Bimodal Expression Research Articles

Bioinformatic meta-analysis of transcriptomics of developing Drosophila muscles identifies temporal regulatory transcription factors including a notch effector

The intricate mechanism of gene regulation coordinates the precise control of when, where, and to what extent genes are activated or repressed, directing the complex processes that govern cellular functions and development. Dysregulation of gene expression can lead to diseases such as autoimmune disorders, cancer, and neurodegeneration. Transcriptional regulation, especially involving transcription factors (TFs), plays a major role in controlling gene expression. This study focuses on identifying gene regulatory mechanisms that generate distinct gene expression patterns during Drosophila muscle development. Utilising a bioinformatics approach, we analysed the developmental time-point-specific transcriptomics resource generated by Spletter et al., which includes mRNA sequencing data at eight stages of indirect flight muscle (IFM) development. They had identified 40 distinct genome-wide clusters representing various temporal expression dynamics using “soft” clustering. Promoter sequences of genes in these clusters were analysed to predict novel motifs that act as TF binding sites. Comparative analysis with known motifs revealed significant overlaps, indicating shared transcriptional regulation. The physiological relevance of predicted TFs was confirmed by cross-referencing with experimental ChIP-seq data. We focused on Cluster 36, characterised by a unique bimodal temporal expression profile, and identified candidate genes Rbfox1 and zfh1 for further study. Ectopic overexpression experiments revealed that the TF Enhancer of split m8 helix- loop-helix [E(spl)m8-HLH], part of the Notch signalling pathway, acts as a transcriptional repressor for Rbfox1 and zfh1. Our findings highlight the complexity of transcriptional regulation during myogenesis and identify key TFs that could be targeted for further research in muscle development and related disorders.

Abstract 4309: Establishment of intratumor heterogeneity using patient derived lung cancer cells

Abstract Overcoming cancer treatment resistance is a problem that must be solved to improve the prognosis of cancer patients. In recent years, genome analysis has revealed the existence of multiple subgroups with different properties within a single tumor, and intratumor heterogeneity is a cause of treatment resistance. It has also been reported that. However, the mechanism of intratumor heterogeneity remains unclear. Previous research on intratumor heterogeneity has mainly focused on analyzing patient cancer cells, classifying subgroups, and estimating characteristic factors. This method has two drawbacks. One is limitations in including all subgroups as research subjects, and other is lack of verification of whether characteristic factors are actually involved in intratumor heterogeneity in patients. Therefore, it is necessary to use a model of "early intratumor heterogeneity" in which monoclonal cells are divided into subgroups with different properties, and to verify the discovered factors using patient-derived cancer cell lines. In alectinib-resistant cell line (H2228-AR1S) created from monoclonal ALK-positive lung cancer cell line (H2228) by exposing to alectinib in vitro, existence of two fractions with different appearances. Using flow cytometry, it was revealed that H2228-AR1S bimodally expresses EpCAM, and the EpCAM-low fraction has high migration ability. In EpCAM-low fraction, epithelial markers were decreased, and mesenchymal markers were increased using immunoblotting. It suggests that EpCAM low subpopulation has undergone epithelial-mesenchymal transition (EMT). Additionally, we established a BRAF-positive lung cancer cell line (KTOR83) collected with consent from patients attending Kyoto University Hospital. Cells after in vitro dabrafenib exposure to KTOR83 showed bimodal EpCAM expression, and the EpCAM-low fraction has high migration ability. We established an intratumor heterogeneous model using commercially available cells and a patient-derived lung cancer cell line. Furthermore, we will explore factors common to the two models and examine factors involved in intratumor heterogeneity. Citation Format: Tomoko Funazo, Hiroaki Ozasa, Takahiro Tsuji, Yusuke Shima, Keiichiro Suminaga, Kentaro Hashimoto, Hiroshi Yoshida, Tatsuya Ogimoto, Kazutaka Hosoya, Hitomi Ajimizu, Takashi Nomizo, Hironori Yoshida, Toyohiro Hirai. Establishment of intratumor heterogeneity using patient derived lung cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4309.

