Mitochondrial-nuclear epistasis underlying phenotypic variation in breast cancer pathology

Mitochondria: a new intervention target for tumor invasion and metastasis

Mitochondria, responsible for cellular energy synthesis and signal transduction, intricately regulate diverse metabolic processes, mediating fundamental biological phenomena such as cell growth, aging, and apoptosis. Tumor invasion and metastasis, key characteristics of malignancies, significantly impact patient prognosis. Tumor cells frequently exhibit metabolic abnormalities in mitochondria, including alterations in metabolic dynamics and changes in the expression of relevant metabolic genes and associated signal transduction pathways. Recent investigations unveil further insights into mitochondrial metabolic abnormalities, revealing their active involvement in tumor cell proliferation, resistance to chemotherapy, and a crucial role in tumor cell invasion and metastasis. This paper comprehensively outlines the latest research advancements in mitochondrial structure and metabolic function. Emphasis is placed on summarizing the role of mitochondrial metabolic abnormalities in tumor invasion and metastasis, including alterations in the mitochondrial genome (mutations), activation of mitochondrial-to-nuclear signaling, and dynamics within the mitochondria, all intricately linked to the processes of tumor invasion and metastasis. In conclusion, the paper discusses unresolved scientific questions in this field, aiming to provide a theoretical foundation and novel perspectives for developing innovative strategies targeting tumor invasion and metastasis based on mitochondrial biology.

Mitonuclear epistasis involving TP63 and haplogroup Uk: Risk of rapid progression of knee OA in patients from the OAI

OBJECTIVE

To investigate genetic interactions between mitochondrial deoxyribonucleic acid (mtDNA) haplogroups and nuclear single nucleotide polymorphisms (nSNPs) to analyze their impact on the development of the rapid progression of knee osteoarthritis (OA).

DESIGN

A total of 1095 subjects from the Osteoarthritis Initiative, with a follow-up time of at least 48-months, were included. Appropriate statistical approaches were performed, including generalized estimating equations adjusting for age, gender, body mass index, contralateral knee OA, Western Ontario and McMaster Universities Osteoarthritis Index pain, previous injury in target knee and the presence of the mtDNA variant m.16519C. Additional genomic data consisted in the genotyping of Caucasian mtDNA haplogroups and eight nSNPs previously associated with the risk of knee OA in robust genome-wide association studies.

RESULTS

The simultaneous presence of the G allele of rs12107036 at TP63 and the haplogroup Uk significantly increases the risk of a rapid progression of knee OA (odds ratio = 1.670; 95% confidence interval [CI]: 1.031-2.706; adjusted p-value = 0.027). The assessment of the population attributable fraction showed that the highest proportion of rapid progressors was under the simultaneous presence of the G allele of rs12107036 and the haplogroup Uk (23.4%) (95%CI: 7.89-38.9; p-value < 0.05). The area under the curve of the cross-validation model (0.730) was very similar to the obtained for the predictive model (0.735). A nomogram was constructed to help clinicians to perform clinical trials or epidemiologic studies.

CONCLUSIONS

This study demonstrates the existence of a mitonuclear epistasis in OA, providing new mechanisms by which nuclear and mitochondrial variation influence the susceptibility to develop different OA phenotypes.

Copy number variation as a tool for implementing pregnancy as an aging model

Copy number variations (CNV) are a major contributor to genome variability and have been linked to aging and other degradable phenotypes such as pregnancy physiology. To demonstrate how pregnancy can be used as a model of aging, we used CNVs from pregnant mice. Candidate CNVs were selected by applying case-control analysis in human centenarians compared with control groups. These CNVs were aligned with the mouse genome and their copy variation was assessed using qRT-PCR in liver and blood tissue samples from pregnant mice throughout pregnancy (baseline; first, second, and third trimester; post-partum). Eight of the ten selected CNVs demonstrated a significant decline/increase trend throughout the pregnancy followed by opposite direction soon after delivery in the liver and blood of the mouse tissues. Furthermore, significant differential expression was detected among the candidate CNVs' close vicinity genes (APA2A, LSS, RBDHF1, PLAAT1, and SCL17A2), but not in the WSCD2 gene. Establishing a genetic link between longevity and pregnancy is a significant step toward implementing the pregnancy process as a model for aging. These results in pregnant mice highlight the mechanism and similarities between pregnancy and aging. Investigating the mechanisms that cause such rejuvenation after labor could change our aging treatment paradigm.

Gene Interactions in Human Disease Studies-Evidence Is Mounting

Despite monumental advances in molecular technology to generate genome sequence data at scale, there is still a considerable proportion of heritability in most complex diseases that remains unexplained. Because many of the discoveries have been single-nucleotide variants with small to moderate effects on disease, the functional implication of many of the variants is still unknown and, thus, we have limited new drug targets and therapeutics. We, and many others, posit that one primary factor that has limited our ability to identify novel drug targets from genome-wide association studies may be due to gene interactions (epistasis), gene-environment interactions, network/pathway effects, or multiomic relationships. We propose that many of these complex models explain much of the underlying genetic architecture of complex disease. In this review, we discuss the evidence from multiple research avenues, ranging from pairs of alleles to multiomic integration studies and pharmacogenomics, that supports the need for further investigation of gene interactions (or epistasis) in genetic and genomic studies of human disease. Our goal is to catalog the mounting evidence for epistasis in genetic studies and the connections between genetic interactions and human health and disease that could enable precision medicine of the future.

