Multiple early factors anticipate post-acute COVID-19 sequelae

Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific auto-antibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8+ T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes, exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time, leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies.

Multi-Omics Resolves a Sharp Disease-State Shift between Mild and Moderate COVID-19

We present an integrated analysis of the clinical measurements, immune cells, and plasma multi-omics of 139 COVID-19 patients representing all levels of disease severity, from serial blood draws collected during the first week of infection following diagnosis. We identify a major shift between mild and moderate disease, at which point elevated inflammatory signaling is accompanied by the loss of specific classes of metabolites and metabolic processes. Within this stressed plasma environment at moderate disease, multiple unusual immune cell phenotypes emerge and amplify with increasing disease severity. We condensed over 120,000 immune features into a single axis to capture how different immune cell classes coordinate in response to SARS-CoV-2. This immune-response axis independently aligns with the major plasma composition changes, with clinical metrics of blood clotting, and with the sharp transition between mild and moderate disease. This study suggests that moderate disease may provide the most effective setting for therapeutic intervention.

Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories

Host immune responses play central roles in controlling SARS-CoV2 infection, yet remain incompletely characterized and understood. Here, we present a comprehensive immune response map spanning 454 proteins and 847 metabolites in plasma integrated with single-cell multi-omic assays of PBMCs in which whole transcriptome, 192 surface proteins, and T and B cell receptor sequence were co-analyzed within the context of clinical measures from 50 COVID19 patient samples. Our study reveals novel cellular subpopulations, such as proliferative exhausted CD8 ⁺ and CD4 ⁺ T cells, and cytotoxic CD4 ⁺ T cells, that may be features of severe COVID-19 infection. We condensed over 1 million immune features into a single immune response axis that independently aligns with many clinical features and is also strongly associated with disease severity. Our study represents an important resource towards understanding the heterogeneous immune responses of COVID-19 patients and may provide key information for informing therapeutic development.

Cancer-secreted AGR2 induces programmed cell death in normal cells

Anterior Gradient 2 (AGR2) is a protein expressed in many solid tumor types including prostate, pancreatic, breast and lung. AGR2 functions as a protein disulfide isomerase in the endoplasmic reticulum. However, AGR2 is secreted by cancer cells that overexpress this molecule. Secretion of AGR2 was also found in salamander limb regeneration. Due to its ubiquity, tumor secretion of AGR2 must serve an important role in cancer, yet its molecular function is largely unknown. This study examined the effect of cancer-secreted AGR2 on normal cells. Prostate stromal cells were cultured, and tissue digestion media containing AGR2 prepared from prostate primary cancer 10-076 CP and adenocarcinoma LuCaP 70CR xenograft were added. The control were tissue digestion media containing no AGR2 prepared from benign prostate 10-076 NP and small cell carcinoma LuCaP 145.1 xenograft. In the presence of tumor-secreted AGR2, the stromal cells were found to undergo programmed cell death (PCD) characterized by formation of cellular blebs, cell shrinkage, and DNA fragmentation as seen when the stromal cells were UV irradiated or treated by a pro-apoptotic drug. PCD could be prevented with the addition of the monoclonal AGR2-neutralizing antibody P3A5. DNA microarray analysis of LuCaP 70CR media-treated vs. LuCaP 145.1 media-treated cells showed downregulation of the gene SAT1 as a major change in cells exposed to AGR2. RT-PCR analysis confirmed the array result. SAT1 encodes spermidine/spermine N1-acetyltransferase, which maintains intracellular polyamine levels. Abnormal polyamine metabolism as a result of altered SAT1 activity has an adverse effect on cells through the induction of PCD.

Conversion of Prostate Adenocarcinoma to Small Cell Carcinoma-Like by Reprogramming