Abstract 6254: Casein kinase II (CK2) expression in pediatric solid tumors

Abstract Despite recent advances, high-risk neuroblastoma (NB) and sarcomas account for 15% and 5% of all pediatric cancer deaths, respectively. Many patients attain remission, but approximately half of patients relapse. Genomic alterations such as MYCN amplification and ALK mutations have been shown to be important in prognosis and treatment. Despite improvements, approximately 20% of patients have refractory or recurrent disease after therapy, underscoring the need for further genomic understanding and new approaches to treatment.CK2 is a serine/threonine kinase that regulates numerous cellular processes such as cell growth, proliferation, and survival. Overexpression of CK2 is associated with a poor prognosis in several adult solid tumors, making it a potential target of interest in pediatric solid tumors. This study aims to investigate CK2 expression among patients with NB, Ewing sarcoma (EWS), and rhabdomyosarcoma (RMS) and its correlation with patient survival. MethodsPatients were enrolled in NMTRC008/009: “Feasibility Trial Using Molecular Guided Therapy for Patients with Relapsed/Refractory Childhood Cancer” and BCC-001. WES and RNA-Seq were performed at the Translational Genomics Research Institute and Sema4 or Caris respectively. RNA expression levels were compared to a normal whole-body reference and a pediatric cancer reference. Differential expression data were interpreted in the context of systems biology.ResultsTumor WES was performed on 297 patients (212 NB, 50 EWS, 35 RMS) and RNA-Seq on 284 patients (210 NB, 39 EWS, 35 RMS). The most common mutations or copy number alterations were seen in MYCN, ATRX, ALK, CDKN2A, and chr17q+ in NB; EWSR1-FLI1 fusion in EWS; PAX3-FOXO1 fusion in RMS. RNA expression analysis was performed to understand MYCN-driver status by covariance and interactions and to identify subsets of patients with high/low expression of CK2 in different tumors. CK2 bimodal expression is seen across various cancers and normal tissues, implying distinct sub-groups exist in the data. In NB, overexpression of the CK2 gene with statistical significance was found in MYCN-amplified and chr17q+ tumors. A positive and negative correlation of the CK2 gene is seen with several target genes like MYCN, TRIM71, LIN28B, and HMGA2, JAK2, AR, STAT3, EGFR, LEF1, and NFKB2, respectively. In EWS and RMS, subgroups are identified with high and low expression of the CK2 gene.ConclusionGenomic sequencing of pediatric solid tumors showed a low mutation burden. RNA analysis resulted in the identification of potential novel therapeutic targets involving CK2 pathways. High expression of CK2 correlates with MYCN status. Further analysis of patient molecular subgroups is ongoing. Citation Format: Abhinav Beeravally Nagulapally, Chandrika Behura, Divya Gandra, Giselle Saulnier Sholler. Casein kinase II (CK2) expression in pediatric solid tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6254.

Human perception of cats' communicative cues: human‐cat communication goes multimodal

As a central key to apprehend the human-cat relationship, the question of how humans decode the behaviours expressed by their feline companions has drawn scientists’ attention in the past decades. To this point, previous studies have not investigated humans’ understanding of cats’ communicative cues in a multimodal perspective. While communication generally implies multiple channels, the influence of signal modality as relates to human-cat communication, is still poorly understood. Therefore, the primary aim of the present study was to investigate whether people can identify cats’ communicative information using unimodal (visual or vocal) and bimodal (visual and vocal) signals. Participants (n=630) were recruited via online advertisement on social media. Each participant viewed 24 carefully operationalised video clips of cats in different emotional/behavioural conditions. Four categories were included: contentment, discontentment, solicitation (food or attention) and predatory behaviour. Clips were presented as vocal only, visual only, or bimodal (visual and vocal cues). Video clips showing a bimodal expression were identified with the highest score (91,8% of correct ratings) compared with the visual only (87,3%) and the vocal only (72,2%). All modalities considered, contentment returned the highest identification score (90,1% correct), followed by solicitation (87,2%), then predatory behaviour (86,3%). Discontentment was the most difficult behaviour to be correctly identified (71,6%). Finally, for all behavioural categories and modalities, professionals working with animals returned a higher score than lay people (86.2% vs 82.9%). Taken together, our data underline the influence of the signal modality on interspecific communication between cats and humans. Information emitted by cats as a bimodal signal (visual and vocal) is better understood by humans than visual signals. The most difficult to decipher for humans are vocal signals emitted alone. A better understanding of humans’ abilities to understand their feline counterparts, could potentially help pet owners, and animal care practitioners to optimise cat care and welfare.

Analyses of POL30 (PCNA) reveal positional effects in transient repression or bi-modal active/silent state at the sub-telomeres of S. cerevisiae

BackgroundClassical studies on position effect variegation in Drosophila have demonstrated the existence of bi-modal Active/Silent state of the genes juxtaposed to heterochromatin. Later studies with irreversible methods for the detection of gene repression have revealed a similar phenomenon at the telomeres of Saccharomyces cerevisiae and other species. In this study, we used dual reporter constructs and a combination of reversible and non-reversible methods to present evidence for the different roles of PCNA and histone chaperones in the stability and the propagation of repressed states at the sub-telomeres of S. cerevisiae.ResultsWe show position dependent transient repression or bi-modal expression of reporter genes at the VIIL sub-telomere. We also show that mutations in the replicative clamp POL30 (PCNA) or the deletion of the histone chaperone CAF1 or the RRM3 helicase lead to transient de-repression, while the deletion of the histone chaperone ASF1 causes a shift from transient de-repression to a bi-modal state of repression. We analyze the physical interaction of CAF1 and RRM3 with PCNA and discuss the implications of these findings for our understanding of the stability and transmission of the epigenetic state of the genes.ConclusionsThere are distinct modes of gene silencing, bi-modal and transient, at the sub-telomeres of S. cerevisiae. We characterise the roles of CAF1, RRM3 and ASF1 in these modes of gene repression. We suggest that the interpretations of past and future studies should consider the existence of the dissimilar states of gene silencing.

