Computational immunogenetics in allogeneic immunotherapy

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PB1-F2 Finder: scanning influenza sequences for PB1-F2 encoding RNA segments

<p>Abstract</p> <p>Background</p> <p>PB1-F2 is a major virulence factor of influenza A. This protein is a product of an alternative reading frame in the PB1-encoding RNA segment 2. Its presence of is dictated by the presence or absence of premature stop codons. This virulence factor is present in every influenza pandemic and major epidemic of the 20th century. Absence of PB1-F2 is associated with mild disease, such as the 2009 H1N1 (“swine flu”).</p> <p>Results</p> <p>The analysis of 8608 segment 2 sequences showed that only 8.5% have been annotated for the presence of PB1-F2. Our analysis indicates that 75% of segment 2 sequences are likely to encode PB1-F2. Two major populations of PB1-F2 are of lengths 90 and 57 while minor populations include lengths 52, 63, 79, 81, 87, and 101. Additional possible populations include the lengths of 59, 69, 81, 95, and 106. Previously described sequences include only lengths 57, 87, and 90. We observed substantial variation in PB1-F2 sequences where certain variants show up to 35% difference to well-defined reference sequences. Therefore this dataset indicates that there are many more variants that need to be functionally characterized.</p> <p>Conclusions</p> <p>Our web-accessible tool PB1-F2 Finder enables scanning of influenza sequences for potential PB1-F2 protein products. It provides an initial screen and annotation of PB1-F2 products. It is accessible at <url>http://cvc.dfci.harvard.edu/pb1-f2</url>.</p

A comparison of curated gene sets versus transcriptomics-derived gene signatures for detecting pathway activation in immune cells

Background: Despite the significant contribution of transcriptomics to the fields of biological and biomedical research, interpreting long lists of significantly differentially expressed genes remains a challenging step in the analysis process. Gene set enrichment analysis is a standard approach for summarizing differentially expressed genes into pathways or other gene groupings. Here, we explore an alternative approach to utilizing gene sets from curated databases. We examine the method of deriving custom gene sets which may be relevant to a given experiment using reference data sets from previous transcriptomics studies. We call these data-derived gene sets, "gene signatures" for the biological process tested in the previous study. We focus on the feasibility of this approach in analyzing immune-related processes, which are complicated in their nature but play an important role in the medical research. Results: We evaluate several statistical approaches to detecting the activity of a gene signature in a target data set. We compare the performance of the data-derived gene signature approach with comparable GO term gene sets across all of the statistical tests. A total of 61 differential expression comparisons generated from 26 transcriptome experiments were included in the analysis. These experiments covered eight immunological processes in eight types of leukocytes. The data-derived signatures were used to detect the presence of immunological processes in the test data with modest accuracy (AUC = 0.67). The performance for GO and literature based gene sets was worse (AUC = 0.59). Both approaches were plagued by poor specificity. Conclusions: When investigators seek to test specific hypotheses, the data-derived signature approach can perform as well, if not better than standard gene-set based approaches for immunological signatures. Furthermore, the data-derived signatures can be generated in the cases that well-defined gene sets are lacking from pathway databases and also offer the opportunity for defining signatures in a cell-type specific manner. However, neither the data-derived signatures nor standard gene-sets can be demonstrated to reliably provide negative predictions for negative cases. We conclude that the data-derived signature approach is a useful and sometimes necessary tool, but analysts should be weary of false positives. © 2020 The Author(s)

Nutrient stripping: the global disparity between food security and soil nutrient stocks

1. The Green Revolution successfully increased food production but in doing so created a legacy of inherently leaky and unsustainable agricultural systems. Central to this are the problems of excessive nutrient mining. If agriculture is to balance the needs of food security with the delivery of other ecosystem services, then current rates of soil nutrient stripping must be reduced and the use of synthetic fertilisers made more efficient. 2. We explore the global extent of the problem, with specific emphasis on the failure of macronutrient management (e.g. nitrogen, phosphorus) to deliver continued improvements in yield and the failure of agriculture to recognise the seriousness of micronutrient depletion (e.g. copper, zinc, selenium). 3. Nutrient removals associated with the relatively immature, nutrient-rich soils of the UK are contrasted with the mature, nutrient-poor soils of India gaining insight into the emerging issue of nutrient stripping and the long-term implications for human health and soil quality. Whilst nutrient deficiencies are rare in developed countries, micronutrient deficiencies are commonly increasing in less-developed countries. Increasing rates of micronutrient depletion are being inadvertently accomplished through increasing crop yield potential and nitrogen fertiliser applications. 4. Amongst other factors, the spatial disconnects caused by the segregation and industrialisation of livestock systems, between rural areas (where food is produced) and urban areas (where food is consumed and human waste treated) are identified as a major constraint to sustainable nutrient recycling. 5. Synthesis and applications. This study advocates that agricultural sustainability can only be accomplished using a whole-systems approach that thoroughly considers nutrient stocks, removals, exports and recycling. Society needs to socially and environmentally re-engineer agricultural systems at all scales. It is suggested that this will be best realised by national-scale initiatives. Failure to do so will lead to an inevitable and rapid decline in the delivery of provisioning services within agricultural systems

