OBJECTIVE: Understanding the mechanisms of hip disease, such as osteoarthritis (OA), is crucial to advance their treatment. Such hip diseases often involve specific morphological changes. Genetic variations, called single nucleotide polymorphisms (SNPs), influence various hip morphological parameters. This study investigated the biological relevance of SNPs correlated to hip morphology in genome-wide association studies (GWAS). The SNP-associated genes were compared to genes associated with OA in other joints, aiming to see if the same genes play a role in both hip development and the risk of OA in other lower limb joints.
METHODS: A systematic literature review was conducted to identify SNPs correlated with hip morphology, based on the Population, Intervention, Comparison, Outcome, and Study (PICOS) framework. Afterwards, Gene Ontology (GO) analysis was performed, using EnrichR, on the SNP-associated genes and compared with non-hip OA-associated genes, across different databases.
RESULTS: Reviewing 49 GWAS identified 436 SNPs associated with hip joint morphology, encompassing variance in bone size, structure and shape. Among the SNP-associated genes, SOX9 plays a pivotal role in size, GDF5 impacts bone structure, and BMP7 affects shape. Overall, skeletal system development, regulation of cell differentiation, and chondrocyte differentiation emerged as crucial processes influencing hip morphology. Eighteen percent of GWAS-identified genes related to hip morphology were also associated with non-hip OA.
CONCLUSIONS: Our findings indicate the existence of multiple shared genetic mechanisms across hip morphology and OA, highlighting the necessity for more extensive research in this area, as in contrast to the hip, the genetic background on knee or foot morphology remains largely understudied.

Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer\'s disease. Knowing where a target protein resides within a cell will give insights into tailored drug design for a disease. As the gold validation standard, the conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, and fluorescent biomarker tags for protein subcellular location identification. However, the booming era of proteomics and high-throughput sequencing generates tons of newly discovered proteins, making protein subcellular localization by wet-lab experiments a mission impossible. To tackle this concern, in the past decades, artificial intelligence (AI) and machine learning (ML), especially deep learning methods, have made significant progress in this research area. In this article, we review the latest advances in AI-based method development in three typical types of approaches, including sequence-based, knowledge-based, and image-based methods. We also elaborately discuss existing challenges and future directions in AI-based method development in this research field.

Cellular homeostasis of the micronutrient iron is highly regulated in plants and responsive to nutrition, stress, and developmental signals. Genes for iron management encode metal and other transporters, enzymes synthesizing chelators and reducing substances, transcription factors, and several types of regulators. In transcriptome or proteome datasets, such iron homeostasis-related genes are frequently found to be differentially regulated. A common method to detect whether a specific cellular pathway is affected in the transcriptome data set is to perform Gene Ontology (GO) enrichment analysis. Hence, the GO database is a widely used resource for annotating genes and identifying enriched biological pathways in Arabidopsis thaliana. However, iron homeostasis-related GO terms do not consistently reflect gene associations and levels of evidence in iron homeostasis. Some genes in the existing iron homeostasis GO terms lack direct evidence of involvement in iron homeostasis. In other aspects, the existing GO terms for iron homeostasis are incomplete and do not reflect the known biological functions associated with iron homeostasis. This can lead to potential errors in the automatic annotation and interpretation of GO term enrichment analyses. We suggest that applicable evidence codes be used to add missing genes and their respective ortholog/paralog groups to make the iron homeostasis-related GO terms more complete and reliable. There is a high likelihood of finding new iron homeostasis-relevant members in gene groups and families like the ZIP, ZIF, ZIFL, MTP, OPT, MATE, ABCG, PDR, HMA, and HMP. Hence, we compiled comprehensive lists of genes involved in iron homeostasis that can be used for custom enrichment analysis in transcriptomic or proteomic studies, including genes with direct experimental evidence, those regulated by central transcription factors, and missing members of small gene families or ortholog/paralog groups. As we provide gene annotation and literature alongside, the gene lists can serve multiple computational approaches. In summary, these gene lists provide a valuable resource for researchers studying iron homeostasis in A. thaliana, while they also emphasize the importance of improving the accuracy and comprehensiveness of the Gene Ontology.

