A conserved expression signature predicts growth rate and reveals cell & lineage-specific differences.
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ABSTRACT:
SUBMITTER: Jiang Z
PROVIDER: S-EPMC8610284 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundType 1 diabetes is a complex heterogenous autoimmune disease without therapeutic interventions available to prevent or reverse the disease. This study aimed to identify transcriptional changes associated with the disease progression in patients with recent-onset type 1 diabetes.MethodsWhole-blood samples were collected as part of the INNODIA study at baseline and 12 months after diagnosis of type 1 diabetes. We used linear mixed-effects modelling on RNA-seq data to identify genes associated with age, sex, or disease progression. Cell-type proportions were estimated from the RNA-seq data using computational deconvolution. Associations to clinical variables were estimated using Pearson's or point-biserial correlation for continuous and dichotomous variables, respectively, using only complete pairs of observations.FindingsWe found that genes and pathways related to innate immunity were downregulated during the first year after diagnosis. Significant associations of the gene expression changes were found with ZnT8A autoantibody positivity. Rate of change in the expression of 16 genes between baseline and 12 months was found to predict the decline in C-peptide at 24 months. Interestingly and consistent with earlier reports, increased B cell levels and decreased neutrophil levels were associated with the rapid progression.InterpretationThere is considerable individual variation in the rate of progression from appearance of type 1 diabetes-specific autoantibodies to clinical disease. Patient stratification and prediction of disease progression can help in developing more personalised therapeutic strategies for different disease endotypes.FundingA full list of funding bodies can be found under Acknowledgments.
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Project description:Recent studies have identified many genes with rare de novo mutations in autism, but a limited number of these have been conclusively established as disease-susceptibility genes due to the lack of recurrence and confounding background mutations. Such extreme genetic heterogeneity severely limits recurrence-based statistical power even in studies with a large sample size. Here, we use cell-type specific expression profiles to differentiate mutations in autism patients from those in unaffected siblings. We report a gene expression signature in different neuronal cell types shared by genes with likely gene-disrupting (LGD) mutations in autism cases. This signature reflects haploinsufficiency of risk genes enriched in transcriptional and post-transcriptional regulators, with the strongest positive associations with specific types of neurons in different brain regions, including cortical neurons, cerebellar granule cells, and striatal medium spiny neurons. When used to prioritize genes with a single LGD mutation in cases, a D-score derived from the signature achieved a precision of 40% as compared with the 15% baseline with a minimal loss in sensitivity. An ensemble model combining D-score with mutation intolerance metrics from Exome Aggregation Consortium further improved the precision to 60%, resulting in 117 high-priority candidates. These prioritized lists can facilitate identification of additional autism-susceptibility genes.
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Project description:Studies have shown that tumor microenvironment (TME) might affect drug sensitivity and the classification of colorectal cancer (CRC). Using TME-specific gene signature to identify CRC subtypes with distinctive clinical relevance has not yet been tested. A total of 18 "bulk" RNA-seq datasets (total n = 2269) and four single-cell RNA-seq datasets were included in this study. We constructed a "Signature associated with FOLFIRI resistant and Microenvironment" (SFM) that could discriminate both TME and drug sensitivity. Further, SFM subtypes were identified using K-means clustering and verified in three independent cohorts. Nearest template prediction algorithm was used to predict drug response. TME estimation was performed by CIBERSORT and microenvironment cell populations-counter (MCP-counter) methods. We identified six SFM subtypes based on SFM signature that discriminated both TME and drug sensitivity. The SFM subtypes were associated with distinct clinicopathological, molecular and phenotypic characteristics, specific enrichments of gene signatures, signaling pathways, prognosis, gut microbiome patterns, and tumor lymphocytes infiltration. Among them, SFM-C and -F were immune suppressive. SFM-F had higher stromal fraction with epithelial-to-mesenchymal transition phenotype, while SFM-C was characterized as microsatellite instability phenotype which was responsive to immunotherapy. SFM-D, -E, and -F were sensitive to FOLFIRI and FOLFOX, while SFM-A, -B, and -C were responsive to EGFR inhibitors. Finally, SFM subtypes had strong prognostic value in which SFM-E and -F had worse survival than other subtypes. SFM subtypes enable the stratification of CRC with potential chemotherapy response thereby providing more precise therapeutic options for these patients.