Stochastic modeling of the mRNA life process: A generalized master equation

The mRNA life cycle is a complex biochemical process, involving transcription initiation, elongation, termination, splicing, and degradation. Each of these molecular events is multistep and can create a memory. The effect of this molecular memory on gene expression is not clear, although there are many related yet scattered experimental reports. To address this important issue, we develop a general theoretical framework formulated as a master equation in the sense of queue theory, which can reduce to multiple previously studied gene models in limiting cases. This framework allows us to interpret experimental observations, extract kinetic parameters from experimental data, and identify how the mRNA kinetics vary under regulatory influences. Notably, it allows us to evaluate the influences of elongation processes on mature RNA distribution; e.g., we find that the non-exponential elongation time can induce the bimodal mRNA expression and there is an optimal elongation noise intensity such that the mature RNA noise achieves the lowest level. In a word, our framework can not only provide insight into complex mRNA life processes but also bridge a dialogue between theoretical studies and experimental data.

Deep Neural Networks for Predicting Single-Cell Responses and Probability Landscapes.

Engineering biology relies on the accurate prediction of cell responses. However, making these predictions is challenging for a variety of reasons, including the stochasticity of biochemical reactions, variability between cells, and incomplete information about underlying biological processes. Machine learning methods, which can model diverse input-output relationships without requiring a priori mechanistic knowledge, are an ideal tool for this task. For example, such approaches can be used to predict gene expression dynamics given time-series data of past expression history. To explore this application, we computationally simulated single-cell responses, incorporating different sources of noise and alternative genetic circuit designs. We showed that deep neural networks trained on these simulated data were able to correctly infer the underlying dynamics of a cell response even in the presence of measurement noise and stochasticity in the biochemical reactions. The training set size and the amount of past data provided as inputs both affected prediction quality, with cascaded genetic circuits that introduce delays requiring more past data. We also tested prediction performance on a bistable auto-activation circuit, finding that our initial method for predicting a single trajectory was fundamentally ill-suited for multimodal dynamics. To address this, we updated the network architecture to predict the entire distribution of future states, showing it could accurately predict bimodal expression distributions. Overall, these methods can be readily applied to the diverse prediction tasks necessary to predict and control a variety of biological circuits, a key aspect of many synthetic biology applications.

#5715 IMMUNE LANDSCAPING IN RHABDOMYOLYSIS INDUCED ACUTE KIDNEY INJURY IDENTIFIES SENESCENCE AS A POTENTIAL TARGET

Abstract Background and Aims Worldwide, acute kidney injury (AKI) has an incidence of 13.3 million cases per year leading to 1.7 million deaths. Rhabdomyolysis (RM) accounts for 10% of the AKI cases, and frequently occurs due to falling (often in elderly), crush syndrome or exposure to drugs. By causing skeletal muscle damage, RM leads to a release of muscle contents such as myoglobin into the circulatory system and trigger renal sterile inflammation. The role of macrophages in driving inflammatory phase of RM-AKI lesions and fibrosis-related prognosis has been reported but both the role of other immune cells and the macrophage heterogeneity in RM-AKI still lack detail. The aim of this study was to describe renal immune cell recruitment and more specifically the mononuclear phagocytic cell (MPC) population (macrophages, monocytes and dendritic cells) during RM-AKI through single cell RNA sequencing (scRNA-seq) in mice. Method Two-month-old C57BL/6J male mice were administered intramuscularly saline (control) or 50% glycerol for induction of RM. Forty-eight hours later mice were sacrificed, kidneys were collected and dissociated and CD45 live cells were sorted. Single cell cDNA libraries were prepared, and sequencing was performed according to the 10X Genomics protocol. Creatinine phosphokinase and blood urea nitrogen were quantified to validate muscle injury and renal function respectively. A combination treatment of dasatinib and quercetin (DQ) as senolytic drugs was performed. Intraperitoneal injections of dasatinib (10mg/kg) and quercetin (50mg/kg) were conducted with and without RM-AKI. Results scRNA-seq of CD45+ renal cells revealed the existence of 7 immune cell clusters with macrophages and monocytes as the most highly recruited cells in RM-AKI condition, indicating a major change in the MPC population. Specific clustering of MPCs divulged 10 clusters (8 macrophages, 1 monocyte and 1 dendritic cell cluster) revealing a higher heterogeneity of this population compared to what was previously observed by flow cytometry. Trajectory, pseudotime and KEGG pathway enrichment analyses disclosed a unique cluster highly expressing Stathmin 1 present in both control and RM-AKI condition where 164 cells were observed in control condition which expanded to 333 cells in RM-AKI condition. This unique cluster was characterized by a bimodal expression of MHCII markers and a downregulation after RM-AKI suggesting a transcriptional reprogramming. Cellular senescence was observed to be a predominant pathway in this unique cluster. Senolytics were identified as a possible pharmacological target based on pathway analysis. DQ protocol accompanied with an improvement of kidney function as well as an attenuation of MPC phenotypic modifications. Conclusion This study revealed the MPC diversity in RM-AKI through immune landscaping, trajectory and pathways in RM-AKI in mice. A unique cluster associated with possible reprogramming of macrophages was identified. Senolytics were established as a potential nephroprotective strategy in RM-AKI.