The lineage relationship between prostate adenocarcinoma and small cell carcinoma was studied by using the LuCaP family of xenografts established from primary neoplasm to metastasis. Expression of four stem cell transcription factor (TF) genes, LIN28A, NANOG, POU5F1, SOX2, were analyzed in the LuCaP lines. These genes, when force expressed in differentiated cells, can reprogram the recipients into stem-like induced pluripotent stem (iPS) cells. Most LuCaP lines expressed POU5F1, while LuCaP 145.1, representative of small cell carcinoma, expressed all four. Through transcriptome database query, many small cell carcinoma genes were also found in stem cells. To test the hypothesis that prostate cancer progression from "differentiated" adenocarcinoma to "undifferentiated" small cell carcinoma could involve re-expression of stem cell genes, the four TF genes were transduced via lentiviral vectors into five adenocarcinoma LuCaP lines-70CR, 73CR, 86.2, 92, 105CR-as done in iPS cell reprogramming. The resultant cells from these five transductions displayed a morphology of small size and dark appearing unlike the parentals. Transcriptome analysis of LuCaP 70CR* ("*" to denote transfected progeny) revealed a unique gene expression close to that of LuCaP 145.1. In a prostate principal components analysis space based on cell-type transcriptomes, the different LuCaP transcriptome datapoints were aligned to suggest a possible ordered sequence of expression changes from the differentiated luminal-like adenocarcinoma cell types to the less differentiated, more stem-like small cell carcinoma types, and LuCaP 70CR*. Prostate cancer progression can thus be molecularly characterized by loss of differentiation with re-expression of stem cell genes. J. Cell. Physiol. 231: 2040-2047, 2016. © 2016 Wiley Periodicals, Inc.

Gene expression relationship between prostate cancer cells of Gleason 3, 4 and normal epithelial cells as revealed by cell type-specific transcriptomes

BACKGROUND

Prostate cancer cells in primary tumors have been typed CD10-/CD13-/CD24hi/CD26+/CD38lo/CD44-/CD104-. This CD phenotype suggests a lineage relationship between cancer cells and luminal cells. The Gleason grade of tumors is a descriptive of tumor glandular differentiation. Higher Gleason scores are associated with treatment failure.

METHODS

CD26+ cancer cells were isolated from Gleason 3+3 (G3) and Gleason 4+4 (G4) tumors by cell sorting, and their gene expression or transcriptome was determined by Affymetrix DNA array analysis. Dataset analysis was used to determine gene expression similarities and differences between G3 and G4 as well as to prostate cancer cell lines and histologically normal prostate luminal cells.

RESULTS

The G3 and G4 transcriptomes were compared to those of prostatic cell types of non-cancer, which included luminal, basal, stromal fibromuscular, and endothelial. A principal components analysis of the various transcriptome datasets indicated a closer relationship between luminal and G3 than luminal and G4. Dataset comparison also showed that the cancer transcriptomes differed substantially from those of prostate cancer cell lines.

CONCLUSIONS

Genes differentially expressed in cancer are potential biomarkers for cancer detection, and those differentially expressed between G3 and G4 are potential biomarkers for disease stratification given that G4 cancer is associated with poor outcomes. Differentially expressed genes likely contribute to the prostate cancer phenotype and constitute the signatures of these particular cancer cell types.

Memory T cell RNA rearrangement programmed by heterogeneous nuclear ribonucleoprotein hnRNPLL

Differentiation of memory cells involves DNA-sequence changes in B lymphocytes but is less clearly defined in T cells. RNA rearrangement is identified here as a key event in memory T cell differentiation by analysis of a mouse mutation that altered the proportions of naive and memory T cells and crippled the process of Ptprc exon silencing needed to generate CD45RO in memory T cells. A single substitution in a memory-induced RNA-binding protein, hnRNPLL, destabilized an RNA-recognition domain that bound with micromolar affinity to RNA containing the Ptprc exon-silencing sequence. Hnrpll mutation selectively diminished T cell accumulation in peripheral lymphoid tissues but not proliferation. Exon-array analysis of Hnrpll mutant naive and memory T cells revealed an extensive program of alternative mRNA splicing in memory T cells, coordinated by hnRNPLL. A remarkable overlap with alternative splicing in neural tissues may reflect a co-opted strategy for diversifying memory T cells.

SLIMarray: lightweight software for microarray facility management

Background

Microarray core facilities are commonplace in biological research organizations, and need systems for accurately tracking various logistical aspects of their operation. Although these different needs could be handled separately, an integrated management system provides benefits in organization, automation and reduction in errors.

Results

We present SLIMarray (System for Lab Information Management of Microarrays), an open source, modular database web application capable of managing microarray inventories, sample processing and usage charges. The software allows modular configuration and is well suited for further development, providing users the flexibility to adapt it to their needs. SLIMarray Lite, a version of the software that is especially easy to install and run, is also available.

Conclusion

SLIMarray addresses the previously unmet need for free and open source software for managing the logistics of a microarray core facility.