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Project description:Recent studies have shown that induced pluripotent stem cells (iPSCs) retain a memory of their origin and exhibit biased differentiation potential. This finding reveals a severe limitation in the application of iPSCs to cell-based therapy because it means that certain cell types are not available for reprogramming for patients. Here we show that the iPSC differentiation process is accompanied by profound gene expression and epigenetic modifications that reflect cells' origins. Under typical conditions for mammary differentiation, iPSCs reprogrammed from tail-tip fibroblasts (TF-iPSCs) activated a fibroblast-specific signature that was not compatible with mammary differentiation. Strikingly, under optimized conditions, including coculture with iPSCs derived from the mammary epithelium or in the presence of pregnancy hormones, the fibroblast-specific signature of TF-iPSCs obtained during differentiation was erased and cells displayed a mammary-specific signature with a markedly enhanced ability for mammary differentiation. These findings provide new insights into the precise control of differentiation conditions that may have applications in personalized cell-based therapy.
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Project description:Causes of secondary sexual dimorphism (SSD) in dioecious plants are very poorly understood, especially in woody plants. SSD is shown mainly in mature plants, but little is known about whether secondary sexual dimorphism can occur in juveniles. It is also assumed that stress conditions intensify differences between the sexes due to the uneven reproductive effort. Therefore, the following research hypotheses were tested: (1) secondary sexual dimorphism will be visible in juveniles; (2) unfavourable soil conditions are the cause of more pronounced differences between the sexes. Rooted shoots of the common yew (Taxus baccata L.) and common juniper (Juniperus communis L.), previously harvested from parental individuals of known sex were used in the study. During two growing seasons vegetation periods and four times a year, comprehensive morphological features of whole plants were measured. Some SSD traits were visible in the analysed juveniles. Contrary to expectations, differences were more pronounced in the fertilized treatment. Both species reacted to fertilization in different ways. Female yew had a clearly higher total plant mass, root mass, and mean root area when fertilized, whereas male juniper had a higher root mass when fertilized. Differences between the sexes independent of the fertilization treatment were seen, which can be interpreted as sexual adaptations to a continued reproduction. Female yews and male junipers made better use of fertile habitats. Our study showed that SSD may be innate, and sexual compensatory mechanisms could generate uneven growth and development of both sexes. Because the SSD pattern was rather different in both species, it was confirmed that SSD is connected with the specific life histories of specific species rather than a universal strategy of dioecious species.
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Project description:In Darwinian evolution, mutations occur approximately at random in a gene, turned into amino acid mutations by the genetic code. Some mutations are fixed to become substitutions and some are eliminated from the population. Partitioning pairs of closely related species with complete genome sequences by average population size of each pair, we looked at the substitution matrices generated for these partitions and compared the substitution patterns between species. We estimated a population genetic model that relates the relative fixation probabilities of different types of mutations to the selective pressure and population size. Parameterizations of the average and distribution of selective pressures for different amino acid substitution types in different population size comparisons were generated with a Bayesian framework. We found that partitions in population size as well as in substitution type are required to explain the substitution data. Selection coefficients were found to decrease with increasingly radical amino acid substitution and with increasing effective population size. To further explore the role of underlying processes in amino acid substitution, we analyzed embryophyte (plant) gene families from TAED (The Adaptive Evolution Database), where solved structures for at least one member exist in the Protein Data Bank. Using PAML, we assigned branches to three categories: strong negative selection, moderate negative selection/neutrality, and positive diversifying selection. Focusing on the first and third categories, we identified sites changing along gene family lineages and observed the spatial patterns of substitution. Selective sweeps were expected to create primary sequence clustering under positive diversifying selection. Co-evolution through direct physical interaction was expected to cause tertiary structural clustering. Under both positive and negative selection, the substitution patterns were found to be nonrandom. Under positive diversifying selection, significant independent signals were found for primary and tertiary sequence clustering, suggesting roles for both selective sweeps and direct physical interaction. Under strong negative selection, the signals were not found to be independent. All together, a complex interplay of population genetic and protein thermodynamics forces is suggested.
