Entropy of mitochondrial DNA circulating in blood is associated with hepatocellular carcinoma

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

Emerging biomolecules for practical theranostics of liver hepatocellular carcinoma

Most cases of hepatocellular carcinoma (HCC) are able to be diagnosed through regular surveillance in an identifiable patient population with chronic hepatitis B or cirrhosis. Nevertheless, 50% of global cases might present incidentally owing to symptomatic advanced-stage HCC after worsening of liver dysfunction. A systematic search based on PUBMED was performed to identify relevant outcomes, covering newer surveillance modalities including secretory proteins, DNA methylation, miRNAs, and genome sequencing analysis which proposed molecular expression signatures as ideal tools in the early-stage HCC detection. In the face of low accuracy without harmonization on the analytical approaches and data interpretation for liquid biopsy, a more accurate incidence of HCC will be unveiled by using deep machine learning system and multiplex immunohistochemistry analysis. A combination of molecular-secretory biomarkers, high-definition imaging and bedside clinical indexes in a surveillance setting offers a comprehensive range of HCC potential indicators. In addition, the sequential use of numerous lines of systemic anti-HCC therapies will simultaneously benefit more patients in survival. This review provides an overview on the most recent developments in HCC theranostic platform.

Relevance of HBx for Hepatitis B Virus-Associated Pathogenesis

The hepatitis B virus (HBV) counts as a major global health problem, as it presents a significant causative factor for liver-related morbidity and mortality. The development of hepatocellular carcinomas (HCC) as a characteristic of a persistent, chronic infection could be caused, among others, by the pleiotropic function of the viral regulatory protein HBx. The latter is known to modulate an onset of cellular and viral signaling processes with emerging influence in liver pathogenesis. However, the flexible and multifunctional nature of HBx impedes the fundamental understanding of related mechanisms and the development of associated diseases, and has even led to partial controversial results in the past. Based on the cellular distribution of HBx-nuclear-, cytoplasmic- or mitochondria-associated-this review encompasses the current knowledge and previous investigations of HBx in context of cellular signaling pathways and HBV-associated pathogenesis. In addition, particular focus is set on the clinical relevance and potential novel therapeutic applications in the context of HBx.

Mitochondria dysfunction in circulating tumor cells

Circulating tumor cells (CTCs) represent a subset of heterogeneous cells, which, once released from a tumor site, have the potential to give rise to metastasis in secondary sites. Recent research focused on the attempt to detect and characterize these rare cells in the circulation, and advancements in defining their molecular profile have been reported in diverse tumor species, with potential implications for clinical applications. Of note, metabolic alterations, involving mitochondria, have been implicated in the metastatic process, as key determinants in the transition of tumor cells to a mesenchymal or stemness-like phenotype, in drug resistance, and in induction of apoptosis. This review aimed to briefly analyse the most recent knowledge relative to mitochondria dysfunction in CTCs, and to envision implications of altered mitochondria in CTCs for a potential utility in clinics.

Mutational profiling of mtDNA control region reveals tumor-specific evolutionary selection involved in mitochondrial dysfunction

Background

Mitochondrial DNA (mtDNA) mutations alter mitochondrial function in oxidative metabolism and play an important role in tumorigenesis. A series of studies have demonstrated that the mtDNA control region (mtCTR), which is essential for mtDNA replication and transcription, represents a mutational hotspot in human tumors. However, a comprehensive pan-cancer evolutionary pattern analysis of mtCTR mutations is urgently needed.

Methods

We generated a comprehensive combined dataset containing 10026 mtDNA somatic mutations from 4664 patients, covering 20 tumor types based on public and private next-generation sequencing data.

