Palindromic rheumatism (PR) is a type of cryptogenic paroxysmal arthritis. Several genes may be involved in PR pathogenesis; however, conducting comprehensive case-control genetic studies for PR poses challenges owing to its rarity as a disease. Moreover, case-control studies may overlook rare variants that occur infrequently but play a significant role in pathogenesis. This study aimed to identify disease-related genes in Japanese patients with PR using whole-genome sequencing (WGS) and rare-variant analysis. Genomic DNA was obtained from two familial cases and one sporadic case, and it was subjected to WGS. WGS data of 104 healthy individuals obtained from a public database were used as controls. We performed data analysis for rare variants on detected variants using SKAT-O, KBAC, and SKAT, and subsequently defined significant genes. Significant genes combined with variants shared between the cases were defined as disease-related genes. We also performed pathway analysis for disease-related genes using Reactome. We identified 2,695,244 variants shared between cases; after excluding polymorphisms and noise, 74,640 variants were detected. We identified 540 disease-related genes, including 1,893 variants. Furthermore, we identified 32 significant pathways. Our results indicate that the detected genes and pathways in this study may be involved in PR pathogenesis.

Bacterial canker caused by Pseudomonas syringae pv. syringae (Pss) is responsible for substantial loss to the production of sweet cherry in Chile. To date, the molecular mechanisms of the Pss-sweet cherry interaction and the disease-related genes in the plant are poorly understood. In order to gain insight into these aspects, a transcriptomic analysis of the sweet cherry cultivar \'Lapins\' for differentially expressed genes (DEGs) in response to Pss inoculation was conducted. Three Pss strains, A1M3, A1M197, and 11116_b1, were inoculated in young twigs, and RNA was extracted from tissue samples at the inoculation site and distal sections. RNA sequencing and transcriptomic expression analysis revealed that the three strains induced different patterns of responses in local and distal tissues. In the local tissues, A1M3 triggered a much more extensive response than the other two strains, enriching DEGs especially involved in photosynthesis. In the distal tissues, the three strains triggered a comparable extent of responses, among which 11116_b1 induced a group of DEGs involved in defense responses. Furthermore, tissues from various inoculations exhibited an enrichment of DEGs related to carbohydrate metabolism, terpene metabolism, and cell wall biogenesis. This study opened doors to future research on the Pss-sweet cherry interaction, immunity responses, and disease control.

As eukaryotes, plants and animals have many commonalities on the genetic level, although they differ greatly in appearance and physiological habits. The primary goal of current plant research is to improve the crop yield and quality. However, plant research has a wider aim, exploiting the evolutionary conservatism similarities between plants and animals, and applying discoveries in the field of botany to promote zoological research that will ultimately serve human health, although very few studies have addressed this aspect. Here, we analyzed 35 human-disease-related gene orthologs in plants and characterized the genes in depth. Thirty-four homologous genes were found to be present in the herbaceous annual plant Arabidopsis thaliana and the woody perennial plant Populus trichocarpa, with most of the genes having more than two exons, including the ATM gene with 78 exons. More surprisingly, 27 (79.4%) of the 34 homologous genes in Arabidopsis were found to be senescence-associated genes (SAGs), further suggesting a close relationship between human diseases and cellular senescence. Protein-protein interaction network analysis revealed that the 34 genes formed two main subnetworks, and genes in the first subnetwork interacted with 15 SAGs. In conclusion, our results show that most of the 34 homologs of human-disease-associated genes in plants are involved in the leaf senescence process, suggesting that leaf senescence may offer a means to study the pathogenesis of human diseases and to screen drugs for the treat of diseases.

