Forecasting Acute Kidney Injury and Resource Utilization in ICU patients using longitudinal, multimodal models

Background

Advances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients admitted to Intensive Care Units (ICU). Although substantial literature exists addressing broad subjects, including the prediction of mortality, length-of-stay, and readmission, studies focusing on forecasting Acute Kidney Injury (AKI), specifically dialysis anticipation like Continuous Renal Replacement Therapy (CRRT) are scarce. The technicality of how to implement AI remains elusive.

Objective

This study aims to elucidate the important factors and methods that are required to develop effective predictive models of AKI and CRRT for patients admitted to ICU, using EHRs in the Medical Information Mart for Intensive Care (MIMIC) database.

Methods

We conducted a comprehensive comparative analysis of established predictive models, considering both time-series measurements and clinical notes from MIMIC-IV databases. Subsequently, we proposed a novel multi-modal model which integrates embeddings of top-performing unimodal models, including Long Short-Term Memory (LSTM) and BioMedBERT, and leverages both unstructured clinical notes and structured time series measurements derived from EHRs to enable the early prediction of AKI and CRRT.

Results

Our multimodal model achieved a lead time of at least 12 hours ahead of clinical manifestation, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.888 for AKI and 0.997 for CRRT, as well as an Area Under the Precision Recall Curve (AUPRC) of 0.727 for AKI and 0.840 for CRRT, respectively, which significantly outperformed the baseline models. Additionally, we performed a SHapley Additive exPlanation (SHAP) analysis using the expected gradients algorithm, which highlighted important, previously underappreciated predictive features for AKI and CRRT.

Conclusion

Our study revealed the importance and the technicality of applying longitudinal, multimodal modeling to improve early prediction of AKI and CRRT, offering insights for timely interventions. The performance and interpretability of our model indicate its potential for further assessment towards clinical applications, to ultimately optimize AKI management and enhance patient outcomes.

Label-aware distance mitigates temporal and spatial variability for clustering and visualization of single-cell gene expression data

Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. The batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Label-Aware Distance (LAD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate LAD on simulated data as well as apply it to a mouse retina development dataset and a lung dataset. We also found the utility of our approach in understanding the progression of the Coronavirus Disease 2019 (COVID-19). LAD provides better cell embedding than state-of-the-art batch correction methods on longitudinal datasets. It can be used in distance-based clustering and visualization methods to combine the power of multiple samples to help make biological findings.

Safety, efficacy and determinants of response of allogeneic CD19-specific CAR-NK cells in CD19+ B cell tumors: a phase 1/2 trial

There is a pressing need for allogeneic chimeric antigen receptor (CAR)-immune cell therapies that are safe, effective and affordable. We conducted a phase 1/2 trial of cord blood-derived natural killer (NK) cells expressing anti-CD19 chimeric antigen receptor and interleukin-15 (CAR19/IL-15) in 37 patients with CD19+ B cell malignancies. The primary objectives were safety and efficacy, defined as day 30 overall response (OR). Secondary objectives included day 100 response, progression-free survival, overall survival and CAR19/IL-15 NK cell persistence. No notable toxicities such as cytokine release syndrome, neurotoxicity or graft-versus-host disease were observed. The day 30 and day 100 OR rates were 48.6% for both. The 1-year overall survival and progression-free survival were 68% and 32%, respectively. Patients who achieved OR had higher levels and longer persistence of CAR-NK cells. Receiving CAR-NK cells from a cord blood unit (CBU) with nucleated red blood cells ≤ 8 × 107 and a collection-to-cryopreservation time ≤ 24 h was the most significant predictor for superior outcome. NK cells from these optimal CBUs were highly functional and enriched in effector-related genes. In contrast, NK cells from suboptimal CBUs had upregulation of inflammation, hypoxia and cellular stress programs. Finally, using multiple mouse models, we confirmed the superior antitumor activity of CAR/IL-15 NK cells from optimal CBUs in vivo. These findings uncover new features of CAR-NK cell biology and underscore the importance of donor selection for allogeneic cell therapies. ClinicalTrials.gov identifier: NCT03056339 .

A Validation Study of CD133 as a Reliable Marker for Identification of Colorectal Cancer Stem-Like Cells

Colorectal carcinoma (CRC) is maintained by putative colorectal cancer stem-like cells (CRC-CSCs) that are responsible for CRC metastasis and relapse. Targeting these CSCs can be an effective treatment of CRC. However, reliable identification of CRC-CSCs remains controversial due to the absence of specific markers. It is assumed that glycoprotein CD133 can serve as a useful marker for identification of CRC-CSCs. In this study, we employed CD133 as a marker to identify CRC-CSCs in human (LoVo, HCT116, and SW620) and mouse (CT26) CRC cell lines. In these lines, CD133+ cells were isolated and identified by magnetic-activated cell sorting and flow cytometry. Proliferation, colony formation, and drug resistance of CD133+ cells were analyzed in vitro, and their tumorigenicity was determined in vivo on mice. Proliferation, colony-forming ability, drug resistance, and tumorigenicity of CD133+ cells were higher than those of CD133- cells. Thus, cultured CD133+ cells had the characteristics of CSCs. Hence, glycoprotein CD133 is a reliable marker to identify CRC-CSCs. These results can be used for designing a novel therapeutic target in CRC treatment.

Temporal recording of mammalian development and precancer

Key to understanding many biological phenomena is knowing the temporal ordering of cellular events, which often require continuous direct observations [1, 2]. An alternative solution involves the utilization of irreversible genetic changes, such as naturally occurring mutations, to create indelible markers that enables retrospective temporal ordering [3-8]. Using NSC-seq, a newly designed and validated multi-purpose single-cell CRISPR platform, we developed a molecular clock approach to record the timing of cellular events and clonality in vivo , while incorporating assigned cell state and lineage information. Using this approach, we uncovered precise timing of tissue-specific cell expansion during murine embryonic development and identified new intestinal epithelial progenitor states by their unique genetic histories. NSC-seq analysis of murine adenomas and single-cell multi-omic profiling of human precancers as part of the Human Tumor Atlas Network (HTAN), including 116 scRNA-seq datasets and clonal analysis of 418 human polyps, demonstrated the occurrence of polyancestral initiation in 15-30% of colonic precancers, revealing their origins from multiple normal founders. Thus, our multimodal framework augments existing single-cell analyses and lays the foundation for in vivo multimodal recording, enabling the tracking of lineage and temporal events during development and tumorigenesis.

Single-nucleotide variant calling in single-cell sequencing data with Monopogen

Single-cell omics technologies enable molecular characterization of diverse cell types and states, but how the resulting transcriptional and epigenetic profiles depend on the cell's genetic background remains understudied. We describe Monopogen, a computational tool to detect single-nucleotide variants (SNVs) from single-cell sequencing data. Monopogen leverages linkage disequilibrium from external reference panels to identify germline SNVs and detects putative somatic SNVs using allele cosegregating patterns at the cell population level. It can identify 100 K to 3 M germline SNVs achieving a genotyping accuracy of 95%, together with hundreds of putative somatic SNVs. Monopogen-derived genotypes enable global and local ancestry inference and identification of admixed samples. It identifies variants associated with cardiomyocyte metabolic levels and epigenomic programs. It also improves putative somatic SNV detection that enables clonal lineage tracing in primary human clonal hematopoiesis. Monopogen brings together population genetics, cell lineage tracing and single-cell omics to uncover genetic determinants of cellular processes.