Regulation by cyclic di-GMP attenuates dynamics and enhances robustness of bimodal curli gene activation in Escherichia coli.

Curli amyloid fibers are a major constituent of the extracellular biofilm matrix formed by bacteria of the Enterobacteriaceae family. Within Escherichia coli biofilms, curli gene expression is limited to a subpopulation of bacteria, leading to heterogeneity of extracellular matrix synthesis. Here we show that bimodal activation of curli gene expression also occurs in well-mixed planktonic cultures of E. coli, resulting in all-or-none stochastic differentiation into distinct subpopulations of curli-positive and curli-negative cells at the entry into the stationary phase of growth. Stochastic curli activation in individual E. coli cells could further be observed during continuous growth in a conditioned medium in a microfluidic device, which further revealed that the curli-positive state is only metastable. In agreement with previous reports, regulation of curli gene expression by the second messenger c-di-GMP via two pairs of diguanylate cyclase and phosphodiesterase enzymes, DgcE/PdeH and DgcM/PdeR, modulates the fraction of curli-positive cells. Unexpectedly, removal of this regulatory network does not abolish the bimodality of curli gene expression, although it affects dynamics of activation and increases heterogeneity of expression levels among individual cells. Moreover, the fraction of curli-positive cells within an E. coli population shows stronger dependence on growth conditions in the absence of regulation by DgcE/PdeH and DgcM/PdeR pairs. We thus conclude that, while not required for the emergence of bimodal curli gene expression in E. coli, this c-di-GMP regulatory network attenuates the frequency and dynamics of gene activation and increases its robustness to cellular heterogeneity and environmental variation.

CD49d Expression Identifies a Biologically Distinct Subtype of Chronic Lymphocytic Leukemia with Inferior Progression-Free Survival on BTK Inhibitor Therapy.

To determine the role of CD49d for response to Bruton's tyrosine kinase inhibitors (BTKi) in patients with chronic lymphocytic leukemia (CLL). In patients treated with acalabrutinib (n = 48), CD49d expression, VLA-4 integrin activation, and tumor transcriptomes of CLL cells were assessed. Clinical responses to BTKis were investigated in acalabrutinib- (n = 48; NCT02337829) and ibrutinib-treated (n = 73; NCT01500733) patients. In patients treated with acalabrutinib, treatment-induced lymphocytosis was comparable for both subgroups but resolved more rapidly for CD49d+ cases. Acalabrutinib inhibited constitutive VLA-4 activation but was insufficient to block BCR and CXCR4-mediated inside-out activation. Transcriptomes of CD49d+ and CD49d- cases were compared using RNA sequencing at baseline and at 1 and 6 months on treatment. Gene set enrichment analysis revealed increased constitutive NF-κB and JAK-STAT signaling, enhanced survival, adhesion, and migratory capacity in CD49d+ over CD49d- CLL that was maintained during therapy. In the combined cohorts of 121 BTKi-treated patients, 48 (39.7%) progressed on treatment with BTK and/or PLCG2 mutations detected in 87% of CLL progressions. Consistent with a recent report, homogeneous and bimodal CD49d-positive cases (the latter having concurrent CD49d+ and CD49d- CLL subpopulations, irrespective of the traditional 30% cutoff value) had a shorter time to progression of 6.6 years, whereas 90% of cases homogenously CD49d- were estimated progression-free at 8 years (P = 0.0004). CD49d/VLA-4 emerges as a microenvironmental factor that contributes to BTKi resistance in CLL. The prognostic value of CD49d is improved by considering bimodal CD49d expression. See related commentary by Tissino et al., p. 3560.

Neurocan expression associates with better survival and viral positivity in Merkel cell carcinoma.

Merkel cell carcinoma (MCC) is a rare cutaneous neuroendocrine carcinoma that is frequently divided into Merkel cell polyomavirus negative and positive tumors due their distinct genomic and transcriptomic profiles, and disease outcomes. Although some prognostic factors in MCC are known, tumorigenic pathways, which that explain outcome differences in MCC are not fully understood. We investigated transcriptomes of 110 tissue samples of a formalin-fixed, paraffin-embedded MCC series by RNA sequencing to identify genes showing a bimodal expression pattern and predicting outcome in cancer and that potentially could play a role in tumorigenesis. We discovered 19 genes among which IGHM, IGKC, NCAN, OTOF, and USH2A were associated also with overall survival (all p-values < 0.05). From these genes, NCAN (neurocan) expression was detected in all 144 MCC samples by immunohistochemistry. Increased NCAN expression was associated with presence of Merkel cell polyomavirus DNA (p = 0.001) and viral large T antigen expression in tumor tissue (p = 0.004) and with improved MCC-specific survival (p = 0.027) and overall survival (p = 0.034). We conclude that NCAN expression is common in MCC, and further studies are warranted to investigate its role in MCC tumorigenesis.