Findings

Our results demonstrated a significantly higher and much more variable mutation rate in mtCTR than in the coding region across different tumor types. Moreover, our data showed a remarkable distributional bias of tumor somatic mutations between the hypervariable segment (HVS) and non-HVS, with a significantly higher mutation density and average mutation sites in HVS. Importantly, the tumor-specific mutational pattern between mtCTR HVS and non-HVS was identified, which was classified into three evolutionary selection types (relaxed, moderate, and strict constraint types). Analysis of substitution patterns revealed that the prevalence of C_H > T_H in non-HVS greatly contributed to the mutational selection pattern of mtCTR across different tumor types. Furthermore, we found that the mutational pattern of mtCTR in the four tumor types was clearly associated with mitochondrial biogenesis, mitochondrial oxidative metabolism, and the overall survival of patients.

Interpretation

Our results suggest that somatic mutations in mtCTR may be shaped by tumor-specific selective pressure and are involved in tumorigenesis.

Fundings

National Natural Science Foundation of China [grants 82020108023, 81830070, 81872302], and Autonomous Project of State Key Laboratory of Cancer Biology, China [grants CBSKL2019ZZ06, CBSKL2019ZZ27].

imPlatelet classifier: image-converted RNA biomarker profiles enable blood-based cancer diagnostics

Liquid biopsies offer a minimally invasive sample collection, outperforming traditional biopsies employed for cancer evaluation. The widely used material is blood, which is the source of tumor‐educated platelets. Here, we developed the imPlatelet classifier, which converts RNA‐sequenced platelet data into images in which each pixel corresponds to the expression level of a certain gene. Biological knowledge from the Kyoto Encyclopedia of Genes and Genomes was also implemented to improve accuracy. Images obtained from samples can then be compared against standard images for specific cancers to determine a diagnosis. We tested imPlatelet on a cohort of 401 non‐small cell lung cancer patients, 62 sarcoma patients, and 28 ovarian cancer patients. imPlatelet provided excellent discrimination between lung cancer cases and healthy controls, with accuracy equal to 1 in the independent dataset. When discriminating between noncancer cases and sarcoma or ovarian cancer patients, accuracy equaled 0.91 or 0.95, respectively, in the independent datasets. According to our knowledge, this is the first study implementing an image‐based deep‐learning approach combined with biological knowledge to classify human samples. The performance of imPlatelet considerably exceeds previously published methods and our own alternative attempts of sample discrimination. We show that the deep‐learning image‐based classifier accurately identifies cancer, even when a limited number of samples are available.

Extracellular mitochondria in the cerebrospinal fluid (CSF): Potential types and key roles in central nervous system (CNS) physiology and pathogenesis

The cerebrospinal fluid (CSF) has an important role in the transport of nutrients and signaling molecules to the central nervous and immune systems through its circulation along the brain and spinal cord tissues. The mitochondrial activity in the central nervous system (CNS) is essential in processes such as neuroplasticity, neural differentiation and production of neurotransmitters. Interestingly, extracellular and active mitochondria have been detected in the CSF where they act as a biomarker for the outcome of pathologies such as subarachnoid hemorrhage and delayed cerebral ischemia. Additionally, cell-free-circulating mitochondrial DNA (ccf-mtDNA) has been detected in both the CSF of healthy donors and in that of patients with neurodegenerative diseases. Key questions arise as there is still much debate regarding if ccf-mtDNA detected in CSF is associated with a diversity of active or inactive extracellular mitochondria coexisting in distinct pathologies. Additionally, it is of great scientific and medical importance to identify the role of extracellular mitochondria (active and inactive) in the CSF and the difference between them being damage associated molecular patterns (DAMPs) or factors that promote homeostasis. This review analyzes the different types of extracellular mitochondria, methods for their identification and their presence in CSF. Extracellular mitochondria in the CSF could have an important implication in health and disease, which may lead to the development of medical approaches that utilize mitochondria as therapeutic agents.