Loss of metabolic fitness drives tumor resistance after CAR-NK cell therapy and can be overcome by cytokine engineering

Chimeric antigen receptor (CAR) engineering of natural killer (NK) cells is promising, with early-phase clinical studies showing encouraging responses. However, the transcriptional signatures that control the fate of CAR-NK cells after infusion and factors that influence tumor control remain poorly understood. We performed single-cell RNA sequencing and mass cytometry to study the heterogeneity of CAR-NK cells and their in vivo evolution after adoptive transfer, from the phase of tumor control to relapse. Using a preclinical model of noncurative lymphoma and samples from a responder and a nonresponder patient treated with CAR19/IL-15 NK cells, we observed the emergence of NK cell clusters with distinct patterns of activation, function, and metabolic signature associated with different phases of in vivo evolution and tumor control. Interaction with the highly metabolically active tumor resulted in loss of metabolic fitness in NK cells that could be partly overcome by incorporation of IL-15 in the CAR construct.

Batch-Corrected Distance Mitigates Temporal and Spatial Variability for Clustering and Visualization of Single-Cell Gene Expression Data

Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. The batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Batch-Corrected Distance (BCD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate BCD on simulated data as well as applied it to a mouse retina development dataset and a lung dataset. We also found the utility of our approach in understanding the progression of the Coronavirus Disease 2019 (COVID-19). BCD achieves more accurate clusters and better visualizations than state-of-the-art batch correction methods on longitudinal datasets. BCD can be directly integrated with most clustering and visualization methods to enable more scientific findings.

Direct CD32 T-cell cytotoxicity: implications for breast cancer prognosis and treatment

The FcγRII (CD32) ligands are IgFc fragments and pentraxins. The existence of additional ligands is unknown. We engineered T cells with human chimeric receptors resulting from the fusion between CD32 extracellular portion and transmembrane CD8α linked to CD28/ζ chain intracellular moiety (CD32-CR). Transduced T cells recognized three breast cancer (BC) and one colon cancer cell line among 15 tested in the absence of targeting antibodies. Sensitive BC cell conjugation with CD32-CR T cells induced CD32 polarization and down-regulation, CD107a release, mutual elimination, and proinflammatory cytokine production unaffected by human IgGs but enhanced by cetuximab. CD32-CR T cells protected immunodeficient mice from subcutaneous growth of MDA-MB-468 BC cells. RNAseq analysis identified a 42 gene fingerprint predicting BC cell sensitivity and favorable outcomes in advanced BC. ICAM1 was a major regulator of CD32-CR T cell-mediated cytotoxicity. CD32-CR T cells may help identify cell surface CD32 ligand(s) and novel prognostically relevant transcriptomic signatures and develop innovative BC treatments.

KLF5 governs sphingolipid metabolism and barrier function of the skin

Stem cells are fundamental units of tissue remodeling whose functions are dictated by lineage-specific transcription factors. Home to epidermal stem cells and their upward-stratifying progenies, skin relies on its secretory functions to form the outermost protective barrier, of which a transcriptional orchestrator has been elusive. KLF5 is a Krüppel-like transcription factor broadly involved in development and regeneration whose lineage specificity, if any, remains unclear. Here we report KLF5 specifically marks the epidermis, and its deletion leads to skin barrier dysfunction in vivo. Lipid envelopes and secretory lamellar bodies are defective in KLF5-deficient skin, accompanied by preferential loss of complex sphingolipids. KLF5 binds to and transcriptionally regulates genes encoding rate-limiting sphingolipid metabolism enzymes. Remarkably, skin barrier defects elicited by KLF5 ablation can be rescued by dietary interventions. Finally, we found that KLF5 is widely suppressed in human diseases with disrupted epidermal secretion, and its regulation of sphingolipid metabolism is conserved in human skin. Altogether, we established KLF5 as a disease-relevant transcription factor governing sphingolipid metabolism and barrier function in the skin, likely representing a long-sought secretory lineage-defining factor across tissue types.

The inhibitory effect of LINC00261 upregulation on the pancreatic cancer EMT process is mediated by KLF13 via the mTOR signaling pathway

PURPOSE

The long noncoding RNA LINC00261 was reported to be involved in carcinogenesis and has been validated as a tumor suppressor in pancreatic cancer (PC); however, how LINC00261 is regulated has not been fully examined. Here, we attempted to investigate the upstream and downstream targets of LINC00261 in PC.

METHODS

LINC00261 expression in PC tissues was examined by the Gene Expression Omnibus (GEO) datasets and the Gene Expression Profiling Interactive Analysis (GEPIA) database. The quantitative reverse transcription polymerase chain reaction (qRT-PCR) assays were performed to detect the expression level of LINC00261 in PC cells. The location of LINC00261 in PC cells was identified by RNA fluorescence in situ hybridization (RNA-FISH). Cell Counting Kit-8 (CCK-8), cell apoptosis assay, transwell invasion and migration assays testified the critical role of LINC00261 in PC. The luciferase reporter assay was applied to confirm the binding of LINC00261 to its upstream transcription factor KLF13. The changes in LINC00261 related target protein levels were analyzed by Western blotting assay.

RESULTS

LINC00261 was significantly lower in PC tissues and was mainly concentrated in the nucleus. Overexpression of LINC00261 inhibited the invasion and migration of PC cells. Mechanistically, transcription factor KLF13 was confirmed to inhibit the epithelial-mesenchymal transition (EMT) process of PC cells by promoting the transcription of LINC00261 and suppressing the expression of metastasis-associated proteins, such as matrix metalloproteinase MMP2 and vimentin, thus inhibiting the metastasis of PC.

CONCLUSION

LINC00261 regulates PC cell metastasis through the "KLF13-LINC00261-mTOR-P70S6K1-S6" signaling pathway, which provides a significant set of potential PC therapeutic targets.

Bi-order multimodal integration of single-cell data

Integration of single-cell multiomics profiles generated by different single-cell technologies from the same biological sample is still challenging. Previous approaches based on shared features have only provided approximate solutions. Here, we present a novel mathematical solution named bi-order canonical correlation analysis (bi-CCA), which extends the widely used CCA approach to iteratively align the rows and the columns between data matrices. Bi-CCA is generally applicable to combinations of any two single-cell modalities. Validations using co-assayed ground truth data and application to a CAR-NK study and a fetal muscle atlas demonstrate its capability in generating accurate multimodal co-embeddings and discovering cellular identity.

Systematic decomposition of sequence determinants governing CRISPR/Cas9 specificity

The specificity of CRISPR/Cas9 genome editing is largely determined by the sequences of guide RNA (gRNA) and the targeted DNA, yet the sequence-dependent rules underlying off-target effects are not fully understood. To systematically explore the sequence determinants governing CRISPR/Cas9 specificity, here we describe a dual-target system to measure the relative cleavage rate between off- and on-target sequences (off-on ratios) of 1902 gRNAs on 13,314 synthetic target sequences, and reveal a set of sequence rules involving 2 factors in off-targeting: 1) a guide-intrinsic mismatch tolerance (GMT) independent of the mismatch context; 2) an "epistasis-like" combinatorial effect of multiple mismatches, which are associated with the free-energy landscape in R-loop formation and are explainable by a multi-state kinetic model. These sequence rules lead to the development of MOFF, a model-based predictor of Cas9-mediated off-target effects. Moreover, the "epistasis-like" combinatorial effect suggests a strategy of allele-specific genome editing using mismatched guides. With the aid of MOFF prediction, this strategy significantly improves the selectivity and expands the application domain of Cas9-based allele-specific editing, as tested in a high-throughput allele-editing screen on 18 cancer hotspot mutations.

Sensei: how many samples to tell a change in cell type abundance?

