants occurred in both genes linked to cardiac development (e.g. AKAP13 and BCAR1) and those currently without a known role (e.g. TFAP2C and SETDB1). Sanger sequencing of a cohort of patients with CHD did not identify a second de novo variant in the candidate dataset. However, literature review identified rare variants in HMCN1, previously reported as causative for pulmonary atresia, confirming the approach utility. As well as diagnostic uplift for unsolved participants of the 100 kGP, our GDP created a dataset of candidate CHD genes, which forms an important resource for further evaluation.

Abstract B038: Establishing a screening platform to identify key signaling molecules regulating cold tumor formation through immune evasion and exclusion

Abstract Many trial data have shown that cancer immunotherapy can successfully induce long lasting anti-cancer immune responses and increased the survival rate of cancer patients. One of the key challenges facing cancer immunotherapy is the low response rate of cancer patients to immunotherapy, suggesting that these cancer patients may have acquired resistance toward immunotherapy before treatments. Therefore, there is an unmet need for identifying the underlying mechanisms that promote resistance. One important hypothesis is that immune-excluded tumors (so called cold tumors) that lack of functional T cells have been often found in cancer patients who cannot respond well to cancer immunotherapy. To address this, we have successfully established a preclinical model for cold lung tumors, conducted RNA-seq, performed hallmark gene set enrichment analysis (GSEA) on cold tumor cells sorted in vivo, and identified several candidates associated with epithelial-mesenchymal transition, Wnt signaling, type I and II interferon (IFN) signaling, and p53 signaling. We hypothesize that their roles may either promote or inhibit the formation of cold tumors. One of the molecular candidates we identified, a molecule designated as IRC001, represents a crucial proof of concept for our experimental tumor model. We have unveiled a novel role for IRC001 in enhancing anti-tumor immunity, leading to the transformation of cold tumors into hot tumors and facilitating swift tumor regression through pathways reliant on T cells and type I IFN signaling. Our mechanistic studies have shown that IRC001 can trigger type I IFN signaling by engaging STING/TBK-1/IRF3-dependent pathways. To delve deeper into the spatial and molecular distinctions in mouse tumors (± IRC001), we initiated a preliminary study utilizing 10x Genomics/Visium to examine the spatial variations between these tumor types. Significantly, our spatial transcriptomic analysis revealed consistent immune-excluded and immune-enriched characteristics compared to traditional RNA sequencing methods. We conducted an analysis of TCGA datasets (The Cancer Genome Atlas) and observed that elevated IRC001 expression is associated with improved prognosis in SKCM (Skin Cutaneous Melanoma). Furthermore, our bioinformatics analysis revealed that melanoma patients exhibiting heightened IRC001 expression displayed positive responses to immune checkpoint blockade therapies. Therefore, we believe that our experimental tumor model can provide vital insights into the cold tumor microenvironment, shaping future therapeutic strategies, and pinpointing molecular candidates that can assist in selecting patients for successful immunotherapy. Citation Format: Yi-Ting Liao, Li-Mei Chen, Wen-Jye Lin. Establishing a screening platform to identify key signaling molecules regulating cold tumor formation through immune evasion and exclusion [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr B038.

Diffusion Monte Carlo calculation of fully heavy pentaquarks

Using a diffusion Monte Carlo algorithm, we calculated the spectra of all possible S-wave fully heavy pentaquarks within the framework of the quark model. Our aim was to compare the masses of different spin-color configurations corresponding to the same spatial wave function for each quark composition. In particular, we computed the masses of the configuration with the maximum number of undistinguishable quarks and those of all the possible baryon+meson splittings compatible with the total number of b’s and c’s for a given pentaquark. What we found was that, in all cases, compact baryon+meson configurations had lower masses than their more symmetric counterparts. Moreover, those “molecular” associations were unstable (with larger masses) that their infinitely separated noninteracting pieces except in the case of (ccb)(cb¯) and (bbc)(bc¯) pentaquarks. In any case, even for unstable arrangements, the excess masses are so small (∼20–60 MeV) as to make those compact molecular structures serious candidates to be experimentally detected. Published by the American Physical Society 2024

