The Impact of miR-155-5p on Myotube Differentiation: Elucidating Molecular Targets in Skeletal Muscle Disorders.

MicroRNAs are small regulatory molecules that control gene expression. An emerging property of muscle miRNAs is the cooperative regulation of transcriptional and epitranscriptional events controlling muscle phenotype. miR-155 has been related to muscular dystrophy and muscle cell atrophy. However, the function of miR-155 and its molecular targets in muscular dystrophies remain poorly understood. Through in silico and in vitro approaches, we identify distinct transcriptional profiles induced by miR-155-5p in muscle cells. The treated myotubes changed the expression of 359 genes (166 upregulated and 193 downregulated). We reanalyzed muscle transcriptomic data from dystrophin-deficient patients and detected overlap with gene expression patterns in miR-155-treated myotubes. Our analysis indicated that miR-155 regulates a set of transcripts, including Aldh1l, Nek2, Bub1b, Ramp3, Slc16a4, Plce1, Dync1i1, and Nr1h3. Enrichment analysis demonstrates 20 targets involved in metabolism, cell cycle regulation, muscle cell maintenance, and the immune system. Moreover, digital cytometry confirmed a significant increase in M2 macrophages, indicating miR-155's effects on immune response in dystrophic muscles. We highlight a critical miR-155 associated with disease-related pathways in skeletal muscle disorders.

High-throughput analysis reveals disturbances throughout the cell caused by Arabidopsis UCP1 and UCP3 double knockdown.

Three genes encoding mitochondrial uncoupling proteins (UCPs) have been described in Arabidopsis thaliana (UCP1 to UCP3). In plants, UCPs may act as an uncoupler or as an aspartate/glutamate exchanger. For instance, much of the data regarding UCP functionality were obtained for the UCP1 and UCP2 isoforms compared with UCP3. Here, to get a better understanding about the concerted action of UCP1 and UCP3 in planta, we investigated the transcriptome and metabolome profiles of ucp1 ucp3 double mutant plants during the vegetative phase. For that, 21-day-old mutant plants, which displayed the most evident phenotypic alterations compared to wild type (WT) plants, were employed. The double knockdown of UCP1 and UCP3, isoforms unequivocally present inside the mitochondria, promoted important transcriptional reprogramming with alterations in the expression of genes related to mitochondrial and chloroplast function as well as those responsive to abiotic stress, suggesting disturbances throughout the cell. The observed transcriptional changes were well integrated with the metabolomic data of ucp1 ucp3 plants. Alterations in metabolites related to primary and secondary metabolism, particularly enriched in the Alanine, Aspartate and Glutamate metabolism, were detected. These findings extend our knowledge of the underlying roles played by UCP3 in concert with UCP1 at the whole plant level.

Low muscle mass in lung cancer is associated with an inflammatory and immunosuppressive tumor microenvironment.

BACKGROUND: Computed tomographies (CT) are useful for identifying muscle loss in non-small lung cancer (NSCLC) cachectic patients. However, we lack consensus on the best cutoff point for pectoralis muscle loss. We aimed to characterize NSCLC patients based on muscularity, clinical data, and the transcriptional profile from the tumor microenvironment to build a cachexia classification model. METHODS: We used machine learning to generate a muscle loss prediction model, and the tumor's cellular and transcriptional profile was characterized in patients with low muscularity. First, we measured the pectoralis muscle area (PMA) of 211 treatment-naive NSCLC patients using CT available in The Cancer Imaging Archive. The cutoffs were established using machine learning algorithms (CART and Cutoff Finder) on PMA, clinical, and survival data. We evaluated the prediction model in a validation set (36 NSCLC). Tumor RNA-Seq (GSE103584) was used to profile the transcriptome and cellular composition based on digital cytometry. RESULTS: CART demonstrated that a lower PMA was associated with a high risk of death (HR = 1.99). Cutoff Finder selected PMA cutoffs separating low-muscularity (LM) patients based on the risk of death (P-value = 0.003; discovery set). The cutoff presented 84% of success in classifying low muscle mass. The high risk of LM patients was also found in the validation set. Tumor RNA-Seq revealed 90 upregulated secretory genes in LM that potentially interact with muscle cell receptors. The LM upregulated genes enriched inflammatory biological processes. Digital cytometry revealed that LM patients presented high proportions of cytotoxic and exhausted CD8+ T cells. CONCLUSIONS: Our prediction model identified cutoffs that distinguished patients with lower PMA and survival with an inflammatory and immunosuppressive TME enriched with inflammatory factors and CD8+ T cells.