An Atypical Presentation of X-linked Lymphoproliferative Disease type 1 Presenting with Seronegative Coccidioidomycosis

X-linked lymphoproliferative disease (XLP-1), an inborn error of immunity (IEI) caused by pathogenic variants in SH2D1A encoding signaling lymphocyte activation molecule-associated protein (SAP), is characterized by hemophagocytic lymphohistiocytosis (HLH), lymphoma, and hypogammaglobulinemia. We report the first known case of XLP-1 presenting with seronegative coccidioidomycosis. CaseA 13-year-old boy with persistent asthma, recurrent otitis media requiring four sets of tympanostomy tubes, chronic sinusitis, and Common Variable Immunodeficiency (CVID) presented with acute-on-chronic hypoxic respiratory failure. He had been off subcutaneous immunoglobulin (SCIg) replacement for 2 years. Sputum culture was positive for H. influenzae, treated with cefepime and azithromycin and supplemental oxygen for acute hypoxic respiratory failure. Fever persisted despite 48 hours of antibiotics. CT chest showed bronchiectasis with mediastinal, subcarinal and hilar adenopathy. Coccidioides immitis IgG and IgM were negative, but urine antigen was positive. There was no radiologic or cerebrospinal fluid evidence of disseminated coccidioidomycosis.Immunologic workup revealed undetectable IgG, IgA, IgM, and IgE (table 1). Lymphocyte subsets showed severe T and NK cell lymphopenia. Total B cell count was normal, but he had decreased CD19+27+ and transitional B cells, with absent switched memory B cells and no plasmablasts (table 1). Lymphocyte proliferation to mitogens/antigens was decreased. Serum ferritin was normal, and EBV PCR was negative. A targeted 429-gene panel identified a hemizygous variant of uncertain significance (VUS) in SH2D1A, c.82_102dup (p.Ser28_Ser34dup). SAP expression was absent in CD4+ T cells and NK cells with a bimodal expression in CD8+ T cells, suggesting possible mosaicism in this subset. He was diagnosed with X-linked lymphoproliferative disease 1 (XLP-1) and treated with fluconazole, trimethoprim-sulfamethoxazole prophylaxis, and IgG replacement (1 g/kg intravenous initially, then transitioned to weekly SCIg targeting an IgG >1000 mg/dL), with clinical improvement, resolution of fevers and acute hypoxia. He was referred for hematopoietic cell transplant evaluation.Coccidioidomycosis has not been described in a new diagnosis of XLP-1 and should be considered in this IEI. Coccidioidomycosis susceptibility has been reported in other IEIs including impaired interleukin-12/interferon-gamma axis signaling, STAT3 loss of function, chronic granulomatous disease, and recently cytidine triphosphate synthase 1 (CTPS1) deficiency.Table 1Patient’s immunologic variables compared to reference range. Absolute values are shownImmunologic VariablePatient ValuesReference RangeIgG<70 (L)<70 mg/dLIgA<15 (L)<15 mg/dLIgM<8 (L)<8 mg/dLIgE<1.5 (L)<1.5 IU/uLTotal CD4+ T Cells11 (L)570–2400 cells/uLTotal CD8+ T Cells63 (L)210–1200 cells/uLTotal CD19+ B cells173165–1154 cells/uLCD19+ CD24++ CD38++ (Transitional B Cells)0 (L)8–392 cells/uLCD19+CD24+CD38+ (Naïve B Cells)12159–1146 cells/uLCD19+CD27-(Naïve B cells)169140–2269 cells/uLCD19+ CD27+ CD38+ IgM- (Plasmablasts)0<30 cells/uLCD19+CD27+IgD-IgM- (Switched-memory B cells)0 (L)6–190 cells/uLCD16+ CD56+ (NK Cells)32 (L)78–470 cells/uLL – low

The FLOWERING LOCUS T gene expression is controlled by high-irradiance response and external coincidence mechanism in long days in Arabidopsis.

In natural long days, the florigen gene FLOWERING LOCUS T (FT) shows a bimodal expression pattern with morning and dusk peaks in Arabidopsis. This pattern differs from the one observed in the laboratory, and little is known about underlying mechanisms. A red : far-red (R : FR) ratio difference between sunlight and fluorescent light causes this FT pattern mismatch. We showed that bimodal FT expression patterns were induced in a day longer than 14 h with sunlight R : FR (= c. 1) conditions. By circadian gating experiments, we found that cumulative exposure of R : FR-adjusted light (R : FR ratio was adjusted to 1 with FR supplement) spanning from the afternoon to the next morning required full induction of FT in the morning. Conversely, only 2 h of R : FR adjustment in the late afternoon was sufficient for FT induction at dusk. We identified that phytochrome A (phyA) is required for the morning FT expression in response to the R : FR adjustment on the previous day. As a part of this mechanism, we showed that PHYTOCHROME-INTERACTING FACTOR 7 contributes to FT regulation. Our results suggest that phyA-mediated high-irradiance response and the external coincidence mechanism contribute to morning FT induction under natural long-day conditions.