Zipper‐Like Dynamic Switching of Coordination Bonds Gives a Polar Bimetallic Halide Toward Self‐Driven X‐Ray Detection

AbstractPolar molecular crystals hold a promise for controlling bulk physical properties originated in their unique switchable polarity via structural transformation. However, the mechanisms for switching polarization are mainly limited to displacive and disorder‐order phase transitions, which rarely involve the reconstruction of chemical bonds. Here, we have switched and tuned electric polarization in a bimetallic halide, (Neopentylammonium)4AgBiBr8 (1), as verified by light‐excited pyroelectric effect. Most notably, its Ag−Br coordination bonds show a zipper‐like dynamic switching behavior from the ‘locked’ to ‘unlocked’ state, namely, reconstruction of chemical bonds. Coupling with the dynamic ordering of organic cations, this bond‐switching transition makes a contribution to switchable polarization of 1. As expected, its polarity creates pyroelectric effect for self‐driven X‐ray detection with high sensitivity (3.8×103 μC Gy−1 cm−2) and low limit of detection (4.8 nGy s−1). This work on the bond‐switching mechanism provides an avenue to design polar molecular candidate for smart optoelectronic devices.

Zipper-Like Dynamic Switching of Coordination Bonds Gives a Polar Bimetallic Halide Toward Self-Driven X-Ray Detection.

Polar molecular crystals hold a promise for controlling bulk physical properties originated in their unique switchable polarity via structural transformation. However, the mechanisms for switching polarization are mainly limited to displacive and disorder-order phase transitions, which rarely involve the reconstruction of chemical bonds. Here, we have switched and tuned electric polarization in a bimetallic halide, (Neopentylammonium)4AgBiBr8 (1), as verified by light-excited pyroelectric effect. Most notably, its Ag-Br coordination bonds show a zipper-like dynamic switching behavior from the 'locked' to 'unlocked' state, namely, reconstruction of chemical bonds. Coupling with the dynamic ordering of organic cations, this bond-switching transition makes a contribution to switchable polarization of 1. As expected, its polarity creates pyroelectric effect for self-driven X-ray detection with high sensitivity (3.8×103 μC Gy-1 cm-2) and low limit of detection (4.8 nGy s-1). This work on the bond-switching mechanism provides an avenue to design polar molecular candidate for smart optoelectronic devices.

Exploring the response of yellow lupine (Lupinus luteus L.) root to drought mediated by pathways related to phytohormones, lipid, and redox homeostasis.

Yellow lupine (Lupinus luteus L.) is a high-protein crop of considerable economic and ecological significance. It has the ability to fix atmospheric nitrogen in symbiosis with Rhizobium, enriching marginal soils with this essential nutrient and reducing the need for artificial fertilizers. Additionally, lupine produces seeds with a high protein content, making it valuable for animal feed production. However, drought negatively affects lupine development, its mutualistic relationship with bacteria, and overall yield. To understand how lupine responds to this stress, global transcriptome sequencing was conducted, along with in-depth biochemical, chromatography, and microscopy analyses of roots subjected to drought. The results presented here contribute to strategies aimed at mitigating the effects of water deficit on lupine growth and development. Based on RNA-seq, drought-specific genes were identified and annotated to biological pathways involved in phytohormone biosynthesis/signaling, lipid metabolism, and redox homeostasis. Our findings indicate that drought-induced disruption of redox balance characterized by the upregulation of reactive oxygen species (ROS) scavenging enzymes, coincided with the accumulation of lipid-metabolizing enzymes, such as phospholipase D (PLD) and lipoxygenase (LOX). This disruption also led to modifications in lipid homeostasis, including increased levels of triacylglycerols (TAG) and free fatty acids (FFA), along with a decrease in polar lipid content. Additionally, the stress response involved alterations in the transcriptional regulation of the linolenic acid metabolism network, resulting in changes in the composition of fatty acids containing 18 carbons. The first comprehensive global transcriptomic profiles of lupine roots, combined with the identification of key stress-responsive molecules, represent a significant advancement in understanding lupine's responses to abiotic stress. The increased expression of the Δ12DESATURASE gene and enhanced PLD activity lead to higher level of linoleic acid (18:2), which is subsequently oxidized by LOX, resulting in membrane damage and malondialdehyde (MDA) accumulation. Oxidative stress elevates the activities of superoxide dismutase (SOD), ascorbate peroxidase (APX), and catalase (CAT), while the conversion of FFAs into TAGs provides protection against ROS. This research offers valuable molecular and biochemical candidates with significant potential to enhance drought tolerance . It enables innovative strategies in lupine breeding and crop improvement to address critical agricultural challenges.