Cellular heterogeneity underlies cancer evolution and metastasis. Advances in single-cell technologies such as single-cell RNA sequencing and mass cytometry have enabled interrogation of cell type-specific expression profiles and abundance across heterogeneous cancer samples obtained from clinical trials and preclinical studies. However, challenges remain in determining sample sizes needed for ascertaining changes in cell type abundances in a controlled study. To address this statistical challenge, we have developed a new approach, named Sensei, to determine the number of samples and the number of cells that are required to ascertain such changes between two groups of samples in single-cell studies. Sensei expands the t-test and models the cell abundances using a beta-binomial distribution. We evaluate the mathematical accuracy of Sensei and provide practical guidelines on over 20 cell types in over 30 cancer types based on knowledge acquired from the cancer cell atlas (TCGA) and prior single-cell studies. We provide a web application to enable user-friendly study design via https://kchen-lab.github.io/sensei/table_beta.html .

Targeting the αv integrin/TGF-β axis improves natural killer cell function against glioblastoma stem cells

Glioblastoma multiforme (GBM), the most aggressive brain cancer, recurs because glioblastoma stem cells (GSCs) are resistant to all standard therapies. We showed that GSCs, but not normal astrocytes, are sensitive to lysis by healthy allogeneic natural killer (NK) cells in vitro. Mass cytometry and single-cell RNA sequencing of primary tumor samples revealed that GBM tumor-infiltrating NK cells acquired an altered phenotype associated with impaired lytic function relative to matched peripheral blood NK cells from patients with GBM or healthy donors. We attributed this immune evasion tactic to direct cell-to-cell contact between GSCs and NK cells via αv integrin-mediated TGF-β activation. Treatment of GSC-engrafted mice with allogeneic NK cells in combination with inhibitors of integrin or TGF-β signaling or with TGFBR2 gene-edited allogeneic NK cells prevented GSC-induced NK cell dysfunction and tumor growth. These findings reveal an important mechanism of NK cell immune evasion by GSCs and suggest the αv integrin/TGF-β axis as a potentially useful therapeutic target in GBM.

Generation of glucocorticoid-resistant SARS-CoV-2 T cells for adoptive cell therapy

Adoptive cell therapy with virus-specific T cells has been used successfully to treat life-threatening viral infections, supporting application of this approach to coronavirus disease 2019 (COVID-19). We expand severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) T cells from the peripheral blood of COVID-19-recovered donors and non-exposed controls using different culture conditions. We observe that the choice of cytokines modulates the expansion, phenotype, and hierarchy of antigenic recognition by SARS-CoV-2 T cells. Culture with interleukin (IL)-2/4/7, but not under other cytokine-driven conditions, results in more than 1,000-fold expansion in SARS-CoV-2 T cells with a retained phenotype, function, and hierarchy of antigenic recognition compared with baseline (pre-expansion) samples. Expanded cytotoxic T lymphocytes (CTLs) are directed against structural SARS-CoV-2 proteins, including the receptor-binding domain of Spike. SARS-CoV-2 T cells cannot be expanded efficiently from the peripheral blood of non-exposed controls. Because corticosteroids are used for management of severe COVID-19, we propose an efficient strategy to inactivate the glucocorticoid receptor gene (NR3C1) in SARS-CoV-2 CTLs using CRISPR-Cas9 gene editing.

Genetic admixture in the culturally unique Peranakan Chinese population in Southeast Asia

The Peranakan Chinese are culturally unique descendants of immigrants from China who settled in the Malay Archipelago ∼300-500 years ago. Today, among large communities in Southeast Asia, the Peranakans have preserved Chinese traditions with strong influence from the local indigenous Malays. Yet, whether or to what extent genetic admixture co-occurred with the cultural mixture has been a topic of ongoing debate. We performed whole-genome sequencing (WGS) on 177 Singapore (SG) Peranakans and analyzed the data jointly with WGS data of Asian and European populations. We estimated that Peranakan Chinese inherited ∼5.62% (95% confidence interval [CI]: 4.75-6.46%) Malay ancestry, much higher than that in SG Chinese (1.08%, 0.69-1.53%), southern Chinese (0.86%, 0.57-1.31%), and northern Chinese (0.25%, 0.18-0.33%). A sex-biased admixture history, in which the Malay ancestry was contributed primarily by females, was supported by X chromosomal variants, and mitochondrial (MT) and Y haplogroups. Finally, we identified an ancient admixture event shared by Peranakan Chinese and SG Chinese ∼1,612 (95% CI: 1,345-1,923) years ago, coinciding with the settlement history of Han Chinese in southern China, apart from the recent admixture event with Malays unique to Peranakan Chinese ∼190 (159-213) years ago. These findings greatly advance our understanding of the dispersal history of Chinese and their interaction with indigenous populations in Southeast Asia.

MiR-543 inhibits proliferation and metastasis of human colorectal cancer cells by targeting PLAS3

OBJECTIVE

Colorectal cancer (CRC) has a very high morbidity and mortality worldwide. Related studies have shown that microRNA-543 (miR-543) is involved in the development of many cancers, including CRC. The purpose of this study was to explore the potential molecular mechanism of miR-543's involvement in the development of CRC.

PATIENTS AND METHODS

QRT-PCR and Western blot were used to detect the expression of proliferation and migration-related proteins, signal transduction and transcriptional activator 3 and protein inhibitor of activated signal transducer and activators of transcription 3 (PIAS3). Cell proliferation and metastasis were measured by MTT, transwell and Western blot. The binding sites of miR-543 and PIAS3 were predicted by TargetScan database and verified by double-luciferase report experiment.

RESULTS

The expression of miR-543 was high in CRC tissues and cell lines, while the mRNA and protein levels of PIAS3 were decreased. Meanwhile, a negative correlation between miR-543 and PIAS3 was also observed in CRC tissues. Moreover, the downregulation of miR-543 led to the inhibition of viability and the expression of proliferation and migration related proteins. Subsequently, miR-543 depletion also blocked cell migration and invasion. MiR-543 inhibits the expression of PISA3. Furthermore, downregulation of PIAS3 undermined the miR-543 depletion-mediated suppression effect on SW480 and LOVO cells. Notably, loss of miR-543 downregulated STAT3 activity, which was rescued by PIAS3 ablation.

CONCLUSIONS

MiR-543 participated in cell proliferation and metastasis by targeting PIAS3 in CRC.

Single-cell manifold-preserving feature selection for detecting rare cell populations

A key challenge in studying organisms and diseases is to detect rare molecular programs and rare cell populations (RCPs) that drive development, differentiation, and transformation. Molecular features such as genes and proteins defining RCPs are often unknown and difficult to detect from unenriched single-cell data, using conventional dimensionality reduction and clustering-based approaches. Here, we propose an unsupervised approach, SCMER (Single-Cell Manifold presERving feature selection), which selects a compact set of molecular features with definitive meanings that preserve the manifold of the data. We applied SCMER in the context of hematopoiesis, lymphogenesis, tumorigenesis, and drug resistance and response. We found that SCMER can identify non-redundant features that sensitively delineate both common cell lineages and rare cellular states. SCMER can be used for discovering molecular features in a high dimensional dataset, designing targeted, cost-effective assays for clinical applications, and facilitating multi-modality integration.