Photoperiodic adaptation of aanat and clock gene expression in seasonal populations of Daphnia pulex

ABSTRACT Changes in day-length entrain the endogenous clock of organisms leading to complex responses to photoperiod. In long-lived organisms experiencing several seasons this response of the clock to photoperiod is phenotypically plastic. However, short-lived organisms often experience a single season without pronounced changes in day-length. For those, a plastic response of the clock to different seasons would not necessarily be adaptive. In aquatic ecosystems, zooplankton species like Daphnia live only for some weeks, i.e. one week up to ca. two months. However, they often show a succession of clones that are seasonally adapted to environmental changes. Here, we found that 16 Daphnia clones per each of three seasons ( = 48 clones) from the same pond and year differed in clock gene expression with a homogenous gene expression pattern in ephippia-hatched spring clones and a bimodal expression pattern in summer and autumn populations indicating an ongoing adaptation process. We clearly demonstrate that spring clones were adapted to a short, and summer clones to a long photoperiod. Furthermore, we found that gene expression of the melatonin-synthesis enzyme AANAT was always lowest in summer clones. In the Anthropocene, Daphnia’s clock might be disturbed by light-pollution and global warming. Since Daphnia is a key-organism in trophic carbon transfer, a disruption of its clock rhythm would be devastating for the stability of freshwater ecosystems. Our results are an important step in understanding the adaptation of Daphnia’s clock to environmental changes.

Abstract PD2-06: Implications of Heterogeneity in Breast Tumor Cell MHC-I Expression on Immunity and Therapeutic Resistance

Abstract Background Immune checkpoint inhibitors (ICIs) targeting the PD-1/L1 axis are approved in early-stage treatment for triple-negative breast cancer (TNBC), but only a fraction of patients benefit. Tumor expressed antigens bound to major histocompatibility complex-I (MHC-I) are required for CD8-mediated tumoricidal activity, and thus, response to anti-PD-1/L1 targeted ICI. However, many breast tumors downregulate, or heterogeneously express, MHC-I, making them less susceptible to ICIs. Tumor cells downregulating MHC-I may be effectively targeted by natural killer (NK) cells due to ‘missing self’ signals. However, this heterogeneity in MHC-I expression is poorly modeled in most preclinical studies, limiting our understanding of how to overcome ICI resistance in the context of heterogeneous MHC-I expression, as is often observed clinically. Objective We aimed to 1) quantitatively delineate how intratumoral heterogeneity in MHC class I expression affects immune responses and immunotherapy outcomes in mouse models and 2) determine whether targeting inhibitory signals on NK cells can overcome ICI resistance in MHC-I heterogenous TNBC. Methods We performed quantitative immunofluorescence for MHC-I, CD8, CD56, and pan-cytokeratin on breast cancer tumors from diverse subtypes (n=314) to obtain single-cell-resolution MHC-I expression and spatial information of tumor and immune cells. Fluorescence intensity and spatial analysis were processed to output individual tumor/stromal cell MHC-I expression and the composition of the local tumor microenvironment. To determine the functional effect of MHC-I heterogeneity in vivo, we generated a CRISPR-guided B2m knockout (B2m-null) in a murine orthotopic model (EMT6). We then combined MHC-I-proficient and MHC-I-deficient isogenic lines at various ratios to model how populations of MHC-I loss affected the immune microenvironment. We also assessed a second, intrinsically heterogenous (MHC-I expression) TNBC model E0771. To evaluate changes in the microenvironment, we used flow cytometry and an immune NanoString panel to evaluate gene expression patterns in tumor cells and infiltrating immune cells. Results TNBC patients had the highest MHC-I expression level across tumor cells, but also the highest variability and probability of demonstrating bimodal MHC-I expression (consisting of high and low/absent cells within a single tumor). ER+ tumors were unimodally low. Using spatial analysis, we identified that heterogenous MHC-I tumors had significantly higher levels of infiltrating NK cells(Paired T-Test: p=0.03). In murine models, even 10% or less of MHC-I-null (B2m-null) EMT6 cells in the tumor injection resulted in acquisition of ICI resistance. Interestingly, heterogeneity in expression of MHC-I resulted in a substantial infiltration by NKG2A+ NK cells compared to MHC-I high or -low tumors (Student T-Test: p=.002). Activation of these infiltrating NK cells via anti-NKG2A and anti-PD-L1 combination treatment restored complete responses in heterogeneous EMT6 tumors, and significantly extended survival in both E0771 (Mantel-Cox: p&amp;lt; 0.0001) and EMT6 models (Mantel-Cox: &amp;lt; 0.0001). Additionally, anti-NKG2A and anti-PDL1 combination treatment improved complete response in the heterogenous MHC-I EMT6 model to 30% and in the parental EMT-6 tumors to 70%. Conclusion Combined therapy with anti-NKG2A (targeting NK cells) and anti-PD-L1 (targeting CD8+ T cells) can restore immunotherapy responses and overcome resistance due to lack of MHC-I expression in tumor cell subpopulations. Citation Format: Brandie C. Taylor, Xiaopeng Sun, Justin Balko, Paula Gonzalez-Ericsson, Melinda Sanders. Implications of Heterogeneity in Breast Tumor Cell MHC-I Expression on Immunity and Therapeutic Resistance [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD2-06.