BACKGROUND: The pond snail, Lymnaea stagnalis (L. stagnalis), has served as a valuable model organism for neurobiology studies due to its simple and easily accessible central nervous system (CNS). L. stagnalis has been widely used to study neuronal networks and recently gained popularity for study of aging and neurodegenerative diseases. However, previous transcriptome studies of L. stagnalis CNS have been exclusively carried out on adult L. stagnalis only. As part of our ongoing effort studying L. stagnalis neuronal growth and connectivity at various developmental stages, we provide the first age-specific transcriptome analysis and gene annotation of young (3 months), adult (6 months), and old (18 months) L. stagnalis CNS.
RESULTS: Using the above three age cohorts, our study generated 55-69 millions of 150 bp paired-end RNA sequencing reads using the Illumina NovaSeq 6000 platform. Of these reads, ~ 74% were successfully mapped to the reference genome of L. stagnalis. Our reference-based transcriptome assembly predicted 42,478 gene loci, of which 37,661 genes encode coding sequences (CDS) of at least 100 codons. In addition, we provide gene annotations using Blast2GO and functional annotations using Pfam for ~ 95% of these sequences, contributing to the largest number of annotated genes in L. stagnalis CNS so far. Moreover, among 242 previously cloned L. stagnalis genes, we were able to match ~ 87% of them in our transcriptome assembly, indicating a high percentage of gene coverage. The expressional differences for innexins, FMRFamide, and molluscan insulin peptide genes were validated by real-time qPCR. Lastly, our transcriptomic analyses revealed distinct, age-specific gene clusters, differentially expressed genes, and enriched pathways in young, adult, and old CNS. More specifically, our data show significant changes in expression of critical genes involved in transcription factors, metabolisms (e.g. cytochrome P450), extracellular matrix constituent, and signaling receptor and transduction (e.g. receptors for acetylcholine, N-Methyl-D-aspartic acid, and serotonin), as well as stress- and disease-related genes in young compared to either adult or old snails.
CONCLUSIONS: Together, these datasets are the largest and most updated L. stagnalis CNS transcriptomes, which will serve as a resource for future molecular studies and functional annotation of transcripts and genes in L. stagnalis.

UNASSIGNED: Studies regarding differentially expressed genes (DEGs) in Parkinson\'s disease (PD) have focused on common upstream regulators or dysregulated pathways or ontologies; however, the relationships between DEGs and disease-related or cell type-enriched genes have not been systematically studied. Meta-analysis of DEGs (meta-DEGs) are expected to overcome the limitations, such as replication failure and small sample size of previous studies.
UNASSIGNED: Meta-DEGs were performed to investigate dysregulated genes enriched with neurodegenerative disorder causative or risk genes in a phenotype-specific manner.
UNASSIGNED: Six microarray datasets from PD patients and controls, for which substantia nigra sample transcriptome data were available, were downloaded from the NINDS data repository. Meta-DEGs were performed using two methods, combining p-values and combing effect size, and common DEGs were used for secondary analyses. Gene sets of cell type-enriched or disease-related genes for PD, Alzheimer\'s disease (AD), and hereditary progressive ataxia were constructed by curation of public databases and/or published literatures.
UNASSIGNED: Our meta-analyses revealed 449 downregulated and 137 upregulated genes. Overrepresentation analyses with cell type-enriched genes were significant in neuron-enriched genes but not in astrocyte- or microglia-enriched genes. Meta-DEGs were significantly enriched in causative genes for hereditary disorders accompanying parkinsonism but not in genes associated with AD or hereditary progressive ataxia. Enrichment of PD-related genes was highly significant in downregulated DEGs but insignificant in upregulated genes.
UNASSIGNED: Downregulated meta-DEGs were associated with PD-related genes, but not with other neurodegenerative disorder genes. These results highlight disease phenotype-specific changes in dysregulated genes in PD.

Marine habitats harbour a large variety of organisms that belong to diverse taxa; from bacteria and unicellular eukaryotes to fungi, animals, and plants. Although we have only started to understand the diversity and structure of marine communities, it is clear that numerous marine species have or might have an impact on human health. Some are a source of natural products with potential or actual medical applications, others are toxic and harmful to humans, and some are used in biomedical research to help understand the molecular basis of human diseases. New molecular genetics and genomic methods provide powerful and ever more indispensable tools for studying marine organisms and all aspects of their influence on human health. Herein, we present work using the latest research, which mostly uses genomics, to tackle the questions related with the topic of the issue.