Genetic parameters of hair cortisol as an indicator of chronic stress under different environments in Holstein cows

Chronic stress is a risk factor for a variety of physiological disorders because of its increased activation of the hypothalamic-pituitary-adrenal (HPA) axis; however, it is difficult to reveal environmental and genetic effects contributing to long-term HPA activity because of the complexity of chronic stress. The hair cortisol concentration (HCC) can be used to reflect the accumulation of HPA axis activity over time. Some studies suggest that the HCC might be associated with the protein concentration (PC) in the hair shaft; however, no studies have revealed a dynamic relationship between them. In the present study, 1,086 hair samples from 418 Holstein cows were collected, and the effects of environmental factors on HCC, PC, and ratio of HCC to PC (HCCP) were studied. Subsequently, regression analysis and curve fitting were used to identify for better-performing indicators of chronic stress. Additionally, univariate and bivariate genetic evaluation were used to estimate the genetic components of cortisol traits and genotype by environment interactions (G × E) under different environmental and physiological states. The results showed that HCC and PC are significantly affected by hair color, sampling year, and season, whereas HCCP is not influenced by hair color. Adjusted PC and HCCP, where confounding effects are excluded, were moderately related with chronic stress indicators. Moderate to high heritabilities were obtained for HCC (0.347 and 0.390 for winter and summer, respectively), PC (0.402 and 0.495 for winter and summer, respectively) and HCCP (0.289 and 0.460 for winter and summer, respectively) when animals in the same season were evaluated. A moderate G × E interaction was detected in this study, as indicated by the low or negative genetic correlation for the same cortisol trait in different environments (e.g. heat stress condition and thermoneutral condition). In conclusion, HCCP is not affected by hair color compared with the other 2 traits; thus, it has potential as an indicator of chronic stress. Hair cortisol traits could monitor stress response process in cattle, as well as provide a better understanding of genetic mechanism for long-term HPA activity.

Genetic parameters for rectal temperature, respiration rate, and drooling score in Holstein cattle and their relationships with various fertility, production, body conformation, and health traits

Genetic selection for improved climatic resilience is paramount to increase the long-term sustainability of high-producing dairy cattle, especially in face of climate change. Various physiological indicators, such as rectal temperature (RT), respiration rate score (RR), and drooling score (DS), can be used to genetically identify animals with more effective coping mechanisms in response to heat stress events. In this study, we investigated genetic parameters for RT, RR (score from 1-3), and DS (score from 1-3). Furthermore, we assessed the genetic relationship among these indicators and other economically important traits for the dairy cattle industry. After data editing, 59,265 (RT), 30,290 (RR), and 30,421 (DS) records from 13,592 lactating Holstein cows were used for the analyses. Variance components were estimated based on a multiple-trait repeatability animal model. The heritability ± standard error estimate for RT, RR, and DS was 0.06 ± 0.01, 0.04 ± 0.01, and 0.02 ± 0.01, respectively, whereas their repeatability was 0.19, 0.14, and 0.14, respectively. Moderate genetic correlations of RR with RT and DS (0.26 ± 0.11 and 0.25 ± 0.16) and nonsignificant correlation between RT and DS (-0.11 ± 0.14) were observed. Furthermore, the approximate genetic correlations between RT, RR, and DS with 12 production, 29 conformation, 5 fertility and reproduction, 5 health, and 9 longevity-indicator traits were assessed. In general, the approximate genetic correlations calculated were low to moderate. In summary, 3 physiological indicators of heat stress response were measured in a large number of animals and shown to be lowly heritable. There is a value in developing a selection index including all the 3 indicators to improve heat tolerance in dairy cattle. All the unfavorable genetic relationships observed between heat tolerance and other economically important traits can be accounted for in a selection index to enable improved climatic resilience while also maintaining or increasing productivity in Holstein cattle.

MEDALT: single-cell copy number lineage tracing enabling gene discovery

We present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles, and a statistical routine named lineage speciation analysis (LSA), whichty facilitates discovery of fitness-associated alterations and genes from SCCN lineage trees. MEDALT appears more accurate than phylogenetics approaches in reconstructing copy number lineage. From data from 20 triple-negative breast cancer patients, our approaches effectively prioritize genes that are essential for breast cancer cell fitness and predict patient survival, including those implicating convergent evolution.The source code of our study is available at https://github.com/KChen-lab/MEDALT .

Ab initio spillover compensation in mass cytometry data

Signal intensity measured in a mass cytometry (CyTOF) channel can often be affected by the neighboring channels due to technological limitations. Such signal artifacts are known as spillover effects and can substantially limit the accuracy of cell population clustering. Current approaches reduce these effects by using additional beads for normalization purposes known as single-stained controls. While effective in compensating for spillover effects, incorporating single-stained controls can be costly and require customized panel design. This is especially evident when executing large-scale immune profiling studies. We present a novel statistical method, named CytoSpill that independently quantifies and compensates the spillover effects in CyTOF data without requiring the use of single-stained controls. Our method utilizes knowledge-guided modeling and statistical techniques, such as finite mixture modeling and sequential quadratic programming, to achieve optimal error correction. We evaluated our method using five publicly available CyTOF datasets obtained from human peripheral blood mononuclear cells (PBMCs), C57BL/6J mouse bone marrow, healthy human bone marrow, chronic lymphocytic leukemia patient, and healthy human cord blood samples. In the PBMCs with known ground truth, our method achieved comparable results to experiments that incorporated single-stained controls. In datasets without ground-truth, our method not only reduced spillover on likely affected markers, but also led to the discovery of potentially novel subpopulations expressing functionally meaningful, cluster-specific markers. CytoSpill (developed in R) will greatly enhance the execution of large-scale cellular profiling of tumor immune microenvironment, development of novel immunotherapy, and the discovery of immune-specific biomarkers. The implementation of our method can be found at https://github.com/KChen-lab/CytoSpill.git.

Genome-Wide Association for HbA1c in Malay Identified Deletion on SLC4A1 that Influences HbA1c Independent of Glycemia

CONTEXT

Glycated hemoglobin A1c (HbA1c) level is used to screen and diagnose diabetes. Genetic determinants of HbA1c can vary across populations and many of the genetic variants influencing HbA1c level were specific to populations.

OBJECTIVE

To discover genetic variants associated with HbA1c level in nondiabetic Malay individuals.

DESIGN AND PARTICIPANTS

We conducted a genome-wide association study (GWAS) analysis for HbA1c using 2 Malay studies, the Singapore Malay Eye Study (SiMES, N = 1721 on GWAS array) and the Living Biobank study (N = 983 on GWAS array and whole-exome sequenced). We built a Malay-specific reference panel to impute ethnic-specific variants and validate the associations with HbA1c at ethnic-specific variants.

RESULTS

Meta-analysis of the 1000 Genomes imputed array data identified 4 loci at genome-wide significance (P < 5 × 10-8). Of the 4 loci, 3 (ADAM15, LINC02226, JUP) were novel for HbA1c associations. At the previously reported HbA1c locus ATXN7L3-G6PC3, association analysis using the exome data fine-mapped the HbA1c associations to a 27-bp deletion (rs769664228) at SLC4A1 that reduced HbA1c by 0.38 ± 0.06% (P = 3.5 × 10-10). Further imputation of this variant in SiMES confirmed the association with HbA1c at SLC4A1. We also showed that these genetic variants influence HbA1c level independent of glucose level.

CONCLUSION

We identified a deletion at SLC4A1 associated with HbA1c in Malay. The nonglycemic lowering of HbA1c at rs769664228 might cause individuals carrying this variant to be underdiagnosed for diabetes or prediabetes when HbA1c is used as the only diagnostic test for diabetes.