HDAC6 as a Prognostic Factor and Druggable Target in HER2-Positive Breast Cancer.

Adjuvant trastuzumab is the standard of care for HER2+ breast cancer (BC) patients. However, >50% of patients become resistant. This study aimed at the identification of the molecular factors associated with disease relapse and their further investigation as therapeutically exploitable targets. Analyses were conducted on formalin-fixed paraffin-embedded tissues of the primary tumors of relapsed (cases) and not relapsed (controls) HER2+ BC patients treated with adjuvant trastuzumab. The nCounter Human Breast Cancer Panel 360 was used. Logistic regression and partitioning around medoids were employed to identify the genes associated with disease recurrence. Cytotoxicity experiments using trastuzumab-resistant cell lines and a network pharmacology approach were carried out to investigate drug efficacy. A total of 52 patients (26 relapsed and 26 not relapsed) were analyzed. We found that a higher expression of HDAC6 was significantly associated with an increased risk of recurrence, with an adjusted OR of 3.20 (95% CI 1.38-9.91, p = 0.016). Then, we investigated the cytotoxic activity of the selective HDAC6 inhibitor Nexturastat A (NextA) on HER2+ cell lines, which were both sensitive and trastuzumab-resistant. A sub-cytotoxic concentration of NextA, combined with trastuzumab, showed a synergistic effect on BC cell lines. Finally, using a network pharmacology approach, we identified HSP90AA1 as the putative molecular candidate responsible for the synergism observed in vitro. Our findings encourage the exploration of the role of HDAC6 as a prognostic factor and the combinatorial use of HDAC6 selective inhibitors combined with trastuzumab in HER2+ BC, in particular for those patients experiencing drug resistance.

Lipids Determine the Toxicity of Human Islet Polypeptide Aggregates in Vivo

The onset and progression of type 2 diabetes is linked to the accumulation and aggregation of human islet amyloid polypeptide (hIAPP) in the pancreas. Amyloid oligomers and fibrils formed as a result of such aggregation exert high cytotoxicity. Although some pieces of evidence suggest that lipids could alter the rate of hIAPP aggregation, the effect of lipids on the aggregation properties of this peptide remains unclear. In this study, we investigate the effect of sphingo-, anionic and zwitterionic phospholipids with different lengths of fatty acids on the aggregation of hIAPP. We found that anionic lipids drastically accelerate peptide aggregation, whereas this effect was substantially weaker for sphingo- and zwitterionic phospholipids. Biophysical analysis revealed that the presence of lipids resulted in substantial differences in morphology and secondary structure of hIAPP fibrils compared to the protein aggregates grown in the lipid-free environment. We also found that zwitterionic phospholipids drastically increased cytotoxicity of hIAPP aggregates, whereas this effect was less evident for sphingo- and anionic phospholipids. Our results showed that drastic differences in lipid-determined cytotoxicity of hIAPP aggregates were linked to molecular mechanisms of autophagy, exocytosis, and unfolded protein response. These findings suggest that molecular candidates that could disrupt protein-lipid interactions would allow for deceleration of the onset and progression of type 2 diabetes.