Oncogenic metabolic reprogramming in breast cancer: focus on signaling pathways and mitochondrial genes

Oncogenic metabolic reprogramming impacts the abundance of key metabolites that regulate signaling and epigenetics. Metabolic vulnerability in the cancer cell is evident from the Warburg effect. The research on metabolism in the progression and survival of breast cancer (BC) is under focus. Oncogenic signal activation and loss of tumor suppressor are important regulators of tumor cell metabolism. Several intrinsic and extrinsic factors contribute to metabolic reprogramming. The molecular mechanisms underpinning metabolic reprogramming in BC are extensive and only partially defined. Various signaling pathways involved in the metabolism play a significant role in the modulation of BC. Notably, PI3K/AKT/mTOR pathway, lactate-ERK/STAT3 signaling, loss of the tumor suppressor Ras, Myc, oxidative stress, activation of the cellular hypoxic response and acidosis contribute to different metabolic reprogramming phenotypes linked to enhanced glycolysis. The alterations in mitochondrial genes have also been elaborated upon along with their functional implications. The outcome of these active research areas might contribute to the development of novel therapeutic interventions and the remodeling of known drugs.

A meta-analysis and a functional study support the influence of mtDNA variant m.16519C on the risk of rapid progression of knee osteoarthritis

OBJECTIVES

To identify mitochondrial DNA (mtDNA) genetic variants associated with the risk of rapid progression of knee osteoarthritis (OA) and to characterise their functional significance using a cellular model of transmitochondrial cybrids.

METHODS

Three prospective cohorts contributed participants. The osteoarthritis initiative (OAI) included 1095 subjects, the Cohort Hip and Cohort Knee included 373 and 326 came from the PROspective Cohort of Osteoarthritis from A Coruña. mtDNA variants were screened in an initial subset of 450 subjects from the OAI by in-depth sequencing of mtDNA. A meta-analysis of the three cohorts was performed. A model of cybrids was constructed to study the functional consequences of harbouring the risk mtDNA variant by assessing: mtDNA copy number, mitochondrial biosynthesis, mitochondrial fission and fusion, mitochondrial reactive oxygen species (ROS), oxidative stress, autophagy and a whole transcriptome analysis by RNA-sequencing.

RESULTS

mtDNA variant m.16519C is over-represented in rapid progressors (combined OR 1.546; 95% CI 1.163 to 2.054; p=0.0027). Cybrids with this variant show increased mtDNA copy number and decreased mitochondrial biosynthesis; they produce higher amounts of mitochondrial ROS, are less resistant to oxidative stress, show a lower expression of the mitochondrial fission-related gene fission mitochondrial 1 and an impairment of autophagic flux. In addition, its presence modulates the transcriptome of cybrids, especially in terms of inflammation, where interleukin 6 emerges as one of the most differentially expressed genes.

CONCLUSIONS

The presence of the mtDNA variant m.16519C increases the risk of rapid progression of knee OA. Among the most modulated biological processes associated with this variant, inflammation and negative regulation of cellular process stand out. The design of therapies based on the maintenance of mitochondrial function is recommended.

ClusterSeg: A crowd cluster pinpointed nucleus segmentation framework with cross-modality datasets

The detection and segmentation of individual cells or nuclei is often involved in image analysis across a variety of biology and biomedical applications as an indispensable prerequisite. However, the ubiquitous presence of crowd clusters with morphological variations often hinders successful instance segmentation. In this paper, nuclei cluster focused annotation strategies and frameworks are proposed to overcome this challenging practical problem. Specifically, we design a nucleus segmentation framework, namely ClusterSeg, to tackle nuclei clusters, which consists of a convolutional-transformer hybrid encoder and a 2.5-path decoder for precise predictions of nuclei instance mask, contours, and clustered-edges. Additionally, an annotation-efficient clustered-edge pointed strategy pinpoints the salient and error-prone boundaries, where a partially-supervised PS-ClusterSeg is presented using ClusterSeg as the segmentation backbone. The framework is evaluated with four privately curated image sets and two public sets with characteristic severely clustered nuclei across a variety range of image modalities, e.g., microscope, cytopathology, and histopathology images. The proposed ClusterSeg and PS-ClusterSeg are modality-independent and generalizable, and superior to current state-of-the-art approaches in multiple metrics empirically. Our collected data, the elaborate annotations to both public and private set, as well the source code, are released publicly at https://github.com/lu-yizhou/ClusterSeg.

Comprehensive Analyses of Ferroptosis-Related Alterations and Their Prognostic Significance in Glioblastoma

Background: This study was designed to explore the implications of ferroptosis-related alterations in glioblastoma patients.

Method: After obtaining the data sets CGGA325, CGGA623, TCGA-GBM, and GSE83300 online, extensive analysis and mutual verification were performed using R language-based analytic technology, followed by further immunohistochemistry staining verification utilizing clinical pathological tissues.

Results: The analysis revealed a substantial difference in the expression of ferroptosis-related genes between malignant and paracancerous samples, which was compatible with immunohistochemistry staining results from clinicopathological samples. Three distinct clustering studies were run sequentially on these data. All of the findings were consistent and had a high prediction value for glioblastoma. Then, the risk score predicting model containing 23 genes (CP, EMP1, AKR1C1, FMOD, MYBPH, IFI30, SRPX2, PDLIM1, MMP19, SPOCD1, FCGBP, NAMPT, SLC11A1, S100A10, TNC, CSMD3, ATP1A2, CUX2, GALNT9, TNFAIP6, C15orf48, WSCD2, and CBLN1) on the basis of “Ferroptosis.gene.cluster” was constructed. In the subsequent correlation analysis of clinical characteristics, tumor mutation burden, HRD, neoantigen burden and chromosomal instability, mRNAsi, TIDE, and GDSC, all the results indicated that the risk score model might have a better predictive efficiency.

Conclusion: In glioblastoma, there were a large number of abnormal ferroptosis-related alterations, which were significant for the prognosis of patients. The risk score-predicting model integrating 23 genes would have a higher predictive value.