Chromatin activation profiling of stereotyped chronic lymphocytic leukemias reveals a subset 8-specific signature.

The chromatin activation landscape of chronic lymphocytic leukemia (CLL) with stereotyped B-cell receptor immunoglobulin is currently unknown. In this study, we report the results of a whole-genome chromatin profiling of histone 3 lysine 27 acetylation of 22 CLLs from major subsets, which were compared against nonstereotyped CLLs and normal B-cell subpopulations. Although subsets 1, 2, and 4 did not differ much from their nonstereotyped CLL counterparts, subset 8 displayed a remarkably distinct chromatin activation profile. In particular, we identified 209 de novo active regulatory elements in this subset, which showed similar patterns with U-CLLs undergoing Richter transformation. These regions were enriched for binding sites of 9 overexpressed transcription factors. In 78 of 209 regions, we identified 113 candidate overexpressed target genes, 11 regions being associated with more than 2 adjacent genes. These included blocks of up to 7 genes, suggesting local coupregulation within the same genome compartment. Our findings further underscore the uniqueness of subset 8 CLL, notable for the highest risk of Richter's transformation among all CLLs and provide additional clues to decipher the molecular basis of its clinical behavior.

Higher-order immunoglobulin repertoire restrictions in CLL: the illustrative case of stereotyped subsets 2 and 169.

Chronic lymphocytic leukemia (CLL) major stereotyped subset 2 (IGHV3-21/IGLV3-21, ∼2.5% of all cases of CLL) is an aggressive disease variant, irrespective of the somatic hypermutation (SHM) status of the clonotypic IGHV gene. Minor stereotyped subset 169 (IGHV3-48/IGLV3-21, ∼0.2% of all cases of CLL) is related to subset 2, as it displays a highly similar variable antigen-binding site. We further explored this relationship through next-generation sequencing and crystallographic analysis of the clonotypic B-cell receptor immunoglobulin. Branching evolution of the predominant clonotype through intraclonal diversification in the context of ongoing SHM was evident in both heavy and light chain genes of both subsets. Molecular similarities between the 2 subsets were highlighted by the finding of shared SHMs within both the heavy and light chain genes in all analyzed cases at either the clonal or subclonal level. Particularly noteworthy in this respect was a ubiquitous SHM at the linker region between the variable and the constant domain of the IGLV3-21 light chains, previously reported as critical for immunoglobulin homotypic interactions underlying cell-autonomous signaling capacity. Notably, crystallographic analysis revealed that the IGLV3-21-bearing CLL subset 169 immunoglobulin retains the same geometry and contact residues for the homotypic intermolecular interaction observed in subset 2, including the SHM at the linker region, and, from a molecular standpoint, belong to a common structural mode of autologous recognition. Collectively, our findings document that stereotyped subsets 2 and 169 are very closely related, displaying shared immunoglobulin features that can be explained only in the context of shared functional selection.

Increased proportion and altered properties of intermediate monocytes in the peripheral blood of patients with lower risk Myelodysplastic Syndrome.

Immune deregulation has a critical role in the pathogenesis of lower risk myelodysplastic syndromes (MDS). The cells of the macrophage/monocyte lineage have been reported to contribute to the inflammatory process in MDS through impaired phagocytosis of the apoptotic hemopoietic cells and abnormal production of cytokines. In the present study we assessed the number of peripheral blood (PB) monocyte subsets, namely the classical CD14bright/CD16-, intermediate CD14bright/CD16+ and non-classical CD14dim/CD16+ cells, in patients with lower risk (low/intermediate-I) MDS (n = 32). We also assessed the production of tumor necrosis factor (TNF)α by patient PB monocytes in response to immune stimulus as well as their transcriptome profile. Compared to age- and sex-matched healthy individuals (n = 19), MDS patients had significantly lower number of classical and increased number of intermediate monocytes. Patient intermediate monocytes displayed increased production of TNFα following stimulation with lipopolysaccharide, compared to healthy individuals. Transcriptional profiling comparison of CD16+ monocytes from patients and controls revealed 43 differentially expressed genes mostly associated with biological pathways/processes relevant to hemopoiesis, immune signaling and cell adhesion. These data provide evidence for the first-time that distinct monocyte subsets display abnormal quantitative and functional characteristics in lower risk MDS substantiating their role in the immune deregulation associated with the disease.

Integrated epigenomic and transcriptomic analysis reveals TP63 as a novel player in clinically aggressive chronic lymphocytic leukemia.