Abstract PO-017: Single-cell copy number heterogeneity tracing enabling cancer gene discovery

Aneuploidy, the phenomenon that genomes acquire or lose chromosomal fragments, has been causally implicated in a wide variety of human cancer. Defining which copy number alterations (CNAs) are pathogenic is an important goal of cancer research. However, data based on bulk samples cannot fully depict tumor heterogeneity and evolution, which occurs in single cells, and thus have limited power to discover CNAs useful for cancer diagnostics and therapeutics. Recent advances in single-cell DNA sequencing have enabled acquisition of single-cell copy number (SCCN) profiles in tens of thousands of cells, which potentiate reconstruction of copy number evolution lineage and discovery of novel cancer genes. However, current analytical approaches that infer clonal lineages are neither accurate, nor scalable to analyzing molecular features, particularly CNA profiles obtained from thousands of cells. Statistical routines have not been established to leverage lineage tracing results towards identifying cancer-related genes. Here, we present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles. In a MEDALT, each node represents a cell, each edge represents a kinship between two cells, arrows point towards younger cells, and the root represents a normal diploid cell. We also present a statistical routine named lineage speciation analysis, which facilitates discovery of fitness-associated alterations (FAAs) and genes from SCCN lineage trees. To evaluate MEDALT, we simulated copy number evolution under various CNA mechanisms such as genome doubling, breakage-fusion-bridge (BFB), etc. and spiked in FAAs. We found that MEDALT substantially improved accuracy of FAA identification over GISTIC and conventional phylogenetics methods such as maximum likelihood (ML), maximal parsimony (MP) and neighbor joining (NJ) trees. We applied our methods on the single-cell DNA-sequencing data acquired from 20 triple-negative breast cancer patients (TNBCs), 4 of which had longitudinal pre-, mid- and post- treatment samples. Most of the TNBC samples appeared to have developed through branched evolution via multiple parallel lineages with distinct mutation rates and DNA damage repair (DDR) loss based on the constructed MEDALT. Using our approaches, we discovered novel genes that are predictive of patient survival in TCGA breast cancer data and are functionally more essential than other control gene sets, based on the CRISPR-cas9 knockout screen data obtained from 27 breast cancer cell lines in the DepMap database. Significant benefits in lineage tracing and cancer gene discovery were also achieved, when applying our approaches on the SCCN profiles derived using InferCNV from the single-cell RNA sequencing data of a cohort of 20 multiple myeloma, head and neck cancer, oral squamous cell carcinoma and ovarian cancer patients. The source code of our study is available at https://github.com/KChen-lab/MEDALT.

Citation Format: Fang Wang, Qihan Wang, Vakul Mohanty, Shaoheng Liang, Jinzhuang Dou, Jincheng Han, Darlan Minussi, Ruli Gao, Li Ding, Nicholas Navin, Ken Chen. Single-cell copy number heterogeneity tracing enabling cancer gene discovery [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-017.

Batch-Corrected Distance Mitigates Temporal and Spatial Variability for Clustering and Visualization of Single-Cell Gene Expression Data

Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. Batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Batch-Corrected Distance (BCD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate BCD on a simulated data as well as applied it to a mouse retina development dataset and a lung dataset. We also found the utility of our approach in understanding the progression of the Coronavirus Disease 2019 (COVID-19). BCD achieves more accurate clusters and better visualizations than state-of-the-art batch correction methods on longitudinal datasets. BCD can be directly integrated with most clustering and visualization methods to enable more scientific findings.

Generation of glucocorticoid resistant SARS-CoV-2 T-cells for adoptive cell therapy

Adoptive cell therapy with viral-specific T cells has been successfully used to treat life-threatening viral infections, supporting the application of this approach against COVID-19. We expanded SARS-CoV-2 T-cells from the peripheral blood of COVID-19-recovered donors and non-exposed controls using different culture conditions. We observed that the choice of cytokines modulates the expansion, phenotype and hierarchy of antigenic recognition by SARS-CoV-2 T-cells. Culture with IL-2/4/7 but not other cytokine-driven conditions resulted in >1000 fold expansion in SARS-CoV-2 T-cells with a retained phenotype, function and hierarchy of antigenic recognition when compared to baseline (pre-expansion) samples. Expanded CTLs were directed against structural SARS-CoV-2 proteins, including the receptor-binding domain of Spike. SARS-CoV-2 T-cells could not be efficiently expanded from the peripheral blood of non-exposed controls. Since corticosteroids are used for the management of severe COVID-19, we developed an efficient strategy to inactivate the glucocorticoid receptor gene ( NR3C1 ) in SARS-CoV-2 CTLs using CRISPR-Cas9 gene editing.

Using off-target data from whole-exome sequencing to improve genotyping accuracy, association analysis and polygenic risk prediction

Whole-exome sequencing (WES) has been widely used to study the role of protein-coding variants in genetic diseases. Non-coding regions, typically covered by sparse off-target data, are often discarded by conventional WES analyses. Here, we develop a genotype calling pipeline named WEScall to analyse both target and off-target data. We leverage linkage disequilibrium shared within study samples and from an external reference panel to improve genotyping accuracy. In an application to WES of 2527 Chinese and Malays, WEScall can reduce the genotype discordance rate from 0.26% (SE= 6.4 × 10-6) to 0.08% (SE = 3.6 × 10-6) across 1.1 million single nucleotide polymorphisms (SNPs) in the deeply sequenced target regions. Furthermore, we obtain genotypes at 0.70% (SE = 3.0 × 10-6) discordance rate across 5.2 million off-target SNPs, which had ~1.2× mean sequencing depth. Using this dataset, we perform genome-wide association studies of 10 metabolic traits. Despite of our small sample size, we identify 10 loci at genome-wide significance (P < 5 × 10-8), including eight well-established loci. The two novel loci, both associated with glycated haemoglobin levels, are GPATCH8-SLC4A1 (rs369762319, P = 2.56 × 10-12) and ROR2 (rs1201042, P = 3.24 × 10-8). Finally, using summary statistics from UK Biobank and Biobank Japan, we show that polygenic risk prediction can be significantly improved for six out of nine traits by incorporating off-target data (P < 0.01). These results demonstrate WEScall as a useful tool to facilitate WES studies with decent amounts of off-target data.

Rapid isolation of non-aflatoxigenic Aspergillus flavus strains

In the present study, a method for screening non-aflatoxigenic Aspergillus flavus in soil samples collected from major peanut-growing regions of China was developed. The single colonies were picked and cultured on Aspergillus flavus and parasiticus agar (AFPA). If the reverse side of the colony on AFPA was orange-coloured, it was considered A. flavus or Aspergillus parasiticus. After the genomic DNA of each strain was extracted, 28S rRNA and calmodulin were amplified and sequenced to determine the species. The key gene, aflR, was amplified and digested via polymerase chain reaction-restriction fragment length polymorphism. The aflatoxigenic A. flavus and the non-aflatoxigenic A. flavus and A. parasiticus were distinguished by enzyme digestion of aflR. 156 strains of A. flavus were screened, which consisted of 135 aflatoxigenic and 21 non-aflatoxigenic strains. The aflatoxin producing ability of each strain was confirmed using solid-state fermentation experiments. Using the method developed in the present study, we confirmed that the non-aflatoxigenic A. flavus strains isolated lost their capacity to produce aflatoxins. Considering there could be some alterations in other functional genes, some non-aflatoxigenic strains could be identified inaccurately as aflatoxigenic strains, although that did not occur in the present study. The growth of non-aflatoxigenic A. flavus was observed, and the most rapidly growing non-aflatoxigenic strain was selected for plate confrontation assays and toxic mixed culture experiments. The inhibition rate of non-aflatoxigenic A. flavus against aflatoxigenic A. flavus was 55.4 and 72.6% in potato dextrose agar (PDA) plate and natural soybean medium, respectively. The screened non-aflatoxigenic A. flavus strains provide a microbial resource for biological control of aflatoxin contamination.

DNA methylation signature of smoking in lung cancer is enriched for exposure signatures in newborn and adult blood

Smoking impacts DNA methylation genome-wide in blood of newborns from maternal smoking during pregnancy and adults from personal smoking. We compared smoking-related DNA methylation in lung adenocarcinoma (61 never smokers, 91 current smokers, and 238 former smokers) quantified with the Illumina450k BeadArray in The Cancer Genome Atlas with published large consortium meta-analyses of newborn and adult blood. We assessed whether CpG sites related to smoking in blood from newborns and adults were enriched in the lung adenocarcinoma methylation signal. Testing CpGs differentially methylated by smoke exposure, we identified 296 in lung adenocarcinoma meeting a P < 10-4 cutoff, while previous meta-analyses identified 3,042 in newborn blood, and 8,898 in adult blood meeting the same P < 10-4 cutoff. Lung signals were highly enriched for those seen in newborn (24 overlapping CpGs, Penrichment = 1.2 × 10-18) and adult blood (66 overlapping CpGs, Penrichment = 1.2 × 10-48). The 105 genes annotated to CpGs differentially methylated in lung tumors, but not blood, were enriched for RNA processing ontologies. Some epigenetic alterations associated with cigarette smoke exposure are tissue specific, but others are common across tissues. These findings support the value of blood-based methylation biomarkers for assessing exposure effects in target tissues.