An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is extremely aggressive, has an unfavorable prognosis, and there are no biomarkers for early detection of the disease or identification of individuals at high risk for morbidity or mortality. The cellular and molecular complexity of PDAC leads to inconsistences in clinical validations of many proteins that have been evaluated as prognostic biomarkers of the disease. The tumor secretome, a potential source of biomarkers in PDAC, plays a crucial role in cell proliferation and metastasis, as well as in resistance to treatments, which together contribute to a worse clinical outcome. The massive amount of proteomic data from pancreatic cancer that has been generated from previous studies can be integrated and explored to uncover secreted proteins relevant to the diagnosis and prognosis of the disease. The present study aimed to perform an integrated meta-analysis of PDAC proteome and secretome public data to identify potential biomarkers of the disease. Our meta-analysis combined mass spectrometry data obtained from two systematic reviews of the pancreatic cancer literature, which independently selected 20 studies of the secretome and 35 of the proteome. Next, we predicted the secreted proteins using seven in silico tools or databases, which identified 39 secreted proteins shared between the secretome and proteome data. Notably, the expression of 31 genes of these secretome-related proteins was upregulated in PDAC samples from The Cancer Genome Atlas (TCGA) when compared to control samples from TCGA and The Genotype-Tissue Expression (GTEx). The prognostic value of these 39 secreted proteins in predicting survival outcome was confirmed using gene expression data from four PDAC datasets (validation set). The gene expression of these secreted proteins was able to distinguish high- and low-survival patients in nine additional tumor types from TCGA, demonstrating that deregulation of these secreted proteins may also contribute to the prognosis in multiple cancers types. Finally, we compared the prognostic value of the identified secreted proteins in PDAC biomarkers studies from the literature. This analysis revealed that our gene signature performed equally well or better than the signatures from these previous studies. In conclusion, our integrated meta-analysis of PDAC proteome and secretome identified 39 secreted proteins as potential biomarkers, and the tumor gene expression profile of these proteins in patients with PDAC is associated with worse overall survival.

Non-mosaic partial duplication 12p in a patient with dysmorphic characteristics and developmental delay.

Duplication of the short arm of chromosome 12 is a rare chromosomal abnormality that may arise de novo or result from malsegregation of a balanced parental translocation. This study comprises the clinical description, cytogenetic and cytogenomic analyses and genotype-phenotype correlation in a patient with facial dysmorphism, developmental delay and intellectual impairment caused by non-mosaic partial duplication and a paracentric inversion 12p. The patient's GTG-banded karyotype was 46,XX,invdup(12)(pter → p13.32::p11.1 → p13.31::p13.31 → qter). A genetic gain of approximately 28 Mb was detected in the chromosomal region arr[GRCh37]12p13.31-p11.1(6914072_34756209)x3. The chromosomal alteration seen in our patient is described as "pure" partial duplication 12p. In most cases, duplication 12p phenotype is characterized by dysmorphic features, multiple congenital anomalies and intellectual disability. A small number of cases in literature have described genes associated with neurodevelopmental disease, such as ING4, CHD4, MFAP5, GRIN2B, SOX5, SCN8A and PIANP. In our patient the duplication 12p was de novo. This study should contribute to the genotype-phenotype correlation in partial duplication 12p cases.

The Pathway to Cancer Cachexia: MicroRNA-Regulated Networks in Muscle Wasting Based on Integrative Meta-Analysis.

Cancer cachexia is a multifactorial syndrome that leads to significant weight loss. Cachexia affects 50%-80% of cancer patients, depending on the tumor type, and is associated with 20%-40% of cancer patient deaths. Besides the efforts to identify molecular mechanisms of skeletal muscle atrophy-a key feature in cancer cachexia-no effective therapy for the syndrome is currently available. MicroRNAs are regulators of gene expression, with therapeutic potential in several muscle wasting disorders. We performed a meta-analysis of previously published gene expression data to reveal new potential microRNA-mRNA networks associated with muscle atrophy in cancer cachexia. We retrieved 52 differentially expressed genes in nine studies of muscle tissue from patients and rodent models of cancer cachexia. Next, we predicted microRNAs targeting these differentially expressed genes. We also include global microRNA expression data surveyed in atrophying skeletal muscles from previous studies as background information. We identified deregulated genes involved in the regulation of apoptosis, muscle hypertrophy, catabolism, and acute phase response. We further predicted new microRNA-mRNA interactions, such as miR-27a/Foxo1, miR-27a/Mef2c, miR-27b/Cxcl12, miR-27b/Mef2c, miR-140/Cxcl12, miR-199a/Cav1, and miR-199a/Junb, which may contribute to muscle wasting in cancer cachexia. Finally, we found drugs targeting MSTN, CXCL12, and CAMK2B, which may be considered for the development of novel therapeutic strategies for cancer cachexia. Our study has broadened the knowledge of microRNA-regulated networks that are likely associated with muscle atrophy in cancer cachexia, pointing to their involvement as potential targets for novel therapeutic strategies.