Plastic Events of the Vestibular Nucleus: the Initiation of Central Vestibular Compensation.

Vestibular compensation is a physiological response of the vestibular organs within the inner ear. This adaptation manifests during consistent exposure to acceleration or deceleration, with the vestibular organs incrementally adjusting to such changes. The molecular underpinnings of vestibular compensation remain to be fully elucidated, yet emerging studies implicate associations with neuroplasticity and signal transduction pathways. Throughout the compensation process, the vestibular sensory neurons maintain signal transmission to the central equilibrium system, facilitating adaptability through alterations in synaptic transmission and neuronal excitability. Notable molecular candidates implicated in this process include variations in ion channels and neurotransmitter profiles, as well as neuronal and synaptic plasticity, metabolic processes, and electrophysiological modifications. This study consolidates the current understanding of the molecular events in vestibular compensation, augments the existing research landscape, and evaluates contemporary therapeutic strategies. Furthermore, this review posits potential avenues for future research that could enhance our comprehension of vestibular compensation mechanisms.

MsFtsH8 Enhances the Tolerance of PEG-Simulated Drought Stress by Boosting Antioxidant Capacity in Medicago sativa L.

Drought is a major abiotic stress that limits the growth and yield of alfalfa, a vital forage legume. The plant metalloproteinase Filamentation temperature-sensitive H (FtsH) is an ATP- and Zn2+-dependent enzyme that plays a significant character in the plant's response to environmental stress. However, its functional role in drought resistance remains largely unexplored. This study investigates the drought tolerance role of alfalfa MsFtsH8 by analyzing the growth, physiology, and gene expression of overexpressing plants under drought conditions. The results demonstrated that both MsFtsH8-overexpressing Arabidopsis and alfalfa plants exhibited superior growth condition and enhanced membrane stability. The overexpressing alfalfa plants also showed reduced MDA levels, higher proline content, lower H2O2 accumulation, an increased activity of antioxidant-related enzymes (SOD, POD, and CAT) activity, and an elevated expression of antioxidant-related genes. These results indicated that the overexpression of MsFtsH8 enhanced growth, improved osmotic regulation, reduced ROS levels, and increased antioxidative capacity, ultimately leading to greater drought tolerance in alfalfa. Our findings suggest that MsFtsH8 mitigates oxidative damage caused by drought by modulating the plant's antioxidant system, thus improving drought tolerance in alfalfa. This study provides a molecular basis and candidate genes for enhancing drought resistance in alfalfa through genetic engineering.

Identification of neutrophil extracellular traps genes as potential biomarkers in psoriasis based on bioinformatics analysis

The epidermal infiltration of neutrophils is a hallmark of psoriasis (PSO) and its activation leads to the release of neutrophil extracellular traps (NETs). However, the molecular mechanism of NETs-related genes (NETRGs) has not been extensively studied in PSO. To define NETs-related-biomarkers for PSO. The GSE13355 and GSE78097 datasets, and NETRGs gene set were included in this study. The datasets used in this study were all microarray data. The weighted gene co-expression network analysis (WGCNA) and machine learning algorithms were used to mine key genes. Later on, single-gene gene set enrichment analysis (GSEA) and immune infiltration analysis were implemented. Finally, the expression of key genes was verified using quantitative real-time fluorescence PCR (qRT-PCR). A total of 3 key genes (S100A9, CLEC7A, and CXCR4) were derived, and they all had excellent diagnostic performance. The single-gene GSEA enrichment results indicated that the key genes were mainly enriched in the chemokine signaling pathway and humoral immune response in the high-expression group, while focal adhesion was enriched in the low-expression group. The correlation analysis indicated that all key genes were strongly negatively correlated with resting mast cells and TGF-β family member receptor, while they were strongly positively correlated with activated CD4 memory T cells and antigen processing and presentation. Lastly, the experimental results showed that the expression trends of key genes were consistent with public database. In this study, we successfully screened three potential PSO diagnostic genes (S100A9, CLEC7A and CXCR4) that were closely related to NETs, and these findings not only provided new molecular marker candidates for the precise diagnosis of PSO patients, but also revealed possible future therapeutic targets. However, further in-depth research and validation were necessary.

