Multidifferential study of identified charged hadron distributions in ZZ-tagged jets in proton-proton collisions at s=\sqrt{s}=13 TeV

Jet fragmentation functions are measured for the first time in proton-proton collisions for charged pions, kaons, and protons within jets recoiling against a $Z$ boson. The charged-hadron distributions are studied longitudinally and transversely to the jet direction for jets with transverse momentum 20 $< p_{\textrm{T}} < 100$ GeV and in the pseudorapidity range $2.5 < \eta < 4$. The data sample was collected with the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.64 fb$^{-1}$. Triple differential distributions as a function of the hadron longitudinal momentum fraction, hadron transverse momentum, and jet transverse momentum are also measured for the first time. This helps constrain transverse-momentum-dependent fragmentation functions. Differences in the shapes and magnitudes of the measured distributions for the different hadron species provide insights into the hadronization process for jets predominantly initiated by light quarks.Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-013.html (LHCb public pages

Study of the B−→Λc+Λˉc−K−B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-} decay

The decay $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ is studied in proton-proton collisions at a center-of-mass energy of $\sqrt{s}=13$ TeV using data corresponding to an integrated luminosity of 5 $\mathrm{fb}^{-1}$ collected by the LHCb experiment. In the $\Lambda_{c}^+ K^{-}$ system, the $\Xi_{c}(2930)^{0}$ state observed at the BaBar and Belle experiments is resolved into two narrower states, $\Xi_{c}(2923)^{0}$ and $\Xi_{c}(2939)^{0}$, whose masses and widths are measured to be $$m(\Xi_{c}(2923)^{0}) = 2924.5 \pm 0.4 \pm 1.1 \,\mathrm{MeV}, \\ m(\Xi_{c}(2939)^{0}) = 2938.5 \pm 0.9 \pm 2.3 \,\mathrm{MeV}, \\ \Gamma(\Xi_{c}(2923)^{0}) = \phantom{000}4.8 \pm 0.9 \pm 1.5 \,\mathrm{MeV},\\ \Gamma(\Xi_{c}(2939)^{0}) = \phantom{00}11.0 \pm 1.9 \pm 7.5 \,\mathrm{MeV},$$ where the first uncertainties are statistical and the second systematic. The results are consistent with a previous LHCb measurement using a prompt $\Lambda_{c}^{+} K^{-}$ sample. Evidence of a new $\Xi_{c}(2880)^{0}$ state is found with a local significance of $3.8\,\sigma$, whose mass and width are measured to be $2881.8 \pm 3.1 \pm 8.5\,\mathrm{MeV}$ and $12.4 \pm 5.3 \pm 5.8 \,\mathrm{MeV}$, respectively. In addition, evidence of a new decay mode $\Xi_{c}(2790)^{0} \to \Lambda_{c}^{+} K^{-}$ is found with a significance of $3.7\,\sigma$. The relative branching fraction of $B^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-}$ with respect to the $B^{-} \to D^{+} D^{-} K^{-}$ decay is measured to be $2.36 \pm 0.11 \pm 0.22 \pm 0.25$, where the first uncertainty is statistical, the second systematic and the third originates from the branching fractions of charm hadron decays.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-028.html (LHCb public pages

Measurement of the ratios of branching fractions R(D∗)\mathcal{R}(D^{*}) and R(D0)\mathcal{R}(D^{0})

The ratios of branching fractions $\mathcal{R}(D^{*})\equiv\mathcal{B}(\bar{B}\to D^{*}\tau^{-}\bar{\nu}_{\tau})/\mathcal{B}(\bar{B}\to D^{*}\mu^{-}\bar{\nu}_{\mu})$ and $\mathcal{R}(D^{0})\equiv\mathcal{B}(B^{-}\to D^{0}\tau^{-}\bar{\nu}_{\tau})/\mathcal{B}(B^{-}\to D^{0}\mu^{-}\bar{\nu}_{\mu})$ are measured, assuming isospin symmetry, using a sample of proton-proton collision data corresponding to 3.0 fb${ }^{-1}$ of integrated luminosity recorded by the LHCb experiment during 2011 and 2012. The tau lepton is identified in the decay mode $\tau^{-}\to\mu^{-}\nu_{\tau}\bar{\nu}_{\mu}$. The measured values are $\mathcal{R}(D^{*})=0.281\pm0.018\pm0.024$ and $\mathcal{R}(D^{0})=0.441\pm0.060\pm0.066$, where the first uncertainty is statistical and the second is systematic. The correlation between these measurements is $\rho=-0.43$. Results are consistent with the current average of these quantities and are at a combined 1.9 standard deviations from the predictions based on lepton flavor universality in the Standard Model.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-039.html (LHCb public pages

