Pancreatic ductal adenocarcinoma (PDAC) is associated with intratumoral microbiota changes. However, defining the normal pancreatic microbial composition remains a challenge. Herein, we tested the hypothesis that the microbial profiling of normal pancreatic tissue from healthy organ donors (HC) could help in determining the signature of microbiota in PDAC. Matched pairs of tumor and normal tissues from PDAC patients (n = 32) and normal pancreatic tissues from HC (n = 17) were analyzed by 16S rRNA gene sequencing. Dissimilarities in all the beta metrics emerged in both normal samples and tumor samples, compared to HC (Bray–Curtis dissimilarity and Jaccard distance: p = 0.002; weighted UniFrac distances: p = 0.42 and p = 0.012, respectively; unweighted UniFrac distance: p = 0.009); a trend toward a lower Faith’s phylogenetic distance was found at the tumor level vs. HC (p = 0.08). Within PDAC, a lower Faith’s phylogenetic distance (p = 0.003) and a significant unweighted UniFrac distance (p = 0.024) were observed in tumor samples vs. normal samples. We noted the presence of a decreased abundance of bacteria with potential beneficial effects (Jeotgalicoccus) and anticancer activity (Acinetobacter_guillouiae) in PDAC vs. HC; bacteria involved in immune homeostasis and suppression of tumor progression (Streptococcus_salivarius, Sphingomonas) were reduced, and those implicated in tumor initiation and development (Methylobacterium-Methylorubrum, g_Delftia) were enhanced in tumor samples vs. normal samples. Metagenomic functions involved in fatty acid synthesis were reduced in normal samples compared to HC, while peptidoglycan biosynthesis IV and L-rhamnose degradation were more abundant in tumor samples vs. normal samples. Future prospective studies on larger populations, also including patients in advanced tumor stages and considering all potential existing confounding factors, as well as further functional investigations, are needed to prove the role of microbiota-mediated pathogenicity in PDAC.

The Concordia Research Station provides a unique location for preparatory activities for future human journey to Mars, to explore microbial diversity at subzero temperatures, and monitor the dissemination of human-associated microorganisms within the pristine surrounding environment. Amplicon sequencing was leveraged to investigate the microbial diversity of surface snow samples collected monthly over a two-year period, at three distances from the Station (10, 500, and 1000 m). Even when the extracted total DNA was below the detection limit, 16S rRNA gene sequencing was successfully performed on all samples, while 18S rRNA was amplified on 19 samples out of 51. No significant relationships were observed between microbial diversity and seasonality (summer or winter) or distance from the Concordia base. This suggested that if present, the anthropogenic impact should have been below the detectable limit. While harboring low microbial diversity, the surface snow samples were characterized by heterogeneous microbiomes. Ultimately, our study corroborated the use of DNA sequencing-based techniques for revealing microbial presence in remote and hostile environments, with implications for Planetary Protection during space missions and for life-detection in astrobiology relevant targets.

APOGEE 2 is a mitochondrially-centered ensemble method designed to improve the accuracy of pathogenicity predictions for interpreting missense mitochondrial variants. Built on the joint consensus recommendations by the American College of Medical Genetics and Genomics/Association for Molecular Pathology (ACMG/AMP), APOGEE 2 features an improved machine learning method and a curated training set for enhanced performance metrics. It offers region-wise assessments of genome fragility and mechanistic analyses of specific amino acids that cause perceptible long-range effects on protein structure. With clinical and research use in mind, APOGEE 2 scores and pathogenicity probabilities are precompiled and available in MitImpact. APOGEE 2’s ability to address challenges in interpreting mitochondrial missense variants makes it an essential tool in the field of mitochondrial genetics.

