Functional characterization of archaic-specific variants in mitonuclear genes: insights from comparative analysis in S. cerevisiae.

Neanderthal and Denisovan hybridisation with modern humans has generated a non-random genomic distribution of introgressed regions, the result of drift and selection dynamics. Cross-species genomic incompatibility and more efficient removal of slightly deleterious archaic variants have been proposed as selection-based processes involved in the post-hybridisation purge of archaic introgressed regions. Both scenarios require the presence of functionally different alleles across Homo species onto which selection operated differently according to which populations hosted them, but only a few of these variants have been pinpointed so far. In order to identify functionally divergent archaic variants removed in humans, we focused on mitonuclear genes, which are underrepresented in the genomic landscape of archaic humans. We searched for non-synonymous, fixed, archaic-derived variants present in mitonuclear genes, rare or absent in human populations. We then compared the functional impact of archaic and human variants in the model organism Saccharomyces cerevisiae. Notably, a variant within the mitochondrial tyrosyl-tRNA synthetase 2 (YARS2) gene exhibited a significant decrease in respiratory activity and a substantial reduction of Cox2 levels, a proxy for mitochondrial protein biosynthesis, coupled with the accumulation of the YARS2 protein precursor and a lower amount of mature enzyme. Our work suggests that this variant is associated with mitochondrial functionality impairment, thus contributing to the purging of archaic introgression in YARS2. While different molecular mechanisms may have impacted other mitonuclear genes, our approach can be extended to the functional screening of mitonuclear genetic variants present across species and populations.

MUSE-XAE: MUtational Signature Extraction with eXplainable AutoEncoder enhances tumour types classification.

MOTIVATION: Mutational signatures are a critical component in deciphering the genetic alterations that underlie cancer development and have become a valuable resource to understand the genomic changes during tumorigenesis. Therefore, it is essential to employ precise and accurate methods for their extraction to ensure that the underlying patterns are reliably identified and can be effectively utilized in new strategies for diagnosis, prognosis, and treatment of cancer patients. RESULTS: We present MUSE-XAE, a novel method for mutational signature extraction from cancer genomes using an explainable autoencoder. Our approach employs a hybrid architecture consisting of a nonlinear encoder that can capture nonlinear interactions among features, and a linear decoder which ensures the interpretability of the active signatures. We evaluated and compared MUSE-XAE with other available tools on both synthetic and real cancer datasets and demonstrated that it achieves superior performance in terms of precision and sensitivity in recovering mutational signature profiles. MUSE-XAE extracts highly discriminative mutational signature profiles by enhancing the classification of primary tumour types and subtypes in real world settings. This approach could facilitate further research in this area, with neural networks playing a critical role in advancing our understanding of cancer genomics. AVAILABILITY AND IMPLEMENTATION: MUSE-XAE software is freely available at https://github.com/compbiomed-unito/MUSE-XAE.

MiRNome Profiling of Circulating Extracellular Vesicles in Patients With Chronic Hepatitis D Undergoing Pegylated Interferon Alpha Treatment.

Micro-RNAs (miRNAs) are involved in the modulation of viral replication and host immune antiviral response. Using next-generation sequencing, we investigated the miRNome profile of circulating extracellular vesicles in 20 patients with chronic hepatitis D virus (HDV) infection undergoing pegylated interferon alpha (Peg-IFNα) treatment. Circulating miRNAs' expression was analysed according to virologic response (i.e., HDV RNA clearance maintained at least 6 months after the end of therapy). Overall, 8 patients (40%) achieved a virologic response to Peg-IFNα treatment. At baseline, 14 miRNAs were differentially expressed between responders and non-responders; after 6 months of Peg-IFNα treatment, 7 miRNAs (miR-155-5p, miR-1246, miR-423-3p, miR-760, miR-744-5p, miR-1307-3p and miR-146a-5p) were consistently de-regulated. Among de-regulated miRNAs, miR-155-5p showed an inverse correlation with HDV RNA (at baseline: rs = -0.39, p = 0.092; at 6 months: rs = -0.53, p = 0.016) and hepatitis B surface antigen (HBsAg) (at baseline: rs = -0.49, p = 0.028; at 6 months: rs-0.71, p < 0.001). At logistic regression analysis, both miR-155-5p (at baseline: OR = 4.52, p = 0.022; at 6 months: OR = 5.30, p = 0.029) and HDV RNA (at baseline: OR = 0.19, p = 0.022; at 6 months: OR = 0.38, p = 0.018) resulted significantly associated to virologic response. Considering that Peg-IFNα still has a relevant role in the treatment of patients with chronic hepatitis D infection, the assessment of EV miR-155-5p may represent an additional valuable tool for the management of HDV patients undergoing Peg-IFNα treatment.

SYNDSURV: A simple framework for survival analysis with data distributed across multiple institutions.

Data sharing among different institutions represents one of the major challenges in developing distributed machine learning approaches, especially when data is sensitive, such as in medical applications. Federated learning is a possible solution, but requires fast communications and flawless security. Here, we propose SYNDSURV (SYNthetic Distributed SURVival), an alternative approach that simplifies the current state-of-the-art paradigm by allowing different centres to generate local simulated instances from real data and then gather them into a centralised hub, where an Artificial Intelligence (AI) model can learn in a standard way. The main advantage of this procedure is that it is model-agnostic, therefore prediction models can be directly applied in distributed applications without requiring particular adaptations as the current federated approaches do. To show the validity of our approach for medical applications, we tested it on a survival analysis task, offering a viable alternative to train AI models on distributed data. While federated learning has been mainly optimised for gradient-based approaches so far, our framework works with any predictive method, proving to be a comparable way of performing distributed learning without being too demanding towards each participating institute in terms of infrastructural requirements.

Influence of Model Structures on Predictors of Protein Stability Changes from Single-Point Mutations.

Missense variation in genomes can affect protein structure stability and, in turn, the cell physiology behavior. Predicting the impact of those variations is relevant, and the best-performing computational tools exploit the protein structure information. However, most of the current protein sequence variants are unresolved, and comparative or ab initio tools can provide a structure. Here, we evaluate the impact of model structures, compared to experimental structures, on the predictors of protein stability changes upon single-point mutations, where no significant changes are expected between the original and the mutated structures. We show that there are substantial differences among the computational tools. Methods that rely on coarse-grained representation are less sensitive to the underlying protein structures. In contrast, tools that exploit more detailed molecular representations are sensible to structures generated from comparative modeling, even on single-residue substitutions.