The Prospective role of lapatinib as an adjuvant therapy in prevalent cancers: Insights from in silico analysis targeting EGFR and HER2

IntroductionVarious pieces of evidence suggest an elevation in the levels of EGFR and HER2 in different cancers leading to the proliferation, invasion, and metastasis of cancer cells. In this study, we conducted a comprehensive investigation into the expression alterations of these two receptors in various cancers using in silico data. In addition, we investigated the therapeutic potential of lapatinib as an inhibitor of these receptors in various cancer types. MethodsRNAseq data for prevalent cancers were downloaded from The Cancer Genome Atlas (TCGA). After initial preprocessing, expression changes of HER2, EGFR, and candidate genes—identified based on their association with EGFR and HER2 signaling pathways—were examined. Human protein atlas data were utilized to assess the protein expression of HER2 and EGFR. GSE129254 was employed to identify molecular pathways and candidate genes associated with lapatinib. The protein-protein interaction network was used to identify lapatinib-influenced hub genes. Clinical data for common cancers were used to investigate the correlation between the expression of candidate genes and patients' mortality rates by Cox regression test. ResultsThe findings clearly indicated a significant increase in the expression levels of HER2 and EGFR in cancers such as kidney, lung, breast, bladder, pancreas, head and neck, stomach, and endometrial, both at the mRNA and protein levels (p-value <0.01). Additionally, more than 30 % of samples in some cancers showed a twofold increase in HER2 or EGFR expression. The analysis of GSE129254 data revealed that lapatinib reduces the expression of numerous genes associated with cell proliferation. METTL1, LYAR, LTV1, CCND1, NOP2, and DDX21 were identified as hub genes related to the effect of lapatinib. Our results demonstrated that many hub genes exhibited elevated expression in candidate cancers, and the upregulation of some of them was correlated with poor prognosis. ConclusionOur results indicate an upregulation in the expression levels of HER2 and EGFR in certain common cancers, suggesting that lapatinib, in addition to breast cancer, could be considered for the treatment of these cancers. Furthermore, we demonstrated that some genes with increased expression in prevalent cancers and associated with poor prognosis have the potential to be modulated by lapatinib.

Characterizing virulence differences in a parasitoid wasp through comparative transcriptomic and proteomic

BackgroundTwo strains of the endoparasitoid Cotesia typhae (Hymenoptera: Braconidae) present a differential parasitism success on the host, Sesamia nonagrioides (Lepidoptera: Noctuidae). One is virulent on both permissive and resistant host populations, and the other only on the permissive host. This interaction provides a very interesting frame for studying virulence factors. Here, we used a combination of comparative transcriptomic and proteomic analyses to unravel the molecular basis underlying virulence differences between the strains.ResultsFirst, we report that virulence genes are mostly expressed during the pupal stage 24 h before adult emergence of the parasitoid. Especially, 55 proviral genes are up-regulated at this stage, while their expression is only expected in the host. Parasitoid gene expression in the host increases from 24 to 96 h post-parasitism, revealing the expression of 54 proviral genes at early parasitism stage and the active participation of teratocytes to the parasitism success at the late stage. Secondly, comparison between strains reveals differences in venom composition, with 12 proteins showing differential abundance. Proviral expression in the host displays a strong temporal variability, along with differential patterns between strains. Notably, a subset of proviral genes including protein-tyrosine phosphatases is specifically over-expressed in the resistant host parasitized by the less virulent strain, 24 h after parasitism. This result particularly hints at host modulation of proviral expression. Combining proteomic and transcriptomic data at various stages, we identified 8 candidate genes to support the difference in reproductive success of the two strains, one proviral and 7 venom genes, one of them being also produced within the host by the teratocytes.ConclusionsThis study sheds light on the temporal expression of virulence factors of Cotesia typhae, both in the host and in the parasitoid. It also identifies potential molecular candidates driving differences in parasitism success between two strains. Together, those findings provide a path for further exploration of virulence mechanisms in parasitoid wasps, and offer insights into host-parasitoid coevolution.