Chronic lymphocytic leukemia (CLL) stereotyped subsets #6 and #8 include cases expressing unmutated B cell receptor immunoglobulin (BcR IG) (U-CLL). Subset #6 (IGHV1-69/IGKV3-20) is less aggressive compared to subset #8 (IGHV4-39/IGKV1(D)-39) which has the highest risk for Richter's transformation among all CLL. The underlying reasons for this divergent clinical behavior are not fully elucidated. To gain insight into this issue, here we focused on epigenomic signatures and their links with gene expression, particularly investigating genome-wide DNA methylation profiles in subsets #6 and #8 as well as other U-CLL cases not expressing stereotyped BcR IG. We found that subset #8 showed a distinctive DNA methylation profile compared to all other U-CLL cases, including subset #6. Integrated analysis of DNA methylation and gene expression revealed significant correlation for several genes, particularly highlighting a relevant role for the TP63 gene which was hypomethylated and overexpressed in subset #8. This observation was validated by quantitative PCR, which also revealed TP63 mRNA overexpression in additional nonsubset U-CLL cases. BcR stimulation had distinct effects on p63 protein expression, particularly leading to induction in subset #8, accompanied by increased CLL cell survival. This pro-survival effect was also supported by siRNA-mediated downregulation of p63 expression resulting in increased apoptosis. In conclusion, we report that DNA methylation profiles may vary even among CLL patients with similar somatic hypermutation status, supporting a compartmentalized approach to dissecting CLL biology. Furthermore, we highlight p63 as a novel prosurvival factor in CLL, thus identifying another piece of the complex puzzle of clinical aggressiveness.

Multi-organ single-cell transcriptomics of immune cells uncovered organ-specific gene expression and functions.

Despite the wealth of publicly available single-cell datasets, our understanding of distinct resident immune cells and their unique features in diverse human organs remains limited. To address this, we compiled a meta-analysis dataset of 114,275 CD45+ immune cells sourced from 14 organs in healthy donors. While the transcriptome of immune cells remains relatively consistent across organs, our analysis has unveiled organ-specific gene expression differences (GTPX3 in kidney, DNTT and ACVR2B in thymus). These alterations are linked to different transcriptional factor activities and pathways including metabolism. TNF-α signaling through the NFkB pathway was found in several organs and immune compartments. The presence of distinct expression profiles for NFkB family genes and their target genes, including cytokines, underscores their pivotal role in cell positioning. Taken together, immune cells serve a dual role: safeguarding the organs and dynamically adjusting to the intricacies of the host organ environment, thereby actively contributing to its functionality and overall homeostasis.

Microbial co-occurrence network demonstrates spatial and climatic trends for global soil diversity.

Despite recent research efforts to explore the co-occurrence patterns of diverse microbes within soil microbial communities, a substantial knowledge-gap persists regarding global climate influences on soil microbiota behaviour. Comprehending co-occurrence patterns within distinct geoclimatic groups is pivotal for unravelling the ecological structure of microbial communities, that are crucial for preserving ecosystem functions and services. Our study addresses this gap by examining global climatic patterns of microbial diversity. Using data from the Earth Microbiome Project, we analyse a meta-community co-occurrence network for bacterial communities. This method unveils substantial shifts in topological features, highlighting regional and climatic trends. Arid, Polar, and Tropical zones show lower diversity but maintain denser networks, whereas Temperate and Cold zones display higher diversity alongside more modular networks. Furthermore, it identifies significant co-occurrence patterns across diverse climatic regions. Central taxa associated with different climates are pinpointed, highlighting climate's pivotal role in community structure. In conclusion, our study identifies significant correlations between microbial interactions in diverse climatic regions, contributing valuable insights into the intricate dynamics of soil microbiota.

Machine learning and multi-omics data in chronic lymphocytic leukemia: the future of precision medicine?

Chronic lymphocytic leukemia is a complex and heterogeneous hematological malignancy. The advance of high-throughput multi-omics technologies has significantly influenced chronic lymphocytic leukemia research and paved the way for precision medicine approaches. In this review, we explore the role of machine learning in the analysis of multi-omics data in this hematological malignancy. We discuss recent literature on different machine learning models applied to single omic studies in chronic lymphocytic leukemia, with a special focus on the potential contributions to precision medicine. Finally, we highlight the recently published machine learning applications in multi-omics data in this area of research as well as their potential and limitations.

InterTADs: integration of multi-omics data on topologically associated domains, application to chronic lymphocytic leukemia.

