Chronic Lung Allograft Dysfunction (CLAD) is a critical post-transplant complication that predominantly determines the long-term survival rates and quality of life of patients undergoing lung transplantation. The limited efficacy of current immunosuppressive strategies underscores our incomplete understanding of the immunological aspects of CLAD. Hence, there is an urgent need for more comprehensive and targeted research to unravel the complex interplay of immune cells in the development and progression of CLAD. This study conducts an in-depth analysis of the immune environment in CLAD. By examining the gene expression profiles of T cells, natural killer cells, B cells, macrophages, and monocytes, we have elucidated a unique immunological landscape in CLAD compared to healthy controls. We highlight the heterogeneity within the immune populations and provide a comprehensive understanding of the immune mechanisms driving CLAD. Enrichment analysis identified specific pathways that are either overactive or suppressed in CLAD, revealing potential molecular targets for therapeutic intervention. Our findings emphasize the crucial role of T cells in the pathophysiology of CLAD, coordinating the immune response and revealing an amplified immune cell network, potentially leading to maladaptive tissue responses. By integrating a comprehensive cellular and molecular portrait of the immune environment, our research not only deepens our understanding of the pathogenesis of CLAD but also lays a foundational approach for the development of targeted therapies.

Reperfusion stroke therapy is a modern treatment that involves thrombolysis and the mechanical removal of thrombus from the extracranial and/or cerebral arteries, thereby increasing penumbra reperfusion. After reperfusion therapy, 46% of patients are able to live independently 3 months after stroke onset. MicroRNAs (miRNAs) are essential regulators in the development of cerebral ischemia/reperfusion injury and the efficacy of the applied treatment. The first aim of this study was to examine the change in serum miRNA levels via next-generation sequencing (NGS) 10 days after the onset of acute stroke and reperfusion treatment. Next, the predictive values of the bioinformatics analysis of miRNA gene targets for the assessment of brain ischemic response to reperfusion treatment were explored. Human serum samples were collected from patients on days 1 and 10 after stroke onset and reperfusion treatment. The samples were subjected to NGS and then validated using qRT-PCR. Differentially expressed miRNAs (DEmiRNAs) were used for enrichment analysis. Hsa-miR-9-3p and hsa-miR-9-5p expression were downregulated on day 10 compared to reperfusion treatment on day 1 after stroke. The functional analysis of miRNA target genes revealed a strong association between the identified miRNA and stroke-related biological processes related to neuroregeneration signaling pathways. Hsa-miR-9-3p and hsa-miR-9-5p are potential candidates for the further exploration of reperfusion treatment efficacy in stroke patients.

UNASSIGNED: It has been reported that high blood pressure (HBP) and triglyceride (TG) are considered risk factors in immunoglobulin A nephropathy (IgAN). This study aimed to explore the causalities between HBP and TG, and IgAN on the basis of Mendelian randomization (MR) analysis.
UNASSIGNED: Firstly, the genome-wide association study (GWAS) summary data of IgAN (GCST90018866) and two exposure factors, TG (ukb-d-30870_raw) and HBP (ukb-a-437), were sourced from the GWAS Catalog and Integrative Epidemiology Unit (IEU) OpenGWAS databases, respectively. In this study, five methods were utilized to perform MR analysis after picking out single nucleotide polymorphisms (SNPs) as instrumental variables, including MR-Egger, weighted median, simple mode, weighted mode, and inverse variance weighted (IVW), followed by the sensitivity analysis containing the heterogeneity, horizontal pleiotropy test and leave-one-out (LOO) analysis. Finally, the enrichment analysis and interaction network construction of genes corresponding to SNPs of HBP and TG were performed.
UNASSIGNED: The univariate MR results revealed that HBP and TG regarded as risk factors were causally related to IgAN [TG: p = 0.046, odds ratio (OR) = 1.065, 95% confidence interval (CI) = 1.001-1.133; HBP: p = 7.09 × 10-7, OR = 1.970, 95% CI = 1.507-2.575] based on random-effect IVM method, of which TG had a weaker impact. The reliability of these univariate MR results was certified by the sensitivity analysis, in which there was no horizontal pleiotropy and exaggerated influence of each SNP. Furthermore, HBP was markedly causally related to IgAN (p = 0.000512) with the help of multivariate MR analysis, rather than TG (p = 0.332). Therefore, when HBP and TG occur simultaneously, HBP is a direct influencing factor on IgAN. Ultimately, a total of 208 and 153 genes separately corresponding to SNPs of TG and HBP were included in enrichment analysis, and thereinto, genes relevant to TG were mainly enriched in lipid homeostasis and cholesterol metabolism, while genes concerned with HBP played their roles in regulation of cell growth, aldosterone synthesis and secretion and so forth.
UNASSIGNED: TG and HBP as risk factors were causally connected with IgAN, of which HBP was strongly related to the onset of IgAN, providing more reliable evidence for further exploring the relationship between TG and HBP and IgAN.