The process of aging is a complex phenomenon that involves a progressive decline in physiological functions required for survival and fertility. To better understand the mechanisms underlying this process, the scientific community has utilized several tools. Among them, mitochondrial DNA has emerged as a crucial factor in biological aging, given that mitochondrial dysfunction is thought to significantly contribute to this phenomenon. Additionally, Drosophila melanogaster has proven to be a valuable model organism for studying aging due to its low cost, capacity to generate large populations, and ease of genetic manipulation and tissue dissection. Moreover, graph theory has been employed to understand the dynamic changes in gene expression patterns associated with aging and to investigate the interactions between aging and aging-related diseases. In this study, we have integrated these approaches to examine the patterns of gene co-expression in Drosophila melanogaster at various stages of development. By applying graph-theory techniques, we have identified modules of co-expressing genes, highlighting those that contain a significantly high number of mitochondrial genes. We found important mitochondrial genes involved in aging and age-related diseases in Drosophila melanogaster , including UQCR-C1, ND-B17.2, ND-20, and Pdhb. Our findings shed light on the role of mitochondrial genes in the aging process and demonstrate the utility of Drosophila melanogaster as a model organism and graph theory in aging research.

Short tandem repeat expansions in exon 1 of the HTT gene drive Huntington’s disease (HD) pathogenesis, with disease onset and progression heavily influenced by somatic mosaicism and sequence interruptions. While sequencing technologies enable repeat sizing, many computational tools lack the resolution to capture subtle interruption motifs and allele-specific somatic variation. We present STRmie-HD, an alignment-free, de novo framework for interruption-aware genotyping and quantitative profiling of somatic mosaicism at single-read resolution. The tool parses individual reads to quantify uninterrupted CAG tract length, CCG repeat content, and critical interruption variants, including Loss of Interruption (LOI) and Duplication of Interruption (DOI). Validated across Illumina, PacBio SMRT, and Oxford Nanopore platforms, STRmie-HD demonstrates high concordance with reference genotypes and superior sensitivity in identifying rare interruption patterns that conventional tools often overlook. Furthermore, it implements somatic mosaicism metrics to characterize repeat dynamics, successfully distinguishing the higher somatic expansion burden in brain tissues compared to peripheral blood. STRmie-HD offers a comprehensive and extensible solution for high-resolution molecular characterization of HTT variation, providing a robust framework for patient stratification and genetic research in HD. Graphical Graphical STRmie-HD flowchart. STRmie-HD is a comprehensive analytical framework that processes sequencing reads to analyze CAG/CCG trinucleotide repeats, interruption variants, and somatic mosaicism in the HTT gene. The workflow begins with sequencing reads (FASTA/FASTQ) that can undergo optional custom processing eq]based on the sequencing design. These reads are then fed into a regular expression-based engine (STRmie-HD) to identify CAG and CCG motifs. The identified motifs lead to the estimation of CAG/CCG alleles, visualized as distinct peaks representing different allele sizes, interruption variant assessment, and somatic mosaicism quantification. STRmie-HD produces an HTML output that wraps this information into a report.

Background/Objectives: Glioblastoma (GBM) is the most malignant primary brain tumor in humans, and it is characterized by frequent relapses and poor prognosis. GBMs contain self-renewing, tumorigenic cancer stem cells (CSCs) that contribute to tumor initiation and therapeutic resistance. It has been proposed that GBM CSCs originate from neural stem cells (NSCs) residing in the adult subventricular zone (SVZ), enclosing the driver mutations of human GBM. We have previously established a model of transient global ischemia in adult macaque monkeys, where we found that SVZ shows a transcriptional profile reminiscent of a profile typical for quiescent NSCs, uncovering novel gene markers for SVZ, such as the apelin receptor (APLNR). Methods: Here we conducted a deep whole genome transcriptomics analysis to compare the gene expression changes of the macaque monkey SVZ occurring in response to transient global cerebral ischemia with those involved in human SVZ neurogenesis (NSCs) or in GBM. Results/Conclusions: Overlap was narrow: only 15 genes were deregulated in all three datasets, and eight of them (including NES, GFAP and CHI3L1) were upregulated both after ischemia and in GBM. Shared pathway signals (Wnt, ERBB4) nominate testable candidates, but the datasets diverge globally and do not by themselves establish the ischemic macaque SVZ as a discovery platform for human GBM.