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During the last few years, DNA and RNA sequencing have started to play an increasingly important role in biological and medical applications, especially due to the greater amount of sequencing data yielded from the new sequencing machines and the enormous decrease in sequencing costs. Particularly, Illumina/Solexa sequencing has had an increasing impact on gathering data from model and non-model organisms. However, accurate and easy to use tools for quality filtering have not yet been established. We present ConDeTri, a method for content dependent read trimming for next generation sequencing data using quality scores of each individual base. The main focus of the method is to remove sequencing errors from reads so that sequencing reads can be standardized. Another aspect of the method is to incorporate read trimming in next-generation sequencing data processing and analysis pipelines. It can process single-end and paired-end sequence data of arbitrary length and it is independent from sequencing coverage and user interaction. ConDeTri is able to trim and remove reads with low quality scores to save computational time and memory usage during de novo assemblies. Low coverage or large genome sequencing projects will especially gain from trimming reads. The method can easily be incorporated into preprocessing and analysis pipelines for Illumina data. Freely available on the web at http://code.google.com/p/condetri.

Despite the availability of batch effect correcting algorithms (BECA), no comprehensive tool that combines batch correction and evaluation of the results exists for microbiome datasets. This work outlines the Microbiome Batch Effects Correction Suite development that integrates several BECAs and evaluation metrics into a software package for the statistical computation framework R.

Type 2 Diabetes Mellitus has reached epidemic levels globally, and several studies have confirmed a link between gut microbial dysbiosis and aberrant glucose homeostasis among people with diabetes. While the assumption is that abnormal metabolomic signatures would often accompany microbial dysbiosis, the connection remains largely unknown. In this study, we investigated how diet changed the gut bacteriome, mycobiome and metabolome in people with and without type 2 Diabetes.1 Differential abundance testing determined that the metabolites Propionate, U8, and 2-Hydroxybutyrate were significantly lower, and 3-Hydroxyphenyl acetate was higher in the high fiber diet compared to low fiber diet in the healthy control group. Next, using multi-omics factor analysis (MOFA2), we attempted to uncover sources of variability that drive each of the different groups (bacterial, fungal, and metabolite) on all samples combined (control and DM II). Performing variance decomposition, ten latent factors were identified, and then each latent factor was tested for significant correlations with age, BMI, diet, and gender. Latent Factor1 was the most significantly correlated. Remarkably, the model revealed that the mycobiome explained most of the variance in the DM II group (12.5%) whereas bacteria explained most of the variance in the control group (64.2% vs. 10.4% in the DM II group). The latent Factor1 was significantly correlated with dietary intake ( q  < 0.01). Further analyses of the impact of bacterial and fungal genera on Factor1 determined that the nine bacterial genera ( Phocaeicola, Ligilactobacillus, Mesosutterella, Acidaminococcus, Dorea A, CAG-317, Caecibacter, Prevotella and Gemmiger ) and one fungal genus ( Malassezia furfur ) were found to have high factor weights (absolute weight > 0.6). Alternatively, a linear regression model was fitted per disease group for each genus to visualize the relationship between the factor values and feature abundances, showing Xylose with positive weights and Propionate, U8, and 2-Hydroxybutyrate with negative weights. This data provides new information on the microbially derived changes that influence metabolic phenotypes in response to different diets and disease conditions in humans.

Type 2 diabetes (T2D) presents a global health concern, with evidence highlighting the role of the human gut microbiome in metabolic diseases. This study employs metabolic modelling to elucidate changes in host–microbiome interactions in T2D. Glucose levels, diet, 16S sequences and metadata were collected for 1866 individuals. In addition, microbial community models, and ecological interactions were simulated for the gut microbiomes. Our findings revealed a significant decrease in metabolic fluxes provided by the host’s diet to the microbiome in T2D patients, accompanied by increased within-community exchanges. Moreover, the diabetic microbiomes shift towards increased exploitative ecological interactions at the expense of collaborative interactions. The reduced microbiome-to-host butyrate flux, along with decreased fluxes of amino acids (including tryptophan), nucleotides, and B vitamins from the host’s diet, further highlight the dysregulation in microbial-host interactions in diabetes. In addition, microbiomes of T2D patients exhibit enrichment in energy metabolism, indicative of increased metabolic activity and antagonism. This study sheds light on the increased microbiome autonomy and antagonism accompanying diabetes, and provides candidate metabolic targets for intervention studies and experimental validation. Synopsis The gut microbiome supports health through balanced metabolic exchanges, but in type 2 diabetes rising blood glucose shifts interactions from cooperation to competition, altering cross-feeding patterns. This disruption may worsen disease progression, suggesting targets for restoring balance. Diabetes reduces host-to-microbiome and microbiome-to-host nutrient transfers—diminishing key metabolites like butyrate and tryptophan—while enhancing internal microbial exchanges, suggesting a shift toward greater microbial autonomy. Elevated blood glucose drives a reorganization of ecological interactions, shifting the balance from cooperative to more antagonistic and competitive relationships among gut bacteria. Metabolic flux analysis reveals that diabetic microbiomes upregulate energy metabolism and exhibit altered flux distributions in core metabolites such as nucleotides, B vitamins, and amino acids. These systemic metabolic changes highlight potential intervention targets—including butyrate, tryptophan, H₂S, and specific nucleotides—to restore balanced host–microbiome interactions in type 2 diabetes. The gut microbiome supports health through balanced metabolic exchanges, but in type 2 diabetes rising blood glucose shifts interactions from cooperation to competition, altering cross-feeding patterns. This disruption may worsen disease progression, suggesting targets for restoring balance.