Background: Diseases of the nervous system are widely considered to be caused by genetic mutations, and they have been shown to share pathogenic genes. Discovering the shared mechanisms of these diseases is useful for designing common treatments. Method: In this study, by reviewing 518 articles published after 2007 on 20 diseases of the nervous system, we compiled data on 1607 mutations occurring in 365 genes, totals that are 1.9 and 3.2 times larger than those collected in the Clinvar database, respectively. A combination with the Clinvar data gives 2434 pathogenic mutations and 424 genes. Using this information, we measured the genetic similarities between the diseases according to the number of genes causing two diseases simultaneously. Further detection was carried out on the similarity between diseases in terms of cell types. Disease-related cell types were defined as those with disease-related gene enrichment among the marker genes of cells, as ascertained by analyzing single-cell sequencing data. Enrichment profiles of the disease-related genes over 25 cell types were constructed. The disease similarity in terms of cell types was obtained by calculating the distances between the enrichment profiles of these genes. The same strategy was applied to measure the disease similarity in terms of brain regions by analyzing the gene expression data from 10 brain regions. Results: The disease similarity was first measured in terms of genes. The result indicated that the proportions of overlapped genes between diseases were significantly correlated to the DMN scores (phenotypic similarity), with a Pearson correlation coefficient of 0.40 and P-value = 6.0×10-3. The disease similarity analysis for cell types identified that the distances between enrichment profiles of the disease-related genes were negatively correlated to the DMN scores, with Spearman correlation coefficient = -0.26 (P-value = 1.5 × 10-2). However, the brain region enrichment profile distances of the disease-related genes were not significantly correlated with the DMN score. Besides the similarity of diseases, this study identified novel relationships between diseases and cell types. Conclusion: We manually constructed the most comprehensive dataset to date for genes with mutations related to 20 nervous system diseases. By using this dataset, the similarities between diseases in terms of genes and cell types were found to be significantly correlated to their phenotypic similarity. However, the disease similarities in terms of brain regions were not significantly correlated with the phenotypic similarities. Thus, the phenotypic similarity between the diseases is more likely to be caused by dysfunctions of the same genes or the same types of neurons rather than the same brain regions. The data are collected into the database NeurodisM, which is available at http://biomed-ai.org/neurodism.

BACKGROUND: Predicting disease-related genes is helpful for understanding the disease pathology and the molecular mechanisms during the disease progression. However, traditional methods are not suitable for screening genes related to the disease development, because there are some samples with weak label information in the disease dataset and a small number of genes are known disease-related genes.
RESULTS: We designed a disease-related gene mining method based on the weakly supervised learning model in this paper. The method is separated into two steps. Firstly, the differentially expressed genes are screened based on the weakly supervised learning model. In the model, the strong and weak label information at different stages of the disease progression is fully utilized. The obtained differentially expressed gene set is stable and complete after the algorithm converges. Then, we screen disease-related genes in the obtained differentially expressed gene set using transductive support vector machine based on the difference kernel function. The difference kernel function can map the input space of the original Huntington\'s disease gene expression dataset to the difference space. The relation between the two genes can be evaluated more accurately in the difference space and the known disease-related gene information can be used effectively.
CONCLUSIONS: The experimental results show that the disease-related gene mining method based on the weakly supervised learning model can effectively improve the precision of the disease-related gene prediction compared with other excellent methods.

Complex diseases involve many genes, and these genes are often associated with several different illnesses. Disease similarity measurement can be based on shared genotype or phenotype. Quantifying relationships between genes can reveal previously unknown connections and form a reference base for therapy development and drug repurposing.
Here we introduce a method to measure disease similarity that incorporates the uniqueness of shared genes. For each disease pair, we calculated the uniqueness score and constructed disease similarity matrices using OMIM and Disease Ontology annotation.
Using the Disease Ontology-based matrix, we identified several interesting connections between cancer and other disease and conditions such as malaria, along with studies to support our findings. We also found several high scoring pairwise relationships for which there was little or no literature support, highlighting potentially interesting connections warranting additional study.
We developed a co-occurrence matrix based on gene uniqueness to examine the relationships between diseases from OMIM and DORIF data. Our similarity matrix can be used to identify potential disease relationships and to motivate further studies investigating the causal mechanisms in diseases.