The integration of multi-omics data can greatly facilitate the advancement of research in Life Sciences by highlighting new interactions. However, there is currently no widespread procedure for meaningful multi-omics data integration. Here, we present a robust framework, called InterTADs, for integrating multi-omics data derived from the same sample, and considering the chromatin configuration of the genome, i.e. the topologically associating domains (TADs). Following the integration process, statistical analysis highlights the differences between the groups of interest (normal versus cancer cells) relating to (i) independent and (ii) integrated events through TADs. Finally, enrichment analysis using KEGG database, Gene Ontology and transcription factor binding sites and visualization approaches are available. We applied InterTADs to multi-omics datasets from 135 patients with chronic lymphocytic leukemia (CLL) and found that the integration through TADs resulted in a dramatic reduction of heterogeneity compared to individual events. Significant differences for individual events and on TADs level were identified between patients differing in the somatic hypermutation status of the clonotypic immunoglobulin genes, the core biological stratifier in CLL, attesting to the biomedical relevance of InterTADs. In conclusion, our approach suggests a new perspective towards analyzing multi-omics data, by offering reasonable execution time, biological benchmarking and potentially contributing to pattern discovery through TADs.

The TΑp63/BCL2 axis represents a novel mechanism of clinical aggressiveness in chronic lymphocytic leukemia.

The TA-isoform of the p63 transcription factor (TAp63) has been reported to contribute to clinical aggressiveness in chronic lymphocytic leukemia (CLL) in a hitherto elusive way. Here, we sought to further understand and define the role of TAp63 in the pathophysiology of CLL. First, we found that elevated TAp63 expression levels are linked with adverse clinical outcomes, including disease relapse and shorter time-to-first treatment and overall survival. Next, prompted by the fact that TAp63 participates in an NF-κB/TAp63/BCL2 antiapoptotic axis in activated mature, normal B cells, we explored molecular links between TAp63 and BCL2 also in CLL. We documented a strong correlation at both the protein and the messenger RNA (mRNA) levels, alluding to the potential prosurvival role of TAp63. This claim was supported by inducible downregulation of TAp63 expression in the MEC1 CLL cell line using clustered regularly interspaced short palindromic repeats (CRISPR) system, which resulted in downregulation of BCL2 expression. Next, using chromatin immunoprecipitation (ChIP) sequencing, we examined whether BCL2 might constitute a transcriptional target of TAp63 and identified a significant binding profile of TAp63 in the BCL2 gene locus, across a genomic region previously characterized as a super enhancer in CLL. Moreover, we identified high-confidence TAp63 binding regions in genes mainly implicated in immune response and DNA-damage procedures. Finally, we found that upregulated TAp63 expression levels render CLL cells less responsive to apoptosis induction with the BCL2 inhibitor venetoclax. On these grounds, TAp63 appears to act as a positive modulator of BCL2, hence contributing to the antiapoptotic phenotype that underlies clinical aggressiveness and treatment resistance in CLL.

Detecting SARS-CoV-2 lineages and mutational load in municipal wastewater and a use-case in the metropolitan area of Thessaloniki, Greece.

The COVID-19 pandemic represents an unprecedented global crisis necessitating novel approaches for, amongst others, early detection of emerging variants relating to the evolution and spread of the virus. Recently, the detection of SARS-CoV-2 RNA in wastewater has emerged as a useful tool to monitor the prevalence of the virus in the community. Here, we propose a novel methodology, called lineagespot, for the monitoring of mutations and the detection of SARS-CoV-2 lineages in wastewater samples using next-generation sequencing (NGS). Our proposed method was tested and evaluated using NGS data produced by the sequencing of 14 wastewater samples from the municipality of Thessaloniki, Greece, covering a 6-month period. The results showed the presence of SARS-CoV-2 variants in wastewater data. lineagespot was able to record the evolution and rapid domination of the Alpha variant (B.1.1.7) in the community, and allowed the correlation between the mutations evident through our approach and the mutations observed in patients from the same area and time periods. lineagespot is an open-source tool, implemented in R, and is freely available on GitHub and registered on bio.tools.

A Computational Framework for Pattern Detection on Unaligned Sequences: An Application on SARS-CoV-2 Data.

The exponential growth of genome sequences available has spurred research on pattern detection with the aim of extracting evolutionary signal. Traditional approaches, such as multiple sequence alignment, rely on positional homology in order to reconstruct the phylogenetic history of taxa. Yet, mining information from the plethora of biological data and delineating species on a genetic basis, still proves to be an extremely difficult problem to consider. Multiple algorithms and techniques have been developed in order to approach the problem multidimensionally. Here, we propose a computational framework for identifying potentially meaningful features based on k-mers retrieved from unaligned sequence data. Specifically, we have developed a process which makes use of unsupervised learning techniques in order to identify characteristic k-mers of the input dataset across a range of different k-values and within a reasonable time frame. We use these k-mers as features for clustering the input sequences and identifying differences between the distributions of k-mers across the dataset. The developed algorithm is part of an innovative and much promising approach both to the problem of grouping sequence data based on their inherent characteristic features, as well as for the study of changes in the distributions of k-mers, as the k-value is fluctuating within a range of values. Our framework is fully developed in Python language as an open source software licensed under the MIT License, and is freely available at https://github.com/BiodataAnalysisGroup/kmerAnalyzer.