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Project description:UnlabelledHepatocellular carcinoma (HCC) is one of the most common cancers in the world. The clinical heterogeneity of HCC, and the lack of good diagnostic markers and treatment strategies, has rendered the disease a major challenge. Patients with HCC have a highly variable clinical course, indicating that HCC comprises several biologically distinctive subgroups reflecting a molecular heterogeneity of the tumors. Transforming growth factor beta (TGF-beta) is known to exhibit tumor stage dependent suppressive (that is, growth inhibition) and oncogenic (that is, invasiveness) properties. Here, we asked if a TGF-beta specific gene expression signature could refine the classification and prognostic predictions for HCC patients. Applying a comparative functional genomics approach we demonstrated that a temporal TGF-beta gene expression signature established in mouse primary hepatocytes successfully discriminated distinct subgroups of HCC. The TGF-beta positive cluster included two novel homogeneous groups of HCC associated with early and late TGF-beta signatures. Kaplan-Meier plots and log-rank statistics indicated that the patients with a late TGF-beta signature showed significantly (P < 0.005) shortened mean survival time (16.2 +/- 5.3 months) compared to the patients with an early (60.7 +/- 16.1 months) TGF-beta signature. Also, tumors expressing late TGF-beta-responsive genes displayed invasive phenotype and increased tumor recurrence. We also showed that the late TGF-beta signature accurately predicted liver metastasis and discriminated HCC cell lines by degree of invasiveness. Finally, we established that the TGF-beta gene expression signature possessed a predictive value for tumors other than HCC.ConclusionThese data demonstrate the clinical significance of the genes embedded in TGF-beta expression signature for the molecular classification of HCC.
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Project description:Nonmalignant human mammary epithelial cells (HMEC) seeded in laminin-rich extracellular matrix (lrECM) form polarized acini and, in doing so, transit from a disorganized proliferating state to an organized growth-arrested state. We hypothesized that the gene expression pattern of organized and growth-arrested HMECs would share similarities with breast tumors with good prognoses. Using Affymetrix HG-U133A microarrays, we analyzed the expression of 22,283 gene transcripts in 184 (finite life span) and HMT3522 S1 (immortal nonmalignant) HMECs on successive days after seeding in a lrECM assay. Both HMECs underwent growth arrest in G0-G1 and differentiated into polarized acini between days 5 and 7. We identified gene expression changes with the same temporal pattern in both lines and examined the expression of these genes in a previously published panel of microarray data for 295 breast cancer samples. We show that genes that are significantly lower in the organized, growth-arrested HMEC than in their proliferating counterparts can be used to classify breast cancer patients into poor and good prognosis groups with high accuracy. This study represents a novel unsupervised approach to identifying breast cancer markers that may be of use clinically.
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Project description:Cellular resources are limited and their relative allocation to gene expression programmes determines physiological states and global properties such as the growth rate. Quantitative studies using various growth conditions have singled out growth rate as a major physiological variable explaining relative protein abundances. Here, we used the simple eukaryote Schizosaccharomyces pombe to determine the importance of growth rate in explaining relative changes in protein and mRNA levels during growth on a series of non-limiting nitrogen sources. Although half of fission yeast genes were significantly correlated with the growth rate, this came alongside widespread nutrient-specific regulation. Proteome and transcriptome often showed coordinated regulation but with notable exceptions, such as metabolic enzymes. Genes positively correlated with growth rate participated in every level of protein production with the notable exception of RNA polymerase II, whereas those negatively correlated mainly belonged to the environmental stress response programme. Critically, metabolic enzymes, which represent ~55-70% of the proteome by mass, showed mainly condition-specific regulation. Specifically, many enzymes involved in glycolysis and NAD-dependent metabolism as well as the fermentative and respiratory pathways were condition-dependent and not consistently correlated with growth. In summary, we provide a rich account of resource allocation to gene expression in a simple eukaryote, advancing our basic understanding of the interplay between growth-rate dependent and nutrient-specific gene expression.
2022-02-24 | PXD027835 | Pride