Modulation of SLFN11 induces changes in DNA Damage response in breast cancer

Abstract Background Lack of Schlafen family member 11 (SLFN11) expression has been recently identified as a dominant genomic determinant of response to DNA damaging agents in numerous cancer types. Thus, several strategies aimed at increasing SLFN11 are explored to restore chemosensitivity of refractory cancers. In this study, we examined various approaches to elevate SLFN11 expression in breast cancer cellular models and confirmed a corresponding increase in chemosensitivity with using the most successful efficient one. As oncogenic transcriptomic downregulation is often driven by methylation of the promotor region, we explore the demethylation effect of 5-aza-2′-deoxycytidine (decitabine), on the SLFN11 gene. Since SLFN11 has been reported as an interferon inducible gene, and interferon is secreted during an active anti-tumor immune response, we investigated the in vitro effect of IFN-γ on SLFN11 expression in breast cancer cell lines. As a secondary approach to pick up cross talk between immune cells and SLFN11 expression we used indirect co-culture of breast cancer cells with activated PBMCs and evaluated if this can drive SLFN11 upregulation. Finally, as a definitive and specific way to modulate SLFN11 expression we implemented SLFN11 dCas9 (dead CRISPR associated protein 9) systems to specifically increase or decrease SLFN11 expression. Results After confirming the previously reported correlation between methylation of SLFN11 promoter and its expression across multiple cell lines, we showed in-vitro that decitabine and IFN-γ could increase moderately the expression of SLFN11 in both BT-549 and T47D cell lines. The use of a CRISPR-dCas9 UNISAM and KRAB system could increase or decrease SLFN11 expression significantly (up to fivefold), stably and specifically in BT-549 and T47D cancer cell lines. We then used the modified cell lines to quantify the alteration in chemo sensitivity of those cells to treatment with DNA Damaging Agents (DDAs) such as Cisplatin and Epirubicin or DNA Damage Response (DDRs) drugs like Olaparib. RNAseq was used to elucidate the mechanisms of action affected by the alteration in SLFN11 expression. In cell lines with robust SLFN11 promoter methylation such as MDA-MB-231, no SLFN11 expression could be induced by any approach. Conclusion To our knowledge this is the first report of the stable non-lethal increase of SLFN11 expression in a cancer cell line. Our results show that induction of SLFN11 expression can enhance DDA and DDR sensitivity in breast cancer cells and dCas9 systems may represent a novel approach to increase SLFN11 and achieve higher sensitivity to chemotherapeutic agents, improving outcome or decreasing required drug concentrations. SLFN11-targeting therapies might be explored pre-clinically to develop personalized approaches

Increased expression of protease-activated receptor 1 (PAR-1) in human leukemias

Protease-activated receptor 1 (PAR-1) is a G-protein-coupled receptor that is overexpressed in solid tumors, being associated with several pro-tumoral responses including primary growth, invasion, metastasis and angiogenesis. Expression of PAR-1 in human leukemic cell lines is reported but the status of its expression in human leukemic patients is currently unknown. In this study we evaluated the expression pattern of PAR-1 in patients with the four main types of leukemia - chronic lymphocytic leukemia subtype B (B-CLL), acute lymphoblastic leukemia subtype B (B-ALL), acute myeloid leukemia (AML) and chronic myeloid leukemia (CML). Flow cytometry analyses show that lymphocytes from B-CLL patients express this receptor at similar levels to healthy individuals. On the other hand, it was observed a significant increase in PAR-1 expression in B-ALL lymphocytes as compared to B-CLL and healthy donors. Flow cytometric and real-time PCR demonstrated a significant increase in PAR-1 expression in granulocytes from CML patients in blast phase (CML-BP) but not in chronic phase (CML-CP) as compared to healthy donors. Finally, a significant increase in PAR-1 expression has been also observed in blasts from AML (subtypes M4 and M5) patients, as compared to monocytes or granulocytes from healthy donors. We conclude that PAR-1 might play an important biological role in aggressive leukemias and might offer additional strategies for the development of new therapies. (C) 2010 Elsevier Inc. All rights reserved.Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)Fundacao de Amparo Pesquisa do Estado do Rio de Janeiro Carlos Chagas Filho (FAPERJ)Fundacao do Cance

Additional file 1 of Modulation of SLFN11 induces changes in DNA Damage response in breast cancer