Cervical cancer is the leading cause of cancer-related death among women in developing countries. However, no comprehensive molecular mechanism for cervical cancer has been established, as many studies were small-cohort studies conducted with small sample sizes. A thorough literature search was performed using the PubMed, Scopus, EBSCOhost, and Science Direct databases. Medical Subject Heading (MeSH) terms such as \"Uterine Cervical Neoplasms\" and \"gene expression\" were used as the keywords in all fields. A total of 4027 studies were retrieved, and only clinical studies, which used the microarray method to identify differentially expressed genes (DEGs) in the cervical tissue of cervical cancer patients, were selected. Following the screening, 6 studies were selected and 1128 DEGs were extracted from the data. Sixty-two differentially expressed genes from at least two studies were selected for further analysis by DAVID, STRING, and Cytoscape software. In cervical cancer pathogenesis, three significant clusters with high intermolecular interactions from the Protein-Protein Interaction (PPI) network complex revealed three major molecular mechanisms, including cell signaling, cell cycle, and cell differentiation. Subsequently, eight genes were chosen as the candidate genes based on their involvement in the relevant gene ontology (GO) and their interaction with other genes in the PPI network through undirected first neighbor nodes. The present systematic review improves our understanding of the molecular mechanism of cervical cancer and the proposed genes that can be used to expand the biomarker panel in the screening for cervical cancer. The targeted genes may be beneficial for the development of better treatment strategies.

Major depressive disorder (MDD) is a neuropsychiatric disorder, which remains challenging to diagnose and manage due to its complex endophenotype. In this aspect, circulatory microRNAs (cimiRNAs) offer great potential as biomarkers and may provide new insights for MDD diagnosis. Therefore, we systemically reviewed the literature to explore various cimiRNAs contributing to MDD diagnosis and underlying molecular pathways. A comprehensive literature survey was conducted, employing four databases from 2012 to January 2021. Out of 1004 records, 157 reports were accessed for eligibility criteria, and 32 reports meeting our inclusion criteria were considered for in-silico analysis. This study identified 99 dysregulated cimiRNAs in MDD patients, out of which 20 cimiRNAs found in multiple reports were selected for in-silico analysis. KEGG pathway analysis indicated activation of ALS, MAPK, p53, and P13K-Akt signaling pathways, while gene ontology analysis demonstrated that most protein targets were associated with transcription. In addition, chromosomal location analysis showed clustering of dysregulated cimiRNAs at proximity 3p22-p21, 9q22.32, and 17q11.2, proposing their coregulation with specific transcription factors primarily involved in MDD physiology. Further analysis of transcription factor sites revealed the existence of HIF-1, REST, and TAL1 in most cimiRNAs. These transcription factors are proposed to target genes linked with MDD, hypothesizing that first-wave cimiRNA dysregulation may trigger the second wave of transcription-wide changes, altering the protein expressions of MDD-affected cells. Overall, this systematic review presented a list of dysregulated cimiRNAs in MDD, notably miR-24-3p, let 7a-5p, miR-26a-5p, miR135a, miR-425-3p, miR-132, miR-124 and miR-16-5p as the most prominent cimiRNAs. However, various constraints did not permit us to make firm conclusions on the clinical significance of these cimiRNAs, suggesting the need for more research on single blood compartment to identify the biomarker potential of consistently dysregulated cimiRNAs in MDD, as well as the therapeutic implications of these in-silico insights.

miR-149 is an miRNA with essential roles in carcinogenesis. This miRNA is encoded by the MIR149 gene on 2q37.3. The miR-149 hairpin produces miR-149-5p and miR-149-3p, which are the \"guide\" and the sister \"passenger\" strands, respectively. Deep sequencing experiments have shown higher prevalence of miR-149-5p compared with miR-149-3p. Notably, both oncogenic and tumor suppressive roles have been reported for miR-149-5p. In this review, we summarize the impact of miR-149-5p in the tumorigenesis and elaborate mechanisms of its involvement in this process in a variety of neoplastic conditions based on three lines of evidence, i.e., in vitro, in vivo and clinical settings.

CADASIL (Cerebral Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoencephalopathy) is a small vessel disease caused by mutations in NOTCH3 that lead to an odd number of cysteines in the epidermal growth factor (EGF)-like repeat domain, causing protein misfolding and aggregation. The main symptoms are migraines, psychiatric disorders, recurrent strokes, and dementia. Omic technologies allow the massive study of different molecules for understanding diseases in a non-biased manner or even for discovering targets and their possible treatments. We analyzed the progress in understanding CADASIL that has been made possible by omics sciences. For this purpose, we included studies that focused on CADASIL and used omics techniques, searching bibliographic resources, such as PubMed. We excluded studies with other phenotypes, such as migraine or leukodystrophies. A total of 18 articles were reviewed. Due to the high prevalence of NOTCH3 mutations considered pathogenic to date in genomic repositories, one can ask whether all of them produce CADASIL, different degrees of the disease, or whether they are just a risk factor for small vessel disease. Besides, proteomics and transcriptomics studies found that the molecules that are significantly altered in CADASIL are mainly related to cell adhesion, the cytoskeleton or extracellular matrix components, misfolding control, autophagia, angiogenesis, or the transforming growth factor β (TGFβ) signaling pathway. The omics studies performed on CADASIL have been useful for understanding the biological mechanisms and could be key factors for finding potential drug targets.

