Big data analysis of human mitochondrial DNA substitution models: a regression approach

Thrifty wide-context models of B cell receptor somatic hypermutation

Somatic hypermutation (SHM) is the diversity-generating process in antibody affinity maturation. Probabilistic models of SHM are needed for analyzing rare mutations, for understanding the selective forces guiding affinity maturation, and for understanding the underlying biochemical process. High throughput data offers the potential to develop and fit models of SHM on relevant data sets. In this paper we model SHM using modern frameworks. We are motivated by recent work suggesting the importance of a wider context for SHM, however, assigning an independent rate to each k-mer leads to an exponential proliferation of parameters. Thus, using convolutions on 3-mer embeddings, we develop "thrifty" models of SHM that have fewer free parameters than a 5-mer model and yet have a significantly wider context. These offer a slight performance improvement over a 5-mer model. We also find that a per-site effect is not necessary to explain SHM patterns given nucleotide context. Also, the two current methods for fitting an SHM model - on out-of-frame sequence data and on synonymous mutations - produce significantly different results, and augmenting out-of-frame data with synonymous mutations does not aid out-of-sample performance.

Association of Inflammatory Mediators with Mitochondrial DNA Variants in Geriatric COVID-19 Patients

COVID-19 remains a serious concern for elderly individuals with underlying comorbidities. SARS-CoV-2 can target and damage mitochondria, potentially leading to mutations in mitochondrial DNA (mtDNA). This study aimed to evaluate single nucleotide substitutions in mtDNA and analyze their correlation with inflammatory biomarkers in elderly COVID-19 patients. A total of 30 COVID-19 patients and 33 older adult controls without COVID-19 (aged over 65 years) were enrolled. mtDNA was extracted from buffy coat samples and sequenced using a chip-based resequencing system (MitoChip v2.0) which detects both homoplasmic and heteroplasmic mtDNA variants (40-60% heteroplasmy) and allows the assessment of low-level heteroplasmy (<10% heteroplasmy). Serum concentrations of IL-6, IFN-α, TNF-α and IL-10 were determined in patients by a high-sensitivity immunoassay. We found a higher burden of total heteroplasmic variants in COVID-19 patients compared to controls with a selective increment in ND1 and COIII genes. Low-level heteroplasmy was significantly elevated in COVID-19 patients, especially in genes of the respiratory complex I. Both heteroplasmic variant burden and low-level heteroplasmy were associated with increased levels of IL-6, TNF-α, and IFN-α. These findings suggest that SARS-CoV-2 may induce mtDNA mutations that are related to the degree of inflammation.

Dating ancient splits in phylogenetic trees, with application to the human-Neanderthal split

BACKGROUND

We tackle the problem of estimating species TMRCAs (Time to Most Recent Common Ancestor), given a genome sequence from each species and a large known phylogenetic tree with a known structure (typically from one of the species). The number of transitions at each site from the first sequence to the other is assumed to be Poisson distributed, and only the parity of the number of transitions is observed. The detailed phylogenetic tree contains information about the transition rates in each site. We use this formulation to develop and analyze multiple estimators of the species' TMRCA. To test our methods, we use mtDNA substitution statistics from the well-established Phylotree as a baseline for data simulation such that the substitution rate per site mimics the real-world observed rates.

RESULTS

We evaluate our methods using simulated data and compare them to the Bayesian optimizing software BEAST2, showing that our proposed estimators are accurate for a wide range of TMRCAs and significantly outperform BEAST2. We then apply the proposed estimators on Neanderthal, Denisovan, and Chimpanzee mtDNA genomes to better estimate their TMRCA with modern humans and find that their TMRCA is substantially later, compared to values cited recently in the literature.

CONCLUSIONS

Our methods utilize the transition statistics from the entire known human mtDNA phylogenetic tree (Phylotree), eliminating the requirement to reconstruct a tree encompassing the specific sequences of interest. Moreover, they demonstrate notable improvement in both running speed and accuracy compared to BEAST2, particularly for earlier TMRCAs like the human-Chimpanzee split. Our results date the human - Neanderthal TMRCA to be [Formula: see text] years ago, considerably later than values cited in other recent studies.

Deleterious heteroplasmic mitochondrial mutations are associated with an increased risk of overall and cancer-specific mortality

Mitochondria carry their own circular genome and disruption of the mitochondrial genome is associated with various aging-related diseases. Unlike the nuclear genome, mitochondrial DNA (mtDNA) can be present at 1000 s to 10,000 s copies in somatic cells and variants may exist in a state of heteroplasmy, where only a fraction of the DNA molecules harbors a particular variant. We quantify mtDNA heteroplasmy in 194,871 participants in the UK Biobank and find that heteroplasmy is associated with a 1.5-fold increased risk of all-cause mortality. Additionally, we functionally characterize mtDNA single nucleotide variants (SNVs) using a constraint-based score, mitochondrial local constraint score sum (MSS) and find it associated with all-cause mortality, and with the prevalence and incidence of cancer and cancer-related mortality, particularly leukemia. These results indicate that mitochondria may have a functional role in certain cancers, and mitochondrial heteroplasmic SNVs may serve as a prognostic marker for cancer, especially for leukemia.

HaploCart: Human mtDNA haplogroup classification using a pangenomic reference graph human mtDNA haplogroup inference

Current mitochondrial DNA (mtDNA) haplogroup classification tools map reads to a single reference genome and perform inference based on the detected mutations to this reference. This approach biases haplogroup assignments towards the reference and prohibits accurate calculations of the uncertainty in assignment. We present HaploCart, a probabilistic mtDNA haplogroup classifier which uses a pangenomic reference graph framework together with principles of Bayesian inference. We demonstrate that our approach significantly outperforms available tools by being more robust to lower coverage or incomplete consensus sequences and producing phylogenetically-aware confidence scores that are unbiased towards any haplogroup. HaploCart is available both as a command-line tool and through a user-friendly web interface. The C++ program accepts as input consensus FASTA, FASTQ, or GAM files, and outputs a text file with the haplogroup assignments of the samples along with the level of confidence in the assignments. Our work considerably reduces the amount of data required to obtain a confident mitochondrial haplogroup assignment.

Statistical modeling of SARS-CoV-2 substitution processes: predicting the next variant

We build statistical models to describe the substitution process in the SARS-CoV-2 as a function of explanatory factors describing the sequence, its function, and more. These models serve two different purposes: first, to gain knowledge about the evolutionary biology of the virus; and second, to predict future mutations in the virus, in particular, non-synonymous amino acid substitutions creating new variants. We use tens of thousands of publicly available SARS-CoV-2 sequences and consider tens of thousands of candidate models. Through a careful validation process, we confirm that our chosen models are indeed able to predict new amino acid substitutions: candidates ranked high by our model are eight times more likely to occur than random amino acid changes. We also show that named variants were highly ranked by our models before their appearance, emphasizing the value of our models for identifying likely variants and potentially utilizing this knowledge in vaccine design and other aspects of the ongoing battle against COVID-19.

As the virus that causes COVID-19 continues to mutate and spread, new methods are needed to predict new potential variants. Here, the authors identify the best regression models for predicting likely mutation sites in the SARS-CoV-2 genome using a candidate set that considers sequence, gene location, and biological function.