Additional file 1: gRNA location and CRISPR modification. (Fig. S1A) The predicted promoter region of SLFN11 (in green) is surrounding the gene’s exon1 and CpG island (in red) analysis show its location in the center of the promoter area. gRNAs were therefore designed along the central region of the promoter of SLFN11 (N1 to N10). (Fig. S1B) Schematic representation of the strategy adopted to respectively increase SLFN11 expression using UNISAM system and decrease SLFN11 expression using KRAB system. After insertion of the gRNA into the respective plasmids, cells were transformed with the integrative plasmids using electroporation and selected for the expression of respectively mCherry or GFP reporter genes. Cells were then analyzed for SLFN11 expression by westernblot and by Q-RT-PCR. Additional file 2: Screening of gRNA efficiency at upregulating or downregulating SLFN11 using UNISAM or KRAB systems. (Fig. S2A–Fig. S2D) Relative mRNA expression of SLFN11 analyzed by Q-RT-PCR (N = 3, technical replicates) (Fig. S2A–Fig. S2C) and relative SLFN11 protein expression analyzed by CWB (N = 2, technical replicates) (Fig. S2B–Fig. S2D) in BT-549 cancer cell lines modified with each gRNA for CRISPR-dCas9-UNISAM (Fig. S2A, Fig. S2B) or CRISPR-dCas9-KRAB (Fig. S2C, Fig. S2D) relative to non-modified cell line. (Fig. S2E–Fig. S2H) Relative mRNA expression of SLFN11 analyzed by Q-RT-PCR (N = 3, technical replicates) (Fig. S2E–Fig. S2G) and relative SLFN11 protein expression analyzed by CWB (N = 2, technical replicates) (Fig. S2F–Fig. S2H) in T47D cancer cell lines modified with each gRNA for CRISPR-dCas9-UNISAM (Fig. S2E–Fig. S2F) or 5 (N1, N2, N6, N7 and N10) of the 7 gRNA for CRISPR-dCas9-KRAB (Fig. S2G, Fig. S2H) relative to non-modified cell line. (Fig. S2I) Relative mRNA expression of SLFN11 analyzed by Q-RT-PCR in MDA-MB-231 cancer cell lines modified with each gRNA for CRISPR-dCas9-UNISAM relative to non-modified cell line. Additional file 3: Representative CWB results. (Fig. S3A) Representative CWB results of the analysis of SLFN11 protein expression in the 8 tested unmodified breast cancer cell lines compared to HFF. (Fig. S3B) Representative SLFN11 protein expression in BT-549, T47D and MDA-MB-231 analyzed by CWB after treatment with 5uM of DAC for 72 h compared to the expression level in untreated HFF. (Fig. S3C) Representative SLFN11 protein expression in BT-549, T47D and MDA-MB-231 analyzed by CWB after treatment with 5 nM of IFN-γ for 24 h compared to the expression level in untreated HFF. (Fig. S3D–Fig. S3E) Representative SLFN11 protein expression in BT-549 (D) or T47D (Fig. S3E) modified with UNISAM and each of the 7 gRNA compared to the respective unmodified cells. (Fig. S3F-Fig. S3G) Representative SLFN11 protein expression in BT-549 (Fig. S3F) or T47D (Fig. S3G) modified with KRAB and each of the 7 gRNA compared to the respective unmodified cells. (Fig. S3H-Fig. S3I) Representative SLFN11 protein expression level analyzed by CWB in BT-549 (Fig. S3H) or T47D (Fig. S3I) after modification with UNISAM (gRNA 7 or gRNA SCR) or KRAB (gRNA 7 or gRNA SCR) compared to respective unmodified cells and HFF. (Fig. S3J) Representative SLFN11 protein expression level analyzed by CWB in MDA-MB-231 after modification with UNISAM (gRNA 7) compared to respective unmodified cells and HFF. Additional file 4: Fig. S4. Principal component analysis pre and post-combat. Principal component analysis based on gene expression for all samples (baseline and cisplatin-treated samples) pre and post performing combat on samples to remove batch effect for BT-549 and T47D cell lines. Additional file 5: Fig. S5 Pathway enrichment analysis. Enriched pathways associated with DEG (n = 181, FDR   = 1) from limma analysis in UNISAM up/KRAB down and UNISAM down/KRAB up, using Ingenuity Pathway Analysis (IPA)

An integrated tumor, immune and microbiome atlas of colon cancer

The lack of multi-omics cancer datasets with extensive follow-up information hinders the identification of accurate biomarkers of clinical outcome. In this cohort study, we performed comprehensive genomic analyses on fresh-frozen samples from 348 patients affected by primary colon cancer, encompassing RNA, whole-exome, deep T cell receptor and 16S bacterial rRNA gene sequencing on tumor and matched healthy colon tissue, complemented with tumor whole-genome sequencing for further microbiome characterization. A type 1 helper T cell, cytotoxic, gene expression signature, called Immunologic Constant of Rejection, captured the presence of clonally expanded, tumor-enriched T cell clones and outperformed conventional prognostic molecular biomarkers, such as the consensus molecular subtype and the microsatellite instability classifications. Quantification of genetic immunoediting, defined as a lower number of neoantigens than expected, further refined its prognostic value. We identified a microbiome signature, driven by Ruminococcus bromii, associated with a favorable outcome. By combining microbiome signature and Immunologic Constant of Rejection, we developed and validated a composite score (mICRoScore), which identifies a group of patients with excellent survival probability. The publicly available multi-omics dataset provides a resource for better understanding colon cancer biology that could facilitate the discovery of personalized therapeutic approaches