UMIc: A Preprocessing Method for UMI Deduplication and Reads Correction.

A recent refinement in high-throughput sequencing involves the incorporation of unique molecular identifiers (UMIs), which are random oligonucleotide barcodes, on the library preparation steps. A UMI adds a unique identity to different DNA/RNA input molecules through polymerase chain reaction (PCR) amplification, thus reducing bias of this step. Here, we propose an alignment free framework serving as a preprocessing step of fastq files, called UMIc, for deduplication and correction of reads building consensus sequences from each UMI. Our approach takes into account the frequency and the Phred quality of nucleotides and the distances between the UMIs and the actual sequences. We have tested the tool using different scenarios of UMI-tagged library data, having in mind the aspect of a wide application. UMIc is an open-source tool implemented in R and is freely available from https://github.com/BiodataAnalysisGroup/UMIc.

miRkit: R framework analyzing miRNA PCR array data.

OBJECTIVE: The characterization of microRNAs (miRNA) in recent years is an important advance in the field of gene regulation. To this end, several approaches for miRNA expression analysis and various bioinformatics tools have been developed over the last few years. It is a common practice to analyze miRNA PCR Array data using the commercially available software, mostly due to its convenience and ease-of-use. RESULTS: In this work we present miRkit, an open source framework written in R, that allows for the comprehensive analysis of RT-PCR data, from the processing of raw data to a functional analysis of the produced results. The main goal of the proposed tool is to provide an assessment of the samples' quality, perform data normalization by endogenous and exogenous miRNAs, and facilitate differential and functional enrichment analysis. The tool offers fast execution times with low memory usage, and is freely available under a ΜΙΤ license from https://bio.tools/mirkit . Overall, miRkit offers the full analysis from the raw RT-PCR data to functional analysis of targeted genes, and specifically designed to support the popular miScript miRNA PCR Array (Qiagen) technology.

The Calcitriol/Vitamin D Receptor System Regulates Key Immune Signaling Pathways in Chronic Lymphocytic Leukemia.

It has been proposed that vitamin D may play a role in prevention and treatment of cancer while epidemiological studies have linked vitamin D insufficiency to adverse disease outcomes in various B cell malignancies, including chronic lymphocytic leukemia (CLL). In this study, we sought to obtain deeper biological insight into the role of vitamin D and its receptor (VDR) in the pathophysiology of CLL. To this end, we performed expression analysis of the vitamin D pathway molecules; complemented by RNA-Sequencing analysis in primary CLL cells that were treated in vitro with calcitriol, the biologically active form of vitamin D. In addition, we examined calcitriol effects ex vivo in CLL cells cultured in the presence of microenvironmental signals, namely anti-IgM/CD40L, or co-cultured with the supportive HS-5 cells; and, CLL cells from patients under ibrutinib treatment. Our study reports that the calcitriol/VDR system is functional in CLL regulating signaling pathways critical for cell survival and proliferation, including the TLR and PI3K/AKT pathways. Moreover, calcitriol action is likely independent of the microenvironmental signals in CLL, since it was not significantly affected when combined with anti-IgM/CD40L or in the context of the co-culture system. This finding was also supported by our finding of preserved calcitriol signaling capacity in CLL patients under ibrutinib treatment. Overall, our results indicate a relevant biological role for vitamin D in CLL pathophysiology and allude to the potential clinical utility of vitamin D supplementation in patients with CLL.

Nuclear inclusions of pathogenic ataxin-1 induce oxidative stress and perturb the protein synthesis machinery.