BACKGROUND: Rice (Oryza sativa L.) is one of the globally important staple food crops, and yield-related traits are prerequisites for improved breeding efficiency in rice. Here, we used six different genome-wide association study (GWAS) models for 198 accessions, with 553,229 single nucleotide markers (SNPs) to identify the quantitative trait nucleotides (QTNs) and candidate genes (CGs) governing rice yield.
RESULTS: Amongst the 73 different QTNs in total, 24 were co-localized with already reported QTLs or loci in previous mapping studies. We obtained fifteen significant QTNs, pathway analysis revealed 10 potential candidates within 100kb of these QTNs that are predicted to govern plant height, days to flowering, and plot yield in rice. Based on their superior allelic information in 20 elite and 6 inferior genotypes, we found a higher percentage of superior alleles in the elite genotypes in comparison to inferior genotypes. Further, we implemented expression analysis and enrichment analysis enabling the identification of 73 candidate genes and 25 homologues of Arabidopsis, 19 of which might regulate rice yield traits. Of these candidate genes, 40 CGs were found to be enriched in 60 GO terms of the studied traits for instance, positive regulator metabolic process (GO:0010929), intracellular part (GO:0031090), and nucleic acid binding (GO:0090079). Haplotype and phenotypic variation analysis confirmed that LOC_OS09G15770, LOC_OS02G36710 and LOC_OS02G17520 are key candidates associated with rice yield.
CONCLUSIONS: Overall, we foresee that the QTNs, putative candidates elucidated in the study could summarize the polygenic regulatory networks controlling rice yield and be useful for breeding high-yielding varieties.

Obesity and diabetes are commonly associated with one another and represent a significant global health issue, with a recent surge in disease incidence. Nigella sativa, also known as black cumin, is believed to possess several health benefits, including anti-diabetic, anticancer, antioxidant, antimicrobial, and anti-obesity properties. In this study, we aimed to identify the active compounds derived from N. sativa, which can potentially inhibit key protein targets and signaling pathways associated with diabesity treatment. We employed an exhaustive in silico search, which led to the identification of 22 potential compounds. Out of these, only five hits were found to be non-toxic, including Arabic and ascorbic acids, dihydrocodeine, catechin, and kaempferol. Our analysis revealed that these hits were associated with genes such as AKT1, IL6, SRC, and EGFR. Finally, we conducted molecular docking and molecular dynamics simulations, which identified kaempferol as the best binder for AKT1 in comparison to the reference molecule. Overall, our in silico integrated pipeline provides a useful approach to identify non-toxic phytocompounds as promising drug candidates to treat diabetes and obesity.Communicated by Ramaswamy H. Sarma.