Exploring the mass spectrum and electromagnetic property of the ΞccK(*) and ΞccK¯(*) molecules

Using the one-boson-exchange model, we investigate the interactions between the doubly charmed baryon Ξcc(3621) and the S wave (anti-)kaon accounting for the S−D wave mixing and coupled-channel effects. We find the coupled ΞccK/ΞccK* state with I(JP)=0(1/2−), the ΞccK* state with 0(1/2−), the ΞccK¯ state with 0(1/2−), and the ΞccK¯* states with 0(1/2−,3/2−) can be recommended as good doubly charmed molecular candidates with strangeness |S|=1. We further examine their M1 radiative decay behaviors and magnetic moments within the constituent quark model framework. This information can enhance our understanding of their inner structures, including the distribution of electric charge and the orientation of the constituent quarks’ spins. Published by the American Physical Society 2024

Exploring doubly heavy tetraquarks in a constituent-quark-model based meson-meson coupled-channels approach

The LHCb Collaboration announced in 2021 the discovery of a new tetraquarklike state, named Tcc+, with minimum quark content ccu¯d¯, close to the D0D*+ threshold. This has motivated countless theoretical works trying to identify the dynamics which is responsible of the formation of such state; in particular, the one performed by us in Ortega [; ], where a D0D*+ molecular candidate whose mass, width, scattering length, and effective ranges are in reasonable agreement with experimental measurements. We explore herein the possibility of having Tcc+ partners in all doubly heavy tetraquark sectors, considering doubly represented light antiquarks u, d, or s, and taking into account all possible spin-parity quantum numbers. The computation is done using a constituent-quark-model based meson-meson coupled-channels framework which has been tested many times in the last fifteen years describing conventional heavy mesons and baryons, their coupling with hadron-hadron thresholds but also in exploring its application to compact multiquark structures. The advantage of using an approach with such a relatively large history is that it allows us to make predictions because all the parameters have already been constrained from our previous works. Then, from this perspective, we present a parameter-free model-dependent prediction of doubly-heavy tetraquarks that may be partners of the discovered Tcc+ state. Published by the American Physical Society 2024

Streamlining Phenotype Classification and Highlighting Feature Candidates: A Screening Method for Non-Targeted Ion Mobility Spectrometry-Mass Spectrometry (IMS-MS) Data.

Nontargeted analysis (NTA) is increasingly utilized for its ability to identify key molecular features beyond known targets in complex samples. NTA is particularly advantageous in exploratory studies aimed at identifying phenotype-associated features or molecules able to classify various sample types. However, implementing NTA involves extensive data analyses and labor-intensive annotations. To address these limitations, we developed a rapid data screening capability compatible with NTA data collected on a liquid chromatography, ion mobility spectrometry, and mass spectrometry (LC-IMS-MS) platform that allows for sample classification while highlighting potential features of interest. Specifically, this method aggregates the thousands of IMS-MS spectra collected across the LC space for each sample and collapses the LC dimension, resulting in a single summed IMS-MS spectrum for screening. The summed IMS-MS spectra are then analyzed with a bootstrapped Lasso technique to identify key regions or coordinates for phenotype classification via support vector machines. Molecular annotations are then performed by examining the features present in the selected coordinates, highlighting potential molecular candidates. To demonstrate this summed IMS-MS screening approach, we applied it to clinical plasma lipidomic NTA data and exposomic NTA data from water sites with varying contaminant levels. Distinguishing coordinates were observed in both studies, enabling the evaluation of phenotypic molecular annotations and resulting in screening models capable of classifying samples with up to a 25% increase in accuracy compared to models using annotated data.

