Excision of HIV-1 provirus as novel approach to eradicate the infection

Background: Despite potent antiretroviral drugs, HIV-1 infection cannot be eliminated and patients must be treated lifetime to avoid AIDS-related illnesses. Decades of exposure to antiretroviral drugs may cause severe metabolic disorders and lead to a chronic HIV disease with various immunodeficiency complications and other clinically relevant comorbidities. Novel strategies to eradicate the infection are clearly needed. Gene editing is an emerging technology that inserts, replaces, or deletes pieces of DNA from a genome. Engineering is achieved by means of artificially constructed endonucleases that bind specific sequences and work as molecular scissors. Aim: To develop Transcription Activator Tale Effector Nucleases (TALEN)to cut off (excise) the HIV-1 provirus from infected cells. Methods: We designed and constructed TALEN targeting an internal conserved region of HIV-1 LTRs. TALEN were tested for specificity and efficiency of cleavage in cells transduced with replication competent HIV-1 molecular clones produced ad hoc and carrying green fluorescent protein(GFP) and luciferase (Luc). Proviral excision was measured by flow cytometry, western blot, and Luc assay. Fate of excised HIV-1 genome and repair of host cell genome were monitored by rolling circle amplification and standard molecular assays.Mock and irrelevant TALEN (irTALEN) transduced cells were tested in parallel as controls. Results: Compared to pre-treatment levels, TALEN reduced GFP and Luc activities to 20% and 15% at day 3 and 7 post-treatment (pt), respectively. GFP became undetectable by western blot from day 3, and Luc activity was almost negligible at day 7. No reduction was observed in irTALEN treated cells. Circular, excised HIV-1 DNA was monitored for molecular conformation , persistence, integration activity, and GFP/Luc expression up to one month pt. Excised provirus , mostly organized in concatamers, devoid of LRTs –and therefore unable to integrate -, and with no expression activity persisted for several weeks. Genome DNA double strand breaks were repaired by cellular DNA repair enzymes. Conclusion. TALEN testing against clinical and laboratory isolates will commence shortly. Activity Should be confirmed, this proof-of-concept study will pave the way to exploit gene editing to cure HIV-1 infected cells

Philadelphia-like acute lymphoblastic leukemia is associated with minimal residual disease persistence and poor outcome. First report of the minimal residual disease-oriented GIMEMA LAL1913

Early recognition of Philadelphia-like (Ph-like) acute lymphoblastic leukemia (ALL) cases could impact on the management and outcome of this subset of B-lineage ALL. In order to assess the prognostic value of the Ph-like status in a pediatric-inspired, minimal residual disease (MRD)-driven trial, we screened 88 B-lineage ALL cases negative for major fusion genes (BCR-ABL1, ETV6-RUNX1, TCF3-PBX1 and KTM2Ar) enrolled in the GIMEMA LAL1913 front-line protocol for adult BCR/ABL1-negative ALL. The screening - performed using the "BCR/ABL1-like predictor" - identified 28 Ph-like cases (31.8%), characterized by CRLF2 overexpression (35.7%), JAK/STAT pathway mutations (33.3%), IKZF1 (63.6%), BTG1 (50%) and EBF1 (27.3%) deletions, and rearrangements targeting tyrosine kinases or CRLF2 (40%). The correlation with outcome highlighted that: i) the complete remission rate was significantly lower in Ph-like compared to non-Phlike cases (74.1% vs. 91.5%, P=0.044); ii) at time point 2, decisional for transplant allocation, 52.9% of Ph-like cases versus 20% of non-Ph-like were MRD-positive (P=0.025); iii) the Ph-like profile was the only parameter associated with a higher risk of being MRD-positive at time point 2 (P=0.014); iv) at 24 months, Ph-like patients had a significantly inferior event-free and disease-free survival compared to non-Ph-like patients (33.5% vs. 66.2%, P=0.005 and 45.5% vs. 72.3%, P=0.062, respectively). This study documents that Ph-like patients have a lower complete remission rate, event-free survival and disease-free survival, as well as a greater MRD persistence also in a pediatric-oriented and MRD-driven adult ALL protocol, thus reinforcing that the early recognition of Ph-like ALL patients at diagnosis is crucial to refine risk-stratification and to optimize therapeutic strategies. Clinicaltrials gov. Identifier: 02067143

Systematic benchmarking of single-cell ATAC-sequencing protocols.

Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool for dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies has remained absent. In this study, we benchmark the performance of eight scATAC-seq methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) as a reference sample and develop PUMATAC, a universal preprocessing pipeline, to handle the various sequencing data formats. Our analyses reveal significant differences in sequencing library complexity and tagmentation specificity, which impact cell-type annotation, genotype demultiplexing, peak calling, differential region accessibility and transcription factor motif enrichment. Our findings underscore the importance of sample extraction, method selection, data processing and total cost of experiments, offering valuable guidance for future research. Finally, our data and analysis pipeline encompasses 169,000 PBMC scATAC-seq profiles and a best practices code repository for scATAC-seq data analysis, which are freely available to extend this benchmarking effort to future protocols

Systematic benchmarking of single-cell ATAC-sequencing protocols.

Acknowledgements: H.H. received support for the project PID2020-115439GB-I00 funded by MCIN/AEI/10.13039/501100011033. This publication is also supported as part of a project (BCLLATLAS and ESPACE) that has received funding from the European Research Council under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement numbers 810287 and 874710). This work was supported by a European Research Council Consolidator grant to S.A. (724226_cis-CONTROL), KU Leuven (C14/22/125 to S.A.), Foundation Against Cancer (F/2020/1396 to S.A.), F.W.O. (grants G0I2722N, G0B5619N and G094121N to S.A. and a PhD fellowship to F.D.) and Aligning Science Across Parkinson’s (grant number ASAP-000430 to S.A.). K.B.M. and S.A.T. are supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194). Computing was performed at the Vlaams Supercomputer Center and high-throughput sequencing at the Genomics Core Leuven. M.R.C. is supported by the National Institutes on Aging K99/R00AG059918. This work was supported by funding from the Rita Allen Foundation (W.J.G.) and the Human Frontiers Science Program (RGY006S; W.J.G.). W.J.G. is a Chan Zuckerberg Biohub investigator and acknowledges grants 2017-174468 and 2018-182817 from the Chan Zuckerberg Initiative and National Institutes of Health grants RM1-HG007735, UM1-HG009442, UM1-HG009436, R01-HG00990901 and U19-AI057266 (to W.J.G.). W.J.G. acknowledges funding from Emerson Collective. B.D. received financial support by Swiss National Science Foundation 310030_197082 and the EPFL. L.S.L. receives support from an Emmy Noether fellowship by the German Research Foundation (LU 2336/2-1), a National Institutes of Health grant UM1HG012076, a Longevity Impetus grant and a Hector Research Career Development Award by the Hector Fellow Academy. A.C.A. is supported by National Institutes of Health grants RF1-MH128842, R35-GM124704 and R01-DA047237 as well as a Silver Family Foundation Innovator Award.Funder: H.H. received support for the project PID2020-115439GB-I00- funded by MCIN/AEI/ 10.13039/501100011033. This publication is also supported as part of a project (BCLLATLAS and ESPACE) that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement No 810287 and 874710).Funder: M.R.C. is supported by the National Institutes on Aging K99/R00AG059918.Funder: K.B.M. is supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194).Funder: S.A.T. is supported by Wellcome (WT211276/Z/18/Z and Sanger core grant WT206194).Funder: This work was supported by funding from the Rita Allen Foundation (W.J.G.), the Human Frontiers Science (RGY006S) (W.J.G.). W.J.G. is a Chan Zuckerberg Biohub investigator and acknowledges grants 2017-174468 and 2018-182817 from the Chan Zuckerberg Initiative, and the National Institutes of Health grants RM1-HG007735, UM1-HG009442, UM1-HG009436, R01- HG00990901, and U19- AI057266 (to W.J.G.). W.J.G. acknowledges funding from Emerson Collective.Funder: This work was supported by an ERC Consolidator Grant to S.A. (no. 724226_cis- CONTROL), KU Leuven (grant no. C14/22/125 to S.A.), Foundation Against Cancer (grant no, F/2020/1396 to S.A.), F.W.O. (grants G0I2722N, G0B5619N and G094121N to S.A.), Aligning Science Across Parkinson’s (ASAP, grant no. ASAP-000430 to S.A.)Single-cell assay for transposase-accessible chromatin by sequencing (scATAC-seq) has emerged as a powerful tool for dissecting regulatory landscapes and cellular heterogeneity. However, an exploration of systemic biases among scATAC-seq technologies has remained absent. In this study, we benchmark the performance of eight scATAC-seq methods across 47 experiments using human peripheral blood mononuclear cells (PBMCs) as a reference sample and develop PUMATAC, a universal preprocessing pipeline, to handle the various sequencing data formats. Our analyses reveal significant differences in sequencing library complexity and tagmentation specificity, which impact cell-type annotation, genotype demultiplexing, peak calling, differential region accessibility and transcription factor motif enrichment. Our findings underscore the importance of sample extraction, method selection, data processing and total cost of experiments, offering valuable guidance for future research. Finally, our data and analysis pipeline encompasses 169,000 PBMC scATAC-seq profiles and a best practices code repository for scATAC-seq data analysis, which are freely available to extend this benchmarking effort to future protocols