NGS-based accurate and efficient detection of circulating cell-free mitochondrial DNA in cancer patients

Mitochondrial DNA (mtDNA) mutations are closely implicated in the pathogenesis of multiple cancers, making circulating cell-free mtDNA (ccf-mtDNA) as a potential non-invasive tumor biomarker. However, an effective approach to comprehensively profile ccf-mtDNA mutations is still lacking. In this study, we first characterized ccf-mtDNA by low-depth whole-genome sequencing (WGS) and found that plasma DNA samples exhibited a dramatic decrease in mtDNA copy number when compared with fresh tumor tissues. Further analysis revealed that plasma ccf-mtDNA had a biased distribution of fragment size with a peak around 90 bp. Based on these insights, we developed a robust captured-based mtDNA deep-sequencing approach that enables accurate and efficient detection of plasma ccf-mtDNA mutations by systematic optimization of probe quantity and length, hybridization temperature, and PCR amplification cycles. Moreover, we found that placement of isolated plasma for 6 h at both 4°C and room temperature (RT) led to a dramatic decrease of ccf-mtDNA stability, highlighting the importance of proper plasma sample processing. We further showed that the optimized approach can successfully detect a substantial fraction of tumor-specific mtDNA mutations in plasma ccf-mtDNA specifically from hepatocellular carcinoma (HCC) patients but not from colorectal cancer (CRC) patients, suggesting the presence of a potential cancer-specific difference in the abundance of tumor-derived mtDNA in plasma.

Convex hulls in hamming space enable efficient search for similarity and clustering of genomic sequences

Background

In molecular epidemiology, comparison of intra-host viral variants among infected persons is frequently used for tracing transmissions in human population and detecting viral infection outbreaks. Application of Ultra-Deep Sequencing (UDS) immensely increases the sensitivity of transmission detection but brings considerable computational challenges when comparing all pairs of sequences. We developed a new population comparison method based on convex hulls in hamming space. We applied this method to a large set of UDS samples obtained from unrelated cases infected with hepatitis C virus (HCV) and compared its performance with three previously published methods.

Results

The convex hull in hamming space is a data structure that provides information on: (1) average hamming distance within the set, (2) average hamming distance between two sets; (3) closeness centrality of each sequence; and (4) lower and upper bound of all the pairwise distances among the members of two sets. This filtering strategy rapidly and correctly removes 96.2% of all pairwise HCV sample comparisons, outperforming all previous methods. The convex hull distance (CHD) algorithm showed variable performance depending on sequence heterogeneity of the studied populations in real and simulated datasets, suggesting the possibility of using clustering methods to improve the performance. To address this issue, we developed a new clustering algorithm, k-hulls, that reduces heterogeneity of the convex hull. This efficient algorithm is an extension of the k-means algorithm and can be used with any type of categorical data. It is 6.8-times more accurate than k-mode, a previously developed clustering algorithm for categorical data.

Conclusions

CHD is a fast and efficient filtering strategy for massively reducing the computational burden of pairwise comparison among large samples of sequences, and thus, aiding the calculation of transmission links among infected individuals using threshold-based methods. In addition, the convex hull efficiently obtains important summary metrics for intra-host viral populations.

An innovative data analysis strategy for accurate next-generation sequencing detection of tumor mitochondrial DNA mutations

Next-generation sequencing technology has been commonly applied to detect mitochondrial DNA (mtDNA) mutations, which are reported to be strongly associated with cancers. However, several key challenges still exist regarding bioinformatics analysis of mtDNA sequencing data that greatly affect the detection accuracy of mtDNA mutations. Here we comprehensively evaluated several key analysis procedures in three different sample types. We found that a trimming procedure was essential for improving mtDNA mapping performance in plasma but not tissue samples. Mapping with a revised Cambridge reference sequence and human genome 19 reference was strongly suggested for mtDNA mutation detection in plasma samples because of the extreme abundance of nuclear DNA of mitochondrial origin. Moreover, our results showed that a setting of 3 mismatches was most appropriate for mtDNA mutation calling. Importantly, we revealed the presence of a negative logarithmic relationship between mtDNA site sequencing depth and minimum detectable mutation frequency and built an innovative and efficient filtering strategy to increase the accuracy and sensitivity of mutation detection. Finally, we verified that higher sequencing depth was required for a PCR-based compared with a capture-based enrichment strategy. We established an innovative data analysis strategy that is of great significance for improving the accuracy of mtDNA mutation detection for different types of tumor samples.