Exploration of the shared diagnostic genes and mechanisms between periodontitis and primary Sjögren’s syndrome by integrated comprehensive bioinformatics analysis and machine learning

BackgroundAccumulating evidence has showed a bidirectional link between periodontitis (PD) and primary Sjögren’s syndrome (pSS), but the mechanisms of their occurrence remain unclear. Hence, this study aimed to investigate the shared diagnostic genes and potential mechanisms between PD and pSS using bioinformatics methods. MethodsGene expression data for PD and pSS were acquired from the Gene Expression Omnibus (GEO) database. Differential expression genes (DEGs) analysis and weighted gene co-expression network analysis (WGCNA) were utilized to search common genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted to explore biological functions. Three machine learning algorithms (least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF)) were used to further identify shared diagnostic genes, and these genes were assessed via receiver operating characteristic (ROC) curves in discovery and validation datasets. CIBERSORT was employed for immune cell infiltration analysis. Transcription factors (TFs)-genes and miRNAs-genes regulatory networks were conducted by NetworkAnalyst. Finally, relevant drug targets were predicted by DSigDB. ResultsBased on DEGs, 173 overlapping genes were obtained and primarily enriched in immune- and inflammation-related pathways. WGCNA revealed 34 common disease-related genes, which were enriched in similar biological pathways. Intersecting the DEGs with WGCNA results yielded 22 candidate genes. Moreover, three machine learning algorithms identified three shared genes (CSF2RB, CXCR4, and LYN) between PD and pSS, and these genes demonstrated good diagnostic performance (AUC>0.85) in both discovery and validation datasets. The immune cell infiltration analysis showed significant dysregulation in several immune cell populations. Regulatory network analysis highlighted that WRNIP1 and has-mir-155-5p might be pivotal co-regulators of the three shared gene expressions. Finally, the top 10 potential gene-targeted drugs were screened. ConclusionCSF2RB, CXCR4, and LYN may serve as potential biomarkers for the concurrent diagnosis of PD and pSS. Additionally, we identified common molecular mechanisms, TFs, miRNAs, and candidate drugs between PD and pSS, which may provide novel insights and targets for future research on the pathogenesis, diagnosis, and therapy of both diseases.

An Ensemble Spectral Prediction (ESP) model for metabolite annotation.

A key challenge in metabolomics is annotating measured spectra from a biological sample with chemical identities. Currently, only a small fraction of measurements can be assigned identities. Two complementary computational approaches have emerged to address the annotation problem: mapping candidate molecules to spectra, and mapping query spectra to molecular candidates. In essence, the candidate molecule with the spectrum that best explains the query spectrum is recommended as the target molecule. Despite candidate ranking being fundamental in both approaches, limited prior works incorporated rank learning tasks in determining the target molecule. We propose a novel machine learning model, Ensemble Spectral Prediction (ESP), for metabolite annotation. ESP takes advantage of prior neural network-based annotation models that utilize multilayer perceptron (MLP) networks and Graph Neural Networks (GNNs). Based on the ranking results of the MLP- and GNN-based models, ESP learns a weighting for the outputs of MLP and GNN spectral predictors to generate a spectral prediction for a query molecule. Importantly, training data is stratified by molecular formula to provide candidate sets during model training. Further, baseline MLP and GNN models are enhanced by considering peak dependencies through label mixing and multi-tasking on spectral topic distributions. When trained on the NIST 2020 dataset and evaluated on the relevant candidate sets from PubChem, ESP improves average rank by 23.7% and 37.2% over the MLP and GNN baselines, respectively, demonstrating performance gain over state-of-the-art neural network approaches. However, MLP approaches remain strong contenders when considering top five ranks. Importantly, we show that annotation performance is dependent on the training dataset, the number of molecules in the candidate set and candidate similarity to the target molecule. The ESP code, a trained model, and a Jupyter notebook that guide users on using the ESP tool is available at https://github.com/HassounLab/ESP.