Clonal Evolution of Chronic Lymphocytic Leukemia with Bimodal CD49d Expression Reveals CD49d Plasticity

Clonal Evolution of Chronic Lymphocytic Leukemia with Bimodal CD49d Expression Reveals CD49d Plasticity

Gene Expression Analysis Reveals Downregulation of Complement Receptor 1 in Cold Agglutinin Disease

Introduction Cold Agglutinin Disease (CAD) is a rare B-cell lymphoproliferative disorder of the bone marrow, manifested by autoimmune hemolytic anemia caused by binding of monoclonal IgM autoantibodies to I antigen. We have described the molecular background of CAD in the context of extensive clinical data, and found frequent somatic mutations in KMT2D and CARD11 genes and showed that CARD11 and CXCR4 gene mutations correlate with hemoglobin level. Moreover, we also have discovered trisomy 3 and 12 or 18 in CAD. Gene expression data has not been reported in CAD and here we present novel RNA-seq data. Differential expression analysis revealed that the complement receptor 1 (CR1) gene was one of the genes most significantly down-regulated in CAD. Flow cytometry analysis further confirmed reduced CR1 protein expression on clonal CAD B cells. CR1 is an important negative regulator of B cell differentiation towards immunoglobulin-producing plasma cells and may thus contribute to CAD pathogenesis. Materials for RNA sequencing We analyzed samples from 12 CAD patients collected for clinical trial (NTC02689986) and 4 healthy controls (HC). Clonal B cells from CAD patients and IGM+ memory B cells from HC, were procured using fluorescence-activated cell sorting of blood or bone marrow cells. IGM+ memory B-cells were used as controls in view of their shared CD19+/CD27+/IGM+ immunophenotype with clonal B cells in CAD. Flow cytometry validation of CR1 expression CR1 protein expression was assessed by flow cytometry of a blood or bone marrow samples obtained from 15 CAD patients and blood samples from 5 HC. For each sample, CR1 expression was compared between IGM+ memory B-cells expressing immunoglobulin kappa light chain (IGK+) and IGM+ memory B-cells expressing immunoglobulin lambda light chain (IGL+), respectively. For CAD patients, the former had previously been demonstrated to be clonal cells, the latter functioned as non-clonal control cells. ResultsGene expression by RNA-seq RNA-seq data analysis and filtering resulted in a selection of 93 the most differentially expressed genes. The top 20 of the most differentially expressed genes from that list are: SLC4A1, SPTA1, YBX3, TESC, HBD, AHSP, TRAF1, HBA2, RHAG, CR1, CA1, SPTB, IL10, UBASH3B, ALAS2, HBA1, CRYM, RGCC, KANK2, IGHV4-34. These genes are upregulated at least 8 times in clonal CAD B cells compared to HC, except for CR1 which is downregulated ~11 times. The p-value, and p-value adjusted for multiple testing, are below 3.0-12 and 2.7-9, respectively, indicating statistically significant differences. Results are illustrated in Figure 1. Validation of CR1 level by flow cytometry In CAD patients, CR1 expression in IGK+ B cells was significantly lower than in IGL+ B cells (Figure 2) as indicated by a median fluorescence intensity that was 10.8x lower in IGK+ than in IGL+ B-cells. HC samples showed similar levels of CR1 expression in IGK+ and IGL+ B cells, respectively. Samples from CAD patients usually showed a bimodal CR1 expression in IGK+ B cells, but not in IGL+ B cells. HC samples did not reveal heterogeneity of CR1 expression in IGM+ memory B cell populations. Discussion CR1 is the receptor for C3b and C4b complement peptides (Klickstein et al. 1988), and its main function is to inhibit the complement cascade (Fearon 1979) and regulate proliferation of B cells (Khera et al. 2009). CR1 blocks proliferation induced by B-cell receptor activation, inhibits the differentiation of B cells to plasmablasts, and the production of immunoglobulins (Kremlitzka et al. 2012). CR1 ligation decreases phosphorylation of key molecules of the BCR-induced signaling cascade (Kremlitzka et al. 2016). Reduced expression of CR1 on B cells has previously been observed in several autoimmune conditions (Prokopec et al. 2010; Kremlitzka et al. 2016). In conclusion, we demonstrated that CR1 expression is highly downregulated in clonal CAD B cells, both at the mRNA and the protein level. This may be an important finding in the context of CAD, because complement controls B and T cell responses, and regulates the adaptive immunity (Killick et al. 2018). Therefore, downregulation of CR1 in CAD might be responsible for increased proliferation, survival of autoreactive B cells and IgM autoantibody secretion. The demonstration of reduced CR1 expression by flow cytometry by IGK compared to IGL IGM+ memory B cells might also be exploited as a diagnostic test. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