Abstract P1-02-10: Early stage breast cancer screening using an emerging novel liquid biopsy screening technology

Background: An emerging novel liquid biopsy technology called Cancer Differentiation Analysis (CDA) has been evaluated as a viable early stage breast cancer screening tool. CDA technology is a blood-sample based, multi-level, multi-parameter diagnostic method which detects signals from both protein, cellular, and to some extent, molecular levels, in which multiple aspects of information can be collected to improve diagnostic accuracy, even for early stage of cancer. Improving capability to screen breast cancer is an important on-going research effort, as breast cancer represents a leading cancer with high incidence rate.

Methods: In this single-blind study, 22 breast cancer patients and 25 healthy individuals were recruited at Changhai Hospital of Shanghai. Histopathological examination results of breast cancer patients were collected, 22 cases were diagnosed as infiltrating ductal carcinoma of breast, of which 10 patients were stage I breast cancer. 25 individuals were confirmed healthy after physical examinations. Peripheral blood was drawn in EDTA tubes For CDA tests. CDA data of 22 breast cancer patients and 25 healthy individuals were conducted using SPSS, and the results were shown in the table below.

Results: The average CDA of breast cancer, stageIbreast cancer, and controls were 43.20, 44.17 and 36.17 (rel. units) respectively as shown in Table 1. Both breast cancer and stage I breast cancer could be significantly distinguished from the control (p = 0.000, p = 0.001, respectively). For stage I breast cancer vs. control group, Area under ROC curve was 0.876, sensitivity and specificity were both 80.0% (Table 2). In contrast to traditional breast cancer screening methodologies which have relatively low sensitivity and high false positives for stage I detection, often with radiation side effects and high costs, advantages of CDA technology include ability to detect early stage cancer with relatively high sensitivity and specificity, and it is also highly cost effective without side effects.

Conclusions: Initial results showed that CDA technology could effectively distinguish stageIbreast cancer from healthy individuals, CDA could be a potential candidate for breast cancer screening.

Table 1Summary of CDA test resultsGroupSample SizeAge RangeAge MeanAge MedianCDA Mean (rel. units)CDA Median (rel. units)CDA STDEVControl2523 - 67413735.6336.176.98Breast Cancer2239 - 78545343.2042.304.18Stage I Breast Cancer1043 - 78595944.1743.254.29Stage II Breast Cancer839 - 55474941.2840.303.06Stage III Breast Cancer255555542.2042.202.12Stage IV Breast Cancer251 - 64585847.0047.007.78

Table 2AUC, Sensitivity and Specificity of Control vs. Stage I breast cancerStage I Breast Cancer vs. ControlArea Under the CurveSensitivitySpecificity 0.87680.0%80.0%

Citation Format: Liu C, Dou J, Sheng Y, Wu J, Hu W, Li Y, Lin Y, Tao H, Tang X, Du X, Yu C. Early stage breast cancer screening using an emerging novel liquid biopsy screening technology [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P1-02-10.

Abstract P1-02-09: CDA screening technology for multi-ethnic group, early stage breast cancer screening

Background: Breast cancer is the second leading cause of death from cancer in American women. Current breast cancer screening technologies have issues with poor sensitivity for early stage breast cancer, high false positives, radiation side effects, etc. Cancer Differentiation Analysis (CDA) technology is a blood-sample based, multi-level, multi-parameter diagnostic method which detects signals from both proteins, cells, and to some extent, molecular level, in which multiple aspects of information are collected to improve diagnostic accuracy. CDA technology has been investigated as a viable clinical utility in breast cancer screening, particularly for early stage breast screening with clear advantages (both whole blood and serum can be used, ability to detect early, easy, simple, no side effects, and high degree of sensitivity and specificity).

Methods: In this study, the human subjects involved are Caucasians, with serum samples of 44 pathologically confirmed breast cancer patients and 34 healthy individuals from 3 blood bank centers in the USA, of which 40 cases were stageIbreast cancer, 2 cases were stageII, and the other 2 cases were stage III breast cancer. CDA data of 44 breast cancer patients and 34 healthy individuals were collected in US lab and analyzed using SPSS, and the results were shown in the table below. Results from the above study was compared with a clinical study on Asian group with data collected in lab in China using CDA technology.

Results: The average CDA value of all breast cancer and stageIbreast cancer samples, and controls were 45.99, 45.76 and 42.36 (rel. units) respectively (see Table 1). Both breast cancer and stageIbreast cancer could be significantly distinguished from the control group (p &lt; 0.001) (Table 2). For stageIbreast cancer vs. control group, Area under ROC curve was 0.727, sensitivity and specificity were 62.5% and 82.4% respectively, which is higher than a typical mammogram. To compare with different ethnic groups, data collected on an Asian group is also shown in Table 2, which showed that overall, AUC, sensitivity and specificity are comparable (some difference may be attributed to sample type difference (whole blood vs. serum)) for early stage breast cancer patients for those two ethnic groups, demonstrating that CDA technology can be extended to multiple ethnic groups.

Conclusions: CDA screening can be extended to different ethnic group including Caucasian and Asian with good sensitivity and specificity for stageIbreast cancer.

We thank Ugur Basmaci, Sunsil Pandit and Sharon Vorse-Yu for their support.

Table 1Summary of CDA Test ResultsGroupSample SizeAge RangeAge MeanAge MedianCDA Mean (rel. units)CDA Median (rel. units)CDA STDEVControl3436 -79575742.3642.652.75Breast Cancer4436 – 77606145.9946.504.22Stage I Breast Cancer4036 – 77606145.7645.554.26Stage II Breast Cancer251 – 64585847.0547.054.88Stage III Breast Cancer262 – 75696949.5049.502.55

Table 2AUC, Sensitivity and Specificity of Control vs. Stage I Breast CancerStage I Breast Cancer vs. ControlArea Under the CurveSensitivitySpecificityCaucasian (Stage I)0.72762.5%82.4%Asian# (Stage I)0.87680.0%80.0%# Whole blood samples. 10 stage I breast cancer samples and 25 control samples

Citation Format: Tao H, Lin Y, Liu C, Dou J, Sheng Y, Wu J, Hu W, Li Y, Tang X, Yu C, Du X. CDA screening technology for multi-ethnic group, early stage breast cancer screening [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P1-02-09.