Environmental genome-wide association studies across precipitation regimes reveal that the E3 ubiquitin ligase MBR1 regulates plant adaptation to rainy environments

In an era characterized by rapidly changing and less-predictable weather conditions fueled by the climate crisis, understanding the mechanisms underlying local adaptation in plants is of paramount importance for the conservation of species. As the frequency and intensity of extreme precipitation events increase, so are the flooding events resulting from soil water saturation. The subsequent onset of hypoxic stress is one of the leading causes of crop damage and yield loss. By combining genomics and remote sensing data, it is now possible to probe natural plant populations that have evolved in different rainfall regimes and look for molecular adaptation to hypoxia. Here, using an environmental genome-wide association study (eGWAS) of 934 non-redundant georeferenced Arabidopsis ecotypes, we have identified functional variants of the gene MED25 BINDING RING-H2 PROTEIN 1 (MBR1). This gene encodes a ubiquitin-protein ligase that regulates MEDIATOR25 (MED25), part of a multiprotein complex that interacts with transcription factors that act as key drivers of the hypoxic response in Arabidopsis, namely the RELATED TO AP2 proteins RAP2.2 and RAP2.12. Through experimental validation, we show that natural variants of MBR1 have different effects on the stability of MED25 and, in turn, on hypoxia tolerance. This study also highlights the pivotal role of the MBR1/MED25 module in establishing a comprehensive hypoxic response. Our findings show that molecular candidates for plant environmental adaptation can be effectively mined from large datasets. This thus supports the need for integration of forward and reverse genetics with robust molecular physiology validation of outcomes.

A Female-Biased Chemosensory Protein PxutCSP19 in the Antennae of Papilio xuthus Tuned to Host Volatiles and Insecticides.

Chemosensory protein (CSP) genes significantly enriched in the female antennae are potential molecular candidates for mediating female oviposition behaviors. In this study, we presented the interaction mechanisms of a female-antenna-biased PxutCSP19 in Papilio xuthus to 47 host volatiles, four biopesticides and 24 synthetic insecticides. Using a bioinformatics-based homology search, 22 genes orthologous to PxutCSP19 were identified from 22 other Papilio butterflies with high sequence identities to each other (73.20~98.72%). Multiple alignment analyses revealed a particularly extended N-terminus of Papilio CSP19s (an average of 154 residues) compared to insects' typical CSPs (approximately 120 residues). The expression profiles indicated that PxutCSP19 was significantly enriched in the female antennae, with a 31.81-fold difference relative to the male antennae. In ligand-binding assays, PxutCSP19 could strongly bind six host odorants with high affinities, ranging from dissociation constant (Ki) values of 20.44 ± 0.64 μM to 22.71 ± 0.73 μM. Notably, this protein was tuned to a monoterpenoid alcohol, linalool, which generally existed in the Rutaceae plants and elicited electrophysiological and behavioral activities of the swallowtail butterfly. On the other hand, PxutCSP19 was also capable of binding eight insecticides with stronger binding abilities (Ki < 12 μM) compared to host odorants. When an extended N-terminal region of PxutCSP19 was truncated into two different proteins, they did not significantly affect the binding of PxutCSP19 to ligands with high affinities, suggesting that this extended N-terminal sequences were not involved in the specificity of ligand recognition. Altogether, our study sheds light on the putative roles of PxutCSP19 enriched in the female antennae of P. xuthus in the perception of host volatiles and the sequestering of insecticides, and it complements the knowledge of butterfly CSPs in olfaction and insecticide resistance.