Acute myeloid leukemia (AML) proliferation is significantly influenced by the interactions between leukemia blasts and the bone marrow (BM) microenvironment. Specifically, bone marrow mesenchymal stem cells (BMSCs) derived from AML patients (AML-MSCs) are known to support leukemia growth and facilitate disease progression. Studies have demonstrated that the transfer of mitochondria from MSCs to AML blasts not only aids in disease progression but also contributes to chemotherapy resistance. Furthermore, BM stromal cells can trigger a metabolic shift in malignant cells from mitochondrial respiration to glycolysis, which enhances both growth and chemo-resistance. This study focuses on identifying transcriptional and metabolic alterations in AML-MSCs to uncover potential targeted therapies for AML. We employed RNA sequencing and microarray analysis on MSCs cocultured with leukemic cells (MLL-AF9) and on MSCs isolated from both non-leukemic and MLL-AF9 leukemic mice. The Gene Set Enrichment Analysis (GSEA) indicated a significant downregulation of gene sets associated with oxidative phosphorylation and glycolysis in AML-MSCs. Furthermore, coculture of MSCs from wild-type mice (WT-MSCs) and a healthy donor individual (HD-MSCs) with AML cells demonstrated reduced oxidative phosphorylation and glycolysis. These metabolic changes were consistent in AML-MSCs derived from both leukemic mice and patients. Our results indicate that AML cells diminish the metabolic capacity of MSCs, specifically targeting oxidative phosphorylation and glycolysis. These findings suggest potential metabolic vulnerabilities that could be exploited to develop more effective therapeutic strategies for AML.

For over a century, the live bearing guppy, Poecilia reticulata, has been used to study sexual selection as well as local adaptation. Natural guppy populations differ in many traits that are of intuitively adaptive significance such as ornamentation, age at maturity, brood size and body shape. Water depth, light supply, food resources and predation regime shape these traits, and barrier waterfalls often separate contrasting environments in the same river. We have assembled and annotated the genome of an inbred single female from a high-predation site in the Guanapo drainage. The final assembly comprises 731.6 Mb with a scaffold N50 of 5.3 MB. Scaffolds were mapped to linkage groups, placing 95% of the genome assembly on the 22 autosomes and the X-chromosome. To investigate genetic variation in the population used for the genome assembly, we sequenced 10 wild caught male individuals. The identified 5 million SNPs correspond to an average nucleotide diversity (π) of 0.0025. The genome assembly and SNP map provide a rich resource for investigating adaptation to different predation regimes. In addition, comparisons with the genomes of other Poeciliid species, which differ greatly in mechanisms of sex determination and maternal resource allocation, as well as comparisons to other teleost genera can begin to reveal how live bearing evolved in teleost fish.