Human Leukocyte Antigen Typing Using a Knowledge Base Coupled with a High-Throughput Oligonucleotide Probe Array Analysis

Human leukocyte antigens (HLA) are important biomarkers because multiple diseases, drug toxicity, and vaccine responses reveal strong HLA associations. Current clinical HLA typing is an elimination process requiring serial testing. We present an alternative in situ synthesized DNA-based microarray method that contains hundreds of thousands of probes representing a complete overlapping set covering 1,610 clinically relevant HLA class I alleles accompanied by computational tools for assigning HLA type to 4-digit resolution. Our proof-of-concept experiment included 21 blood samples, 18 cell lines, and multiple controls. The method is accurate, robust, and amenable to automation. Typing errors were restricted to homozygous samples or those with very closely related alleles from the same locus, but readily resolved by targeted DNA sequencing validation of flagged samples. High-throughput HLA typing technologies that are effective, yet inexpensive, can be used to analyze the world’s populations, benefiting both global public health and personalized health care

Loss of Serpina1 in Mice Leads to Altered Gene Expression in Inflammatory and Metabolic Pathways

The SERPINA1 gene encodes alpha1-antitrypsin (AAT), an acute phase glycoprotein and serine protease inhibitor that is mainly (80-90%) produced in the liver. Point mutations in the SERPINA1 gene can lead to the misfolding, intracellular accumulation, and deficiency of circulating AAT protein, increasing the risk of developing chronic liver diseases or chronic obstructive pulmonary disease. Currently, siRNA technology can knock down the SERPINA1 gene and limit defective AAT production. How this latter affects other liver genes is unknown. Livers were taken from age- and sex-matched C57BL/6 wild-type (WT) and Serpina1 knockout mice (KO) aged from 8 to 14 weeks, all lacking the five serpin A1a-e paralogues. Total RNA was isolated and RNA sequencing, and transcriptome analysis was performed. The knockout of the Serpina1 gene in mice changed inflammatory, lipid metabolism, and cholesterol metabolism-related gene expression in the liver. Independent single-cell sequencing data of WT mice verified the involvement of Serpina1 in cholesterol metabolism. Our results from mice livers suggested that designing therapeutic strategies for the knockout of the SERPINA1 gene in humans must account for potential perturbations of key metabolic pathways and consequent mitigation of side effects.RNA sequencing was supported by the grant ISCIII-AESI PI20CIII/00015.S

RNA-SeQC: RNA-seq metrics for quality control and process optimization

Summary: RNA-seq, the application of next-generation sequencing to RNA, provides transcriptome-wide characterization of cellular activity. Assessment of sequencing performance and library quality is critical to the interpretation of RNA-seq data, yet few tools exist to address this issue. We introduce RNA-SeQC, a program which provides key measures of data quality. These metrics include yield, alignment and duplication rates; GC bias, rRNA content, regions of alignment (exon, intron and intragenic), continuity of coverage, 3′/5′ bias and count of detectable transcripts, among others. The software provides multi-sample evaluation of library construction protocols, input materials and other experimental parameters. The modularity of the software enables pipeline integration and the routine monitoring of key measures of data quality such as the number of alignable reads, duplication rates and rRNA contamination. RNA-SeQC allows investigators to make informed decisions about sample inclusion in downstream analysis. In summary, RNA-SeQC provides quality control measures critical to experiment design, process optimization and downstream computational analysis

Comprehensive comparative analysis of RNA sequencing methods for degraded or low input samples

RNA-Seq is an effective method to study the transcriptome, but can be difficult to apply to scarce or degraded RNA from fixed clinical samples, rare cell populations, or cadavers. Recent studies have proposed several methods for RNA-Seq of low quality and/or low quantity samples, but their relative merits have not been systematically analyzed. Here, we compare five such methods using metrics relevant to transcriptome annotation, transcript discovery, and gene expression. Using a single human RNA sample, we constructed and sequenced ten libraries with these methods and two control libraries. We find that the RNase H method performed best for low quality RNA, and confirmed this with actual degraded samples. RNase H can even effectively replace oligo (dT) based methods for standard RNA-Seq. SMART and NuGEN had distinct strengths for low quantity RNA. Our analysis allows biologists to select the most suitable methods and provides a benchmark for future method development