Essential genes are critical for the growth and survival of any organism. The machine learning approach complements the experimental methods to minimize the resources required for essentiality assays. Previous studies revealed the need to discover relevant features that significantly classify essential genes, improve on the generalizability of prediction models across organisms, and construct a robust gold standard as the class label for the train data to enhance prediction. Findings also show that a significant limitation of the machine learning approach is predicting conditionally essential genes. The essentiality status of a gene can change due to a specific condition of the organism. This review examines various methods applied to essential gene prediction task, their strengths, limitations and the factors responsible for effective computational prediction of essential genes. We discussed categories of features and how they contribute to the classification performance of essentiality prediction models. Five categories of features, namely, gene sequence, protein sequence, network topology, homology and gene ontology-based features, were generated for Caenorhabditis elegans to perform a comparative analysis of their essentiality prediction capacity. Gene ontology-based feature category outperformed other categories of features majorly due to its high correlation with the genes\' biological functions. However, the topology feature category provided the highest discriminatory power making it more suitable for essentiality prediction. The major limiting factor of machine learning to predict essential genes conditionality is the unavailability of labeled data for interest conditions that can train a classifier. Therefore, cooperative machine learning could further exploit models that can perform well in conditional essentiality predictions.
Identification of essential genes is imperative because it provides an understanding of the core structure and function, accelerating drug targets\' discovery, among other functions. Recent studies have applied machine learning to complement the experimental identification of essential genes. However, several factors are limiting the performance of machine learning approaches. This review aims to present the standard procedure and resources available for predicting essential genes in organisms, and also highlight the factors responsible for the current limitation in using machine learning for conditional gene essentiality prediction. The choice of features and ML technique was identified as an important factor to predict essential genes effectively.

Wound healing is an elaborated process, well-regulated via cell migration and proliferation. Although the physiological basics of wound healing have been thoroughly investigated and reported, much remain to be studied. Particularly, various studies have demonstrated the immunomodulatory roles of exosomes derived from plant cells, mammalian cells and mesenchymal stem cells (MSCs) in the healing and repair system. The paracrine and therapeutic effects of exosomes are mainly associated with the broad exosomal cargo content comprising of growth factors, cytokines, enzymes, nucleic acids, proteins and lipid signaling molecules. Nevertheless, functional or mechanism pathway of exosomes with reference to overall exosomal cargo remains undetermined. To date, combinatorial analysis strategies employing Database for Annotation, Visualization, and Integrated Discovery (DAVID), STRING tools, Gene Ontology (GO), Kyoto Encyclopedia of Genes, Genomes (KEGG) pathway enrichment analysis, as well as Ingenuity Pathway Analysis (IPA) have been applied in elucidating network interaction and functional pathway of exosomes. In this review paper, application of combinatorial analysis strategies is demonstrated to better understand on the therapeutic potentials of exosomes in wound healing process. In conclusion, functional modulation of exosomal cargo for specify biological treatment is achievable, modelling of combinatorial analysis strategies will hopefully bridge the research gap and provides a paradigm shift to regenerative processes.

Alzheimer\'s disease (AD) is a complex neurodegenerative disease, affecting a significant part of the population. The majority of AD cases occur in the elderly with a typical age of onset of the disease above 65 years. AD presents a major burden for the healthcare system and since population is rapidly aging, the burden of the disease will increase in the future. However, no effective drug treatment for a full-blown disease has been developed to date. The genetic background of AD is extensively studied; numerous genome-wide association studies (GWAS) identified significant genes associated with increased risk of AD development. This review summarizes more than 100 risk loci. Many of them may serve as biomarkers of AD progression, even in the preclinical stage of the disease. Furthermore, we used GWAS data to identify key pathways of AD pathogenesis: cellular processes, metabolic processes, biological regulation, localization, transport, regulation of cellular processes, and neurological system processes. Gene clustering into molecular pathways can provide background for identification of novel molecular targets and may support the development of tailored and personalized treatment of AD.