Epstein–Barr virus (EBV)-associated diffuse large B-cell lymphoma not otherwise specified (DLBCL NOS) constitute a distinct clinicopathological entity in the current World Health Organization (WHO) classification. However, its genomic features remain sparsely characterized. Here, we combine whole-genome sequencing (WGS), targeted amplicon sequencing (tNGS), and fluorescence in situ hybridization (FISH) from 47 EBV + DLBCL (NOS) cases to delineate the genomic landscape of this rare disease. Integrated WGS and tNGS analysis clearly distinguished this tumor type from EBV-negative DLBCL due to frequent mutations in ARID1A (45%), KMT2A/KMT2D (32/30%), ANKRD11 (32%), or NOTCH2 (32%). WGS uncovered structural aberrations including 6q deletions (5/8 patients), which were subsequently validated by FISH (14/32 cases). Expanding on previous reports, we identified recurrent alterations in CCR6 (15%), DAPK1 (15%), TNFRSF21 (13%), CCR7 (11%), and YY1 (6%). Lastly, functional annotation of the mutational landscape by sequential gene set enrichment and network propagation predicted an effect on the nuclear factor κB (NFκB) pathway (CSNK2A2, CARD10), IL6/JAK/STAT (SOCS1/3, STAT3), and WNT signaling (FRAT1, SFRP5) alongside aberrations in immunological processes, such as interferon response. This first comprehensive description of EBV + DLBCL (NOS) tumors substantiates the evidence of its pathobiological independence and helps stratify the molecular taxonomy of aggressive lymphomas in the effort for future therapeutic strategies.

A small number of de novo assembled human genomes have been reported to date, and few have been complemented with population-based genetic variation, which is particularly important for North Africa, a region underrepresented in current genome-wide references. Here, we combine long- and short-read whole-genome sequencing data with recent assembly approaches into a de novo assembly of an Egyptian genome. The assembly demonstrates well-balanced quality metrics and is complemented with variant phasing via linked reads into haploblocks, which we associate with gene expression changes in blood. To construct an Egyptian genome reference, we identify genome-wide genetic variation within a cohort of 110 Egyptian individuals. We show that differences in allele frequencies and linkage disequilibrium between Egyptians and Europeans may compromise the transferability of European ancestry-based genetic disease risk and polygenic scores, substantiating the need for multi-ethnic genome references. Thus, the Egyptian genome reference will be a valuable resource for precision medicine. North Africans are underrepresented in current genome-wide data sets. Here, the authors provide an Egyptian genome reference, consisting of de novo assembly of the genome of an Egyptian individual and genome-wide genetic variation from a representative cohort of 110 Egyptian individuals.

BackgroundDuplications at the Xp21.2 locus have previously been linked to 46,XY gonadal dysgenesis (GD), which is thought to result from gene dosage effects of NR0B1 (DAX1), but the exact disease mechanism remains unknown.MethodsPatients with 46,XY GD were analysed by whole genome sequencing. Identified structural variants were confirmed by array CGH and analysed by high-throughput chromosome conformation capture (Hi-C).ResultsWe identified two unrelated patients: one showing a complex rearrangement upstream of NR0B1 and a second harbouring a 1.2 Mb triplication, including NR0B1. Whole genome sequencing and Hi-C analysis revealed the rewiring of a topological-associated domain (TAD) boundary close to NR0B1 associated with neo-TAD formation and may cause enhancer hijacking and ectopic NR0B1 expression. Modelling of previous Xp21.2 structural variations associated with isolated GD support our hypothesis and predict similar neo-TAD formation as well as TAD fusion.ConclusionHere we present a general mechanism how deletions, duplications or inversions at the NR0B1 locus can lead to partial or complete GD by disrupting the cognate TAD in the vicinity of NR0B1. This model not only allows better diagnosis of GD with copy number variations (CNVs) at Xp21.2, but also gives deeper insight on how spatiotemporal activation of developmental genes can be disrupted by reorganised TADs causing impairment of gonadal development.

Onychomycosis (OM) is a common fungal nail infection. Based on the rich mycobial diversity in healthy toenails, we speculated that this is lost in OM due to the predominance of a single pathogen. We used next generation sequencing to obtain insights into the biodiversity of fungal communities in both healthy individuals and OM patients. By sequencing, a total of 338 operational-taxonomic units were found in OM patients and healthy controls. Interestingly, a classifier distinguished three distinct subsets: healthy controls and two groups within OM patients with either a low or high abundance of Trichophyton . Diversity per sample was decreased in controls compared to cases with low Trichophyton abundance (LTA), while cases with a high Trichophyton abundance (HTA) showed a lower diversity. Variation of mycobial communities between the samples showed shifts in the community structure between cases and controls—mainly driven by HTA cases. Indeed, LTA cases had a fungal β-diversity undistinguishable from that of healthy controls. Collectively, our data provides an in-depth characterization of fungal diversity in health and OM. Our findings also suggest that onychomycosis develops either through pathogen-driven mechanisms, i.e., in HTA cases, or through host and/or environmental factors, i.e., in cases with a low Trichophyton abundance.