Lactobacillus acidophilus and Clostridium butyricum ameliorate colitis in murine by strengthening the gut barrier function and decreasing inflammatory factors

Ulcerative colitis is a type of chronic inflammation present in the intestines for which the aetiology is not yet clear. The current therapies for ulcerative colitis cannot be considered to be long-term management strategies due to their significant side effects. Therefore, it is essential to identify an alternative therapeutic strategy for ulcerative colitis. The present study focused on the evaluation of the anti-inflammatory activities of Lactobacillus acidophilus CGMCC 7282 and Clostridium butyricum CGMCC 7281. The roles of both single and combination of L. acidophilus CGMCC 7282 and C. butyricum CGMCC 7281 in ulcerative colitis were investigated in 2,4,6-trinitrobenzenesulfonic acid-induced acute colitis (Th1-type colitis) in Sprague-Dawley rats and oxazolone-induced chronic colitis (Th2-type colitis) in BALB/c mice. The in vivo studies showed that the administration of L. acidophilus CGMCC 7282, C. butyricum CGMCC 7281 and L. acidophilus CGMCC 7282 plus C. butyricum CGMCC 7281 could reduce the Th1-type colitis as well as the Th2-type colitis, and the combination of the two strains exhibited the most notable effects, as indicated by the reduced mortality rates, the suppressed disease activity indices, the improved body weights, the reduced colon weight/colon length and colon weight/body weight ratios, and the improved gross anatomic characteristics and histological features (ameliorations of neutrophil infiltration and ulceration in the colon). It was found that the alterations of the gut microbiome, the barrier function changing and the selected inflammation-related cytokines are observed in the ulcerative colitis rats/mice treated with L. acidophilus CGMCC 7282 and C. butyricum CGMCC 7281. The combination of L. acidophilus CGMCC 7282 plus C. butyricum CGMCC 7281 also exerted a stronger anti-inflammatory effect than either of the single strains alone in vitro. These findings provide evidence that the administration of L. acidophilus CGMCC 7282 plus C. butyricum CGMCC 7281 may be a promising therapy for ulcerative colitis.

Risk factors of haemoglobinuria after microwave ablation of liver tumours

AIM

To explore the risk factors predicting haemoglobinuria after ultrasound-guided percutaneous microwave ablation (MWA) of liver tumours and discuss the treatments and outcomes.

MATERIALS AND METHODS

The present study comprised 2,829 patients admitted for liver tumours treated with MWA from Jan 2011 to April 2017. Ethics committee approval was waived and informed consent for treatment procedures were obtained from the patients. Haemoglobinuria after MWA was found in 149 patients. The influence of 19 risk factors was assessed. Binary logistic regression and receiver operating characteristic (ROC) curve analysis were used for statistical analysis. The treatments and outcomes of patients with haemoglobinuria were summarised.

RESULTS

By univariate analysis, histopathology, liver cirrhosis, MWA volume, MWA energy, and MWA duration were significant risk factors. By multivariate analysis and ROC curve, MWA energy, duration, and volume were identified as predictors of haemoglobinuria after MWA. Drug treatments including kidney protection, adequate hydration, alkalisation of urine, and diuresis were administrated to the patients with haemoglobinuria. One patient progressed to acute kidney injury (AKI) while others had good clinical outcomes.

CONCLUSION

Haemoglobinuria is a controllable side effect after MWA of liver tumours, which is related to high MWA energy, long MWA duration, and great MWA volume. It usually caused few side effects on renal function with correct treatment.

Integrative analyses reveal biological pathways and key genes in psoriasis

BACKGROUND

Psoriasis is a complex disease influenced by both genetic and environmental factors with abnormal gene expression in lesional skin. However, no studies are available on genome-scale gene expression of psoriatic lesions in the Chinese population. In addition, systematic studies on the biological pathways, pathogenicity and interaction networks of psoriasis-related genes with abnormal expression profiles require further investigation.

OBJECTIVES

To further explore the associated pathways in psoriasis by functional analysis and to identify the key genes by gene pathogenicity analysis.

METHODS

We performed RNA sequencing on 60 skin biopsy samples from patients with psoriasis and healthy controls to identify the primary differentially expressed genes in psoriatic lesional skin. We retrieved all reported psoriasis-associated genes and performed integrative analyses covering gene expression profiling, pathway analysis, gene pathogenicities and protein-protein interaction networks.

RESULTS

We found that internal and external stimuli may activate immunoinflammatory responses to promote the development of psoriasis. Pathways associated with infectious diseases and cancers were identified by functional and pathway analyses. The gene pathogenicity analysis revealed five key genes in psoriasis: PPARD, GATA3, TIMP3, WNT5A and PTTG1.

CONCLUSIONS

Our analyses showed that genes contributed to the pathogenesis of psoriasis by activating risk pathways with components abnormality in expression. We identified five potentially pathogenic genes for psoriasis that may serve as important biomarkers for the diagnosis and treatment.

Estimation of kinship coefficient in structured and admixed populations using sparse sequencing data

Knowledge of biological relatedness between samples is important for many genetic studies. In large-scale human genetic association studies, the estimated kinship is used to remove cryptic relatedness, control for family structure, and estimate trait heritability. However, estimation of kinship is challenging for sparse sequencing data, such as those from off-target regions in target sequencing studies, where genotypes are largely uncertain or missing. Existing methods often assume accurate genotypes at a large number of markers across the genome. We show that these methods, without accounting for the genotype uncertainty in sparse sequencing data, can yield a strong downward bias in kinship estimation. We develop a computationally efficient method called SEEKIN to estimate kinship for both homogeneous samples and heterogeneous samples with population structure and admixture. Our method models genotype uncertainty and leverages linkage disequilibrium through imputation. We test SEEKIN on a whole exome sequencing dataset (WES) of Singapore Chinese and Malays, which involves substantial population structure and admixture. We show that SEEKIN can accurately estimate kinship coefficient and classify genetic relatedness using off-target sequencing data down sampled to ~0.15X depth. In application to the full WES dataset without down sampling, SEEKIN also outperforms existing methods by properly analyzing shallow off-target data (~0.75X). Using both simulated and real phenotypes, we further illustrate how our method improves estimation of trait heritability for WES studies.

Inference of genetic relatedness from molecular markers has broad applications in many areas, including quantitative genetics, forensics, evolution and ecology. Classic estimators, however, are not suitable for low-coverage sequencing data, which have high levels of genotype uncertainty and missing data. We evaluate existing methods and describe a new method for kinship estimation using sparse sequencing data. Our method leverages correlations between neighboring markers and models genotype uncertainty in kinship estimators for both homogeneous populations and admixed populations. We show that our method can accurately estimate kinship coefficient even when the sequencing depth is as low as ~0.15X, while existing methods have strong downward bias. Our method can be applied to estimate kinship using sparse off-target data and thus enables control of family structure and estimation of heritability in target sequencing studies, in which the deeply sequenced target regions are often too small to infer genetic relatedness. Even for whole exome sequencing, we show that our method can improve kinship and heritability estimation by including off-target data, compared to conventional analyses solely based on the target regions.

Scallop genome provides insights into evolution of bilaterian karyotype and development

Reconstructing the genomes of bilaterian ancestors is central to our understanding of animal evolution, where knowledge from ancient and/or slow-evolving bilaterian lineages is critical. Here we report a high-quality, chromosome-anchored reference genome for the scallop Patinopecten yessoensis, a bivalve mollusc that has a slow-evolving genome with many ancestral features. Chromosome-based macrosynteny analysis reveals a striking correspondence between the 19 scallop chromosomes and the 17 presumed ancestral bilaterian linkage groups at a level of conservation previously unseen, suggesting that the scallop may have a karyotype close to that of the bilaterian ancestor. Scallop Hox gene expression follows a new mode of subcluster temporal co-linearity that is possibly ancestral and may provide great potential in supporting diverse bilaterian body plans. Transcriptome analysis of scallop mantle eyes finds unexpected diversity in phototransduction cascades and a potentially ancient Pax2/5/8-dependent pathway for noncephalic eyes. The outstanding preservation of ancestral karyotype and developmental control makes the scallop genome a valuable resource for understanding early bilaterian evolution and biology.