Spinocerebellar ataxia type-1 (SCA1) is caused by an abnormally expanded polyglutamine (polyQ) tract in ataxin-1. These expansions are responsible for protein misfolding and self-assembly into intranuclear inclusion bodies (IIBs) that are somehow linked to neuronal death. However, owing to lack of a suitable cellular model, the downstream consequences of IIB formation are yet to be resolved. Here, we describe a nuclear protein aggregation model of pathogenic human ataxin-1 and characterize IIB effects. Using an inducible Sleeping Beauty transposon system, we overexpressed the ATXN1(Q82) gene in human mesenchymal stem cells that are resistant to the early cytotoxic effects caused by the expression of the mutant protein. We characterized the structure and the protein composition of insoluble polyQ IIBs which gradually occupy the nuclei and are responsible for the generation of reactive oxygen species. In response to their formation, our transcriptome analysis reveals a cerebellum-specific perturbed protein interaction network, primarily affecting protein synthesis. We propose that insoluble polyQ IIBs cause oxidative and nucleolar stress and affect the assembly of the ribosome by capturing or down-regulating essential components. The inducible cell system can be utilized to decipher the cellular consequences of polyQ protein aggregation. Our strategy provides a broadly applicable methodology for studying polyQ diseases.

Pretransplant Genetic Susceptibility: Clinical Relevance in Transplant-Associated Thrombotic Microangiopathy.

Transplant-associated thrombotic microangiopathy (TA-TMA) is a life-threatening complication of allogeneic hematopoietic cell transplantation (HCT). We hypothesized that pretransplant genetic susceptibility is evident in adult TA-TMA and further investigated the association of TMA-associated variants with clinical outcomes. We studied 40 patients with TA-TMA, donors of 18 patients and 40 control non-TMA HCT recipients, without significant differences in transplant characteristics. Genomic DNA from pretransplant peripheral blood was sequenced for TMA-associated genes. Donors presented significantly lower frequency of rare variants and variants in exonic/splicing/untranslated region (UTR) regions, compared with TA-TMA patients. Controls also showed a significantly lower frequency of rare variants in ADAMTS13, CD46, CFH, and CFI. The majority of TA-TMA patients (31/40) presented with pathogenic or likely pathogenic variants. Patients refractory to conventional treatment (62%) and patients that succumbed to transplant-related mortality (65%) were significantly enriched for variants in exonic/splicing/UTR regions. In conclusion, increased incidence of pathogenic, rare and variants in exonic/splicing/UTR regions of TA-TMA patients suggests genetic susceptibility not evident in controls or donors. Notably, variants in exonic/splicing/UTR regions were associated with poor response and survival. Therefore, pretransplant genomic screening may be useful to intensify monitoring and early intervention in patients at high risk for TA-TMA.

MeinteR: A framework to prioritize DNA methylation aberrations based on conformational and cis-regulatory element enrichment.

DNA methylation studies have been reformed with the advent of single-base resolution arrays and bisulfite sequencing methods, enabling deeper investigation of methylation-mediated mechanisms. In addition to these advancements, numerous bioinformatics tools address important computational challenges, covering DNA methylation calling up to multi-modal interpretative analyses. However, contrary to the analytical frameworks that detect driver mutational signatures, the identification of putatively actionable epigenetic events remains an unmet need. The present work describes a novel computational framework, called MeinteR, that prioritizes critical DNA methylation events based on the following hypothesis: critical aberrations of DNA methylation more likely occur on a genomic substrate that is enriched in cis-acting regulatory elements with distinct structural characteristics, rather than in genomic "deserts". In this context, the framework incorporates functional cis-elements, e.g. transcription factor binding sites, tentative splice sites, as well as conformational features, such as G-quadruplexes and palindromes, to identify critical epigenetic aberrations with potential implications on transcriptional regulation. The evaluation on multiple, public cancer datasets revealed significant associations between the highest-ranking loci with gene expression and known driver genes, enabling for the first time the computational identification of high impact epigenetic changes based on high-throughput DNA methylation data.

DNA methylation profiles in chronic lymphocytic leukemia patients treated with chemoimmunotherapy.

BACKGROUND: In order to gain insight into the contribution of DNA methylation to disease progression of chronic lymphocytic leukemia (CLL), using 450K Illumina arrays, we determined the DNA methylation profiles in paired pre-treatment/relapse samples from 34 CLL patients treated with chemoimmunotherapy, mostly (n = 31) with the fludarabine-cyclophosphamide-rituximab (FCR) regimen. RESULTS: The extent of identified changes in CLL cells versus memory B cells from healthy donors was termed "epigenetic burden" (EB) whereas the number of changes between the pre-treatment versus the relapse sample was termed "relapse changes" (RC). Significant (p < 0.05) associations were identified between (i) high EB and short time-to-first-treatment (TTFT); and, (ii) few RCs and short time-to-relapse. Both the EB and the RC clustered in specific genomic regions and chromatin states, including regulatory regions containing binding sites of transcription factors implicated in B cell and CLL biology. CONCLUSIONS: Overall, we show that DNA methylation in CLL follows different dynamics in response to chemoimmunotherapy. These epigenetic alterations were linked with specific clinical and biological features.