The amplitude of low-frequency fluctuations (ALFF) of resting-state functional MRI signals is a reliable neuroimaging measure of spontaneous brain activity. Inconsistent ALFF alterations have been reported in major depressive disorder (MDD) possibly due to clinical heterogeneity. This study was designed to investigate clinically sensitive and insensitive genes associated with ALFF alterations in MDD and the potential mechanisms.
Transcription-neuroimaging association analyses of case-control ALFF differences from two independent neuroimaging datasets with gene expression data from Allen Human Brain Atlas were performed to identify the two gene sets. Various enrichment analyses were conducted to characterize their preference in biological functions, cell types, temporal stages, and shared effects with other psychiatric disorders.
Compared with controls, first-episode and drug-naïve patients showed more extensive ALFF alterations than patients with varied clinical features. We identified 903 clinically sensitive genes and 633 clinically insensitive genes, and the former was enriched for genes with down-regulated expression in the cerebral cortex of MDD patients. Despite shared functions of cell communication, signaling, and transport, clinically sensitive genes were enriched for cell differentiation and development whereas clinically insensitive genes were for ion transport and synaptic signaling. Clinically sensitive genes showed enrichment for microglia and macrophage from childhood to young adulthood in contrast to clinically insensitive genes for neurons before early infancy. Clinically sensitive genes (15.2%) were less likely correlated with ALFF alterations in schizophrenia than clinically insensitive genes (66.8%), and both were not relevant to bipolar disorder and adult attention deficit and hyperactivity disorder based on a third independent neuroimaging dataset.
Present results provide novel insights into the molecular mechanisms of spontaneous brain activity changes in clinically different patients with MDD.

The aims of this study were (1) to identify genomic regions associated with a N efficiency index (NEI) and its composition traits and (2) to analyze the functional annotation of identified genomic regions. The NEI included N intake (NINT1), milk true protein N (MTPN1), milk urea N yield (MUNY1) in primiparous cattle, and N intake (NINT2+), milk true protein N (MTPN2+), and milk urea N yield (MUNY2+) in multiparous cattle (2 to 5 parities). The edited data included 1,043,171 records on 342,847 cows distributed in 1,931 herds. The pedigree consisted of 505,125 animals (17,797 males). Data of 565,049 SNPs were available for 6,998 animals included in the pedigree (5,251 females and 1,747 males). The SNP effects were estimated using a single-step genomic BLUP approach. The proportion of the total additive genetic variance explained by windows of 50 consecutive SNPs (with an average size of about 240 kb) was calculated. The top 3 genomic regions explaining the largest rate of the total additive genetic variance of the NEI and its composition traits were selected for candidate gene identification and quantitative trait loci (QTL) annotation. The selected genomic regions explained from 0.17% (MTPN2+) to 0.58% (NEI) of the total additive genetic variance. The largest explanatory genomic regions of NEI, NINT1, NINT2+, MTPN1, MTPN2+, MUNY1, and MUNY2+ were Bos taurus autosome 14 (1.52-2.09 Mb), 26 (9.24-9.66 Mb), 16 (75.41-75.51 Mb), 6 (8.73-88.92 Mb), 6 (8.73-88.92 Mb), 11 (103.26-103.41 Mb), 11 (103.26-103.41 Mb). Based on the literature, gene ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction, 16 key candidate genes were identified for NEI and its composition traits, which are mainly expressed in the milk cell, mammary, and liver tissues. The number of enriched QTL related to NEI, NINT1, NINT2+, MTPN1, and MTPN2+ were 41, 6, 4, 11, 36, 32, and 32, respectively, and most of them were related to the milk, health, and production classes. In conclusion, this study identified genomic regions associated with NEI and its composition traits, and identified key candidate genes describing the genetic mechanisms of N use efficiency-related traits. Furthermore, the NEI reflects not only its composition traits but also the interactions among them.

Radiotranscriptomics is an emerging field that aims to investigate the relationships between the radiomic features extracted from medical images and gene expression profiles that contribute in the diagnosis, treatment planning, and prognosis of cancer. This study proposes a methodological framework for the investigation of these associations with application on non-small-cell lung cancer (NSCLC). Six publicly available NSCLC datasets with transcriptomics data were used to derive and validate a transcriptomic signature for its ability to differentiate between cancer and non-malignant lung tissue. A publicly available dataset of 24 NSCLC-diagnosed patients, with both transcriptomic and imaging data, was used for the joint radiotranscriptomic analysis. For each patient, 749 Computed Tomography (CT) radiomic features were extracted and the corresponding transcriptomics data were provided through DNA microarrays. The radiomic features were clustered using the iterative K-means algorithm resulting in 77 homogeneous clusters, represented by meta-radiomic features. The most significant differentially expressed genes (DEGs) were selected by performing Significance Analysis of Microarrays (SAM) and 2-fold change. The interactions among the CT imaging features and the selected DEGs were investigated using SAM and a Spearman rank correlation test with a False Discovery Rate (FDR) of 5%, leading to the extraction of 73 DEGs significantly correlated with radiomic features. These genes were used to produce predictive models of the meta-radiomics features, defined as p-metaomics features, by performing Lasso regression. Of the 77 meta-radiomic features, 51 can be modeled in terms of the transcriptomic signature. These significant radiotranscriptomics relationships form a reliable basis to biologically justify the radiomics features extracted from anatomic imaging modalities. Thus, the biological value of these radiomic features was justified via enrichment analysis on their transcriptomics-based regression models, revealing closely associated biological processes and pathways. Overall, the proposed methodological framework provides joint radiotranscriptomics markers and models to support the connection and complementarities between the transcriptome and the phenotype in cancer, as demonstrated in the case of NSCLC.

Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (PFUSION = 5.12 × 10-6, PeMAGMA = 1.94 × 10-7 for whole blood), IL-6R (PFUSION = 8.63 × 10-7, PeMAGMA = 2.74 × 10-6 for cells EBV-transformed lymphocytes), and Fas (PFUSION = 5.21 × 10-5, PeMAGMA = 1.08 × 10-6 for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (PFUSION = 2.10 × 10-7, PeMAGMA = 3.93 × 10-8 for Liver), LAT (PFUSION = 1.53 × 10-4, PeMAGMA = 4.62 × 10-7 for Artery Aorta), and MAGI3 (PFUSION = 1.30 × 10-5, PeMAGMA = 1.73 × 10-7 for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA.

The phase angle (PhA) measured with bioelectrical impedance analysis is considered to reflect the interrelated components body cell mass and fluid distribution based on technical and physical aspects of the PhA measurement. However, the biomedical meaning of the PhA remains vague. Previous studies mainly assessed associations of the PhA with numerous diseases and health outcomes, but few connected protein markers to the PhA. To broaden our understanding of the biomedical background of the PhA, we aimed to explore a proteomics profile associated with the PhA and related biological factors.
The study sample encompassed 1484 participants (725 women and 759 men) aged 55-74 years from the population-based Cooperative Health Research in the Region of Augsburg (KORA) S4 study. Proteomics measurements were performed with a proximity extension assay. We employed boosting with stability selection to establish a set of markers that was strongly associated with the PhA from a group of 233 plasma protein markers. We integrated the selected protein markers into a network and enrichment analysis to identify gene ontology (GO) terms significantly overrepresented for the selected PhA protein markers.
Boosting with stability selection identified seven protein markers that were strongly and independently associated with the PhA: N-terminal prohormone brain natriuretic peptide (NT-proBNP), insulin-like growth factor-binding protein 2 (IGFBP2), adrenomedullin (ADM), myoglobin (MB), matrix metalloproteinase-9 (MMP9), protein-glutamine gamma-glutamyltransferase 2 (TGM2), and fractalkine (CX3CL1) [beta coefficient per 1 standard deviation increase in normalized protein expression values on a log 2 scale (95% confidence interval): -0.12 (-0.15, -0.08), -0.13 (-0.17, -0.09), -0.14 (-0.18, -0.10), 0.10 (0.07, 0.14), 0.07 (0.04, 0.10), 0.08 (0.05, 0.11), -0.06 (-0.10, -0.03), respectively]. According to the enrichment analysis, this protein profile was significantly overrepresented in the following top five GO terms: positive regulation of cell population proliferation (p-value: 1.32E-04), extracellular space (p-value: 1.34E-04), anatomical structure formation involved in morphogenesis (p-value: 2.92E-04), regulation of multicellular organismal development (p-value: 5.72E-04), and metal ion homeostasis (p-value: 8.86E-04).
Implementing a proteomics approach, we identified six new protein markers strongly associated with the PhA and confirmed that NT-proBNP is a key PhA marker. The main biological processes that were related to this PhA\'s protein profile are involved in regulating the amount and growth of cells, reinforcing, from a biomedical perspective, the current technical-based consensus of the PhA to reflect body cell mass.