MethylRAD: a simple and scalable method for genome-wide DNA methylation profiling using methylation-dependent restriction enzymes

Characterization of dynamic DNA methylomes in diverse phylogenetic groups has attracted growing interest for a better understanding of the evolution of DNA methylation as well as its function and biological significance in eukaryotes. Sequencing-based methods are promising in fulfilling this task. However, none of the currently available methods offers the 'perfect solution', and they have limitations that prevent their application in the less studied phylogenetic groups. The recently discovered Mrr-like enzymes are appealing for new method development, owing to their ability to collect 32-bp methylated DNA fragments from the whole genome for high-throughput sequencing. Here, we have developed a simple and scalable DNA methylation profiling method (called MethylRAD) using Mrr-like enzymes. MethylRAD allows for de novo (reference-free) methylation analysis, extremely low DNA input (e.g. 1 ng) and adjustment of tag density, all of which are still unattainable for most widely used methylation profiling methods such as RRBS and MeDIP. We performed extensive analyses to validate the power and accuracy of our method in both model (plant Arabidopsis thaliana) and non-model (scallop Patinopecten yessoensis) species. We further demonstrated its great utility in identification of a gene (LPCAT1) that is potentially crucial for carotenoid accumulation in scallop adductor muscle. MethylRAD has several advantages over existing tools and fills a void in the current epigenomic toolkit by providing a universal tool that can be used for diverse research applications, e.g. from model to non-model species, from ordinary to precious samples and from small to large genomes, but at an affordable cost.

Evaluation of the 2b-RAD method for genomic selection in scallop breeding

The recently developed 2b-restriction site-associated DNA (2b-RAD) sequencing method provides a cost-effective and flexible genotyping platform for aquaculture species lacking sufficient genomic resources. Here, we evaluated the performance of this method in the genomic selection (GS) of Yesso scallop (Patinopecten yessoensis) through simulation and real data analyses using six statistical models. Our simulation analysis revealed that the prediction accuracies obtained using the 2b-RAD markers were slightly lower than those obtained using all polymorphic loci in the genome. Furthermore, a small subset of markers obtained from a reduced tag representation (RTR) library presented comparable performance to that obtained using all markers, making RTR be an attractive approach for GS purpose. Six GS models exhibited variable performance in prediction accuracy depending on the scenarios (e.g., heritability, sample size, population structure), but Bayes-alphabet and BLUP-based models generally outperformed other models. Finally, we performed the evaluation using an empirical dataset composed of 349 Yesso scallops that were derived from five families. The prediction accuracy for this empirical dataset could reach 0.4 based on optimal GS models. In summary, the genotyping flexibility and cost-effectiveness make 2b-RAD be an ideal genotyping platform for genomic selection in aquaculture breeding programs.

Construction of a High-Density Genetic Map and Quantitative Trait Locus Mapping in the Sea Cucumber Apostichopus japonicus

Genetic linkage maps are critical and indispensable tools in a wide range of genetic and genomic research. With the advancement of genotyping-by-sequencing (GBS) methods, the construction of a high-density and high-resolution linkage maps has become achievable in marine organisms lacking sufficient genomic resources, such as echinoderms. In this study, high-density, high-resolution genetic map was constructed for a sea cucumber species, Apostichopus japonicus, utilizing the 2b-restriction site-associated DNA (2b-RAD) method. A total of 7839 markers were anchored to the linkage map with the map coverage of 99.57%, to our knowledge, this is the highest marker density among echinoderm species. QTL mapping and association analysis consistently captured one growth-related QTL located in a 5 cM region of linkage group (LG) 5. An annotated candidate gene, retinoblastoma-binding protein 5 (RbBP5), which has been reported to be an important regulator of cell proliferation, was recognized in the QTL region. This linkage map represents a powerful tool for research involving both fine-scale QTL mapping and marker assisted selection (MAS), and will facilitate chromosome assignment and improve the whole-genome assembly of sea cucumber in the future.

Bioavailability of quercetin: problems and promises

Quercetin (QC) is a typical plant flavonoid, possesses diverse pharmacologic effects including antiinflammatory, antioxidant, anti-cancer, anti-anaphylaxis effects and against aging. However, the application of QC in pharmaceutical field is limited due to its poor solubility, low bioavailability, poor permeability and instability. To improve the bioavailability of QC, numerous approaches have been undertaken, involving the use of promising drug delivery systems such as inclusion complexes, liposomes, nanoparticles or micelles, which appear to provide higher solubility and bioavailability. Enhanced bioavailability of QC in the near future is likely to bring this product to the forefront of therapeutic agents for treatment of human disease.

Overexpression of microRna-200c in CD44+CD133+ CSCS inhibits the cellular migratory and invasion as well as tumorigenicity in mice

Cancer stem cells (CSCs) are believed to be responsible for drug resistance, metastasis of tumors. To investigate the biological characteristics of CD44+CD133+CSCs with over- expressing microRNA-200c (miR-200c), and to provide evidences for miR-200c as a tumor suppressor to treat melanoma. CD44+CD133+CSCs were isolated from the mouse melanoma B16F10 cell line by using immune magnetic activated cell sorting. The lentivirus miR-200c was transduced into the cells, and the effect of miR-200c overexpression on the biological characteristics of B16F10 CD44+ CD133+CSCs was analyzed by a series assays. The stable overexpression of miR-200c in B16F10 CD44+CD133+CSCs obviously resulted in downregulation of zinc-finger E-box binding homeobox 1 expression, reduction of the cell proliferation, colony forming, cell migratory and invasion ability in vitro as well as tumorigenicity in vivo compared with those of the B16F10 cells and B16F10 non-CD44+ CD133+CSCs. These findings suggest that the miR-200c overexpression as a novel strategy to target therapy of melanoma CSCs.

High-resolution linkage and quantitative trait locus mapping aided by genome survey sequencing: building up an integrative genomic framework for a bivalve mollusc

Genetic linkage maps are indispensable tools in genetic and genomic studies. Recent development of genotyping-by-sequencing (GBS) methods holds great promise for constructing high-resolution linkage maps in organisms lacking extensive genomic resources. In the present study, linkage mapping was conducted for a bivalve mollusc (Chlamys farreri) using a newly developed GBS method-2b-restriction site-associated DNA (2b-RAD). Genome survey sequencing was performed to generate a preliminary reference genome that was utilized to facilitate linkage and quantitative trait locus (QTL) mapping in C. farreri. A high-resolution linkage map was constructed with a marker density (3806) that has, to our knowledge, never been achieved in any other molluscs. The linkage map covered nearly the whole genome (99.5%) with a resolution of 0.41 cM. QTL mapping and association analysis congruously revealed two growth-related QTLs and one potential sex-determination region. An important candidate QTL gene named PROP1, which functions in the regulation of growth hormone production in vertebrates, was identified from the growth-related QTL region detected on the linkage group LG3. We demonstrate that this linkage map can serve as an important platform for improving genome assembly and unifying multiple genomic resources. Our study, therefore, exemplifies how to build up an integrative genomic framework in a non-model organism.

ESAT-6-gpi DNA vaccine augmented the specific antitumour efficacy induced by the tumour vaccine B16F10-ESAT-6-gpi/IL-21 in a mouse model

In this study, we hypothesized that the mice immunized with the glycosylphosphatidylinositol (GPI) anchored 6-kDa early-secreted antigenic target (ESAT-6) DNA vaccine (ESAT-6-gpi) and the tumour vaccine B16F10-ESAT-6-gpi/IL-21 might significantly enhance immune responses and antimelanoma efficacy. Our experimental results indicated that the anti-ESAT-6 antibody induced by the DNA vaccine ESAT-6-gpi bound ESAT-6 to the surface of tumour vaccine to activate a complement classical pathway and resulted in the B16F10 tumour cell lysis and apoptosis, which served as a potential trigger for breaking melanomatous immune tolerance to elicit an initiation of natural antimelanoma immunity. Our innovative approach of using the DNA vaccine ESAT-6-gpi priming and the tumour vaccine B16F10-ESAT-6-gpi/IL-21 boosting induced strong antimelanoma immunity that inhibited melanomatous growth. These findings highlighted the DNA vaccine ESAT-6-gpi as an immune enhancer to augment the immune efficacy of the tumour vaccine B16F10-ESAT -6-gpi/IL-21 against melanoma in a mouse model.