GMMchi: gene expression clustering using Gaussian mixture modeling

BackgroundCancer evolution consists of a stepwise acquisition of genetic and epigenetic changes, which alter the gene expression profiles of cells in a particular tissue and result in phenotypic alterations acted upon by natural selection. The recurrent appearance of specific genetic lesions across individual cancers and cancer types suggests the existence of certain “driver mutations,” which likely make up the major contribution to tumors’ selective advantages over surrounding normal tissue and as such are responsible for the most consequential aspects of the cancer cells’ gene expression patterns and phenotypes. We hypothesize that such mutations are likely to cluster with specific dichotomous shifts in the expression of the genes they most closely control, and propose GMMchi, a Python package that leverages Gaussian Mixture Modeling to detect and characterize bimodal gene expression patterns across cancer samples, as a tool to analyze such correlations using 2 × 2 contingency table statistics.ResultsUsing well-defined simulated data, we were able to confirm the robust performance of GMMchi, reaching 85% accuracy with a sample size of n = 90. We were also able to demonstrate a few examples of the application of GMMchi with respect to its capacity to characterize background florescent signals in microarray data, filter out uninformative background probe sets, as well as uncover novel genetic interrelationships and tumor characteristics. Our approach to analysing gene expression analysis in cancers provides an additional lens to supplement traditional continuous-valued statistical analysis by maximizing the information that can be gathered from bulk gene expression data.ConclusionsWe confirm that GMMchi robustly and reliably extracts bimodal patterns from both colorectal cancer (CRC) cell line-derived microarray and tumor-derived RNA-Seq data and verify previously reported gene expression correlates of some well-characterized CRC phenotypes.AvailabilityThe Python package GMMchi and our cell line microarray data used in this paper is available for downloading on GitHub at https://github.com/jeffliu6068/GMMchi.

AE-TPGG: a novel autoencoder-based approach for single-cell RNA-seq data imputation and dimensionality reduction.

Single-cell RNA sequencing (scRNA-seq) technology has become an effective tool for high-throughout transcriptomic study, which circumvents the averaging artifacts corresponding to bulk RNA-seq technology, yielding new perspectives on the cellular diversity of potential superficially homogeneous populations. Although various sequencing techniques have decreased the amplification bias and improved capture efficiency caused by the low amount of starting material, the technical noise and biological variation are inevitably introduced into experimental process, resulting in high dropout events, which greatly hinder the downstream analysis. Considering the bimodal expression pattern and the right-skewed characteristic existed in normalized scRNA-seq data, we propose a customized autoencoder based on a two-part-generalized-gamma distribution (AE-TPGG) for scRNA-seq data analysis, which takes mixed discrete-continuous random variables of scRNA-seq data into account using a two-part model and utilizes the generalized gamma (GG) distribution, for fitting the positive and right-skewed continuous data. The adopted autoencoder enables AE-TPGG to captures the inherent relationship between genes. In addition to the ability of achieving low-dimensional representation, the AE-TPGG model also provides a denoised imputation according to statistical characteristic of gene expression. Results on real datasets demonstrate that our proposed model is competitive to current imputation methods and ameliorates a diverse set of typical scRNA-seq data analyses.Electronic Supplementary MaterialSupplementary material is available in the online version of this article at 10.1007/s11704-022-2011-y.

A modified risk detection approach of biomarkers by frailty effect on multiple time to event data

Disease progression in a cancer patient are identified by a number of indications in the form of loco-regional relapse, distant metastasis etc. Biomarkers play an essential role in early identification of these indications. The biomarkers can influence how a particular cancer behaves and how it may respond to a specific treatment, e.g., the survival probability of breast cancer patients diagnosed with HER2 positive status is different from the same with HER2 negative status. Researches have shown that the genetic mutations are often inconsistent across tumours and this is depicted in the form of bimodal and multi-modal expression values. So, the heterogeneity of the biomarker statuses or levels should be taken into consideration while modelling the survival outcome. This heterogeneity factor which is often unobserved, is called frailty. In the presence of competing events, the scenario becomes more complex as only one of them can occur, which will censor the occurrence of other events. Incorporating independent frailties of each biomarker status for every cause of indications will not portray the complete picture of heterogeneity. The events indicating cancer progression are likely to be inter-related. So, the correlation should be incorporated through the frailties of different events. The process can be performed for a single biomarker using a competing risk model with correlated frailty and estimates are obtained using Expectation-Maximization algorithm. Based on the estimated variance of the frailty, the threshold levels of a biomarker are utilised as early detection tool of the disease progression or death. For multiple biomarkers, we have employed a sequential proportional hazard model using p-value combination method which will incorporate the correlations between the biomarkers. With the extensive algorithm in R, we have obtained the threshold levels of activity of multiple biomarkers in a competing risk scenario.