Improving Novel Gene Discovery in High-Throughput Gene Expression Datasets

High-throughput gene expression datasets (including RNA-seq and microarray datasets) can quantify the expression level of tens of thousands of genes in an organism, which allows for the identification of putative functions for previously unstudied genes involved in treatment/condition responses. For static (single timepoint) high-throughput gene expression experiments, the most common first analysis step to discover novel genes is to filter out genes based on their degree of differential expression and the amount of inter-replicate noise. However, this filtering step may remove genes with very high baseline expression levels, and genes with important functional annotations in the experiment being studied. Chapter 2 presents a novel knowledge-based clustering approach for novel gene discovery, in which known functionally important genes as well as genes with very high expression levels (which would typically be removed by a strict fold change filter) are saved prior to filtering

Proliferation tracing with single-cell mass cytometry optimizes generation of stem cell memory-like T cells.

Selective differentiation of naive T cells into multipotent T cells is of great interest clinically for the generation of cell-based cancer immunotherapies. Cellular differentiation depends crucially on division state and time. Here we adapt a dye dilution assay for tracking cell proliferative history through mass cytometry and uncouple division, time and regulatory protein expression in single naive human T cells during their activation and expansion in a complex ex vivo milieu. Using 23 markers, we defined groups of proteins controlled predominantly by division state or time and found that undivided cells account for the majority of phenotypic diversity. We next built a map of cell state changes during naive T-cell expansion. By examining cell signaling on this map, we rationally selected ibrutinib, a BTK and ITK inhibitor, and administered it before T cell activation to direct differentiation toward a T stem cell memory (TSCM)-like phenotype. This method for tracing cell fate across division states and time can be broadly applied for directing cellular differentiation

Microbial community dynamics and coexistence in a sulfide-driven phototrophic bloom

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Bhatnagar, S., Cowley, E. S., Kopf, S. H., Pérez Castro, S., Kearney, S., Dawson, S. C., Hanselmann, K., & Ruff, S. E. Microbial community dynamics and coexistence in a sulfide-driven phototrophic bloom. Environmental Microbiome, 15(1),(2020): 3, doi:10.1186/s40793-019-0348-0.Background: Lagoons are common along coastlines worldwide and are important for biogeochemical element cycling, coastal biodiversity, coastal erosion protection and blue carbon sequestration. These ecosystems are frequently disturbed by weather, tides, and human activities. Here, we investigated a shallow lagoon in New England. The brackish ecosystem releases hydrogen sulfide particularly upon physical disturbance, causing blooms of anoxygenic sulfur-oxidizing phototrophs. To study the habitat, microbial community structure, assembly and function we carried out in situ experiments investigating the bloom dynamics over time. Results: Phototrophic microbial mats and permanently or seasonally stratified water columns commonly contain multiple phototrophic lineages that coexist based on their light, oxygen and nutrient preferences. We describe similar coexistence patterns and ecological niches in estuarine planktonic blooms of phototrophs. The water column showed steep gradients of oxygen, pH, sulfate, sulfide, and salinity. The upper part of the bloom was dominated by aerobic phototrophic Cyanobacteria, the middle and lower parts by anoxygenic purple sulfur bacteria (Chromatiales) and green sulfur bacteria (Chlorobiales), respectively. We show stable coexistence of phototrophic lineages from five bacterial phyla and present metagenome-assembled genomes (MAGs) of two uncultured Chlorobaculum and Prosthecochloris species. In addition to genes involved in sulfur oxidation and photopigment biosynthesis the MAGs contained complete operons encoding for terminal oxidases. The metagenomes also contained numerous contigs affiliating with Microviridae viruses, potentially affecting Chlorobi. Our data suggest a short sulfur cycle within the bloom in which elemental sulfur produced by sulfide-oxidizing phototrophs is most likely reduced back to sulfide by Desulfuromonas sp. Conclusions: The release of sulfide creates a habitat selecting for anoxygenic sulfur-oxidizing phototrophs, which in turn create a niche for sulfur reducers. Strong syntrophism between these guilds apparently drives a short sulfur cycle that may explain the rapid development of the bloom. The fast growth and high biomass yield of Chlorobi-affiliated organisms implies that the studied lineages of green sulfur bacteria can thrive in hypoxic habitats. This oxygen tolerance is corroborated by oxidases found in MAGs of uncultured Chlorobi. The findings improve our understanding of the ecology and ecophysiology of anoxygenic phototrophs and their impact on the coupled biogeochemical cycles of sulfur and carbon.This work was carried out at the Microbial Diversity summer course at the Marine Biological Laboratory in Woods Hole, MA. The course was supported by grants from National Aeronautics and Space Administration, the US Department of Energy, the Simons Foundation, the Beckman Foundation, and the Agouron Institute. Additional funding for SER was provided by the Marine Biological Laboratory

Microbial community dynamics of attached biofilm BioCord technology in wastewater treatment

Within this context, the data follows the lognormal and power law distribution, and a TS-IDS algorithm has been proposed in the literature and demonstrated to outperform both the greedy and IDS algorithm. We have evaluated the performance of various greedy approaches to the set covering problem, including the TS-IDS, using open source dataset in the context of resource management. The data are sampled from a given roadmap with different coverage radius

Optimoitu protokolla fuusiogeenien tunnistukseen akuutin leukemian diagnostiikassa RNA sekvensoinnilla

Acute leukemia is a life-threatening disease of blood and bone marrow, which is caused by malignant transformation of immature white blood cells. These malignant white blood cells invade space in bone marrow decreasing its ability to produce normal blood cells, eventually leading to death within weeks after the diagnosis without treatment. The acute leukemia can be broadly divided into its lymphoblastic and myeloid form, based on the affected cell lineage. Furthermore, acute leukemias can be classified based on different genomic features, such as gene fusions. Fusion genes are strong drivers in various cancers such as acute leukemias, and they are formed when two or more original genes join together forming a novel hybrid gene. If the novel hybrid gene is transcribed, it can lead to a translation of an abnormal fusion protein with altered function. The detection of the gene fusions is very important, since it affects to diagnosis and treatment of the patient. Various techniques can be used for fusion gene detection, of which the RNA sequencing is the method of choice, due to its ability to provide an unbiased identification of all known and novel gene fusions from the sample in a single experiment. In this thesis, the overarching aim was to develop an optimal sampling protocol for fusion gene detection using RNA sequencing for acute leukemia diagnostics. First, the whole blood samples in EDTA-tubes were collected from acute leukemia patients based on the findings from routine diagnostics. Next, the RNA was extracted at three different timepoints (0h, 8h, and 32h). The samples were stored at 4°C between the extractions. Finally, the RNA sequencing libraries were constructed, and the RNA sequencing was performed. After the sequencing, the data was analyzed using the FusionCatcher algorithm for fusion gene detection and the EdgeR-package for differential expression analysis. The FusionCatcher detected the same gene fusion in all the four fusion gene positive patients compared to routine diagnostics. However, the FusionCatcher failed to recognize the gene fusion in some of the samples with very low number of fusion breakpoint-spanning reads. These reads were visualized with IGV, suggesting that the detection failure resulted from the very low number of break-point-spanning reads. Furthermore, the sample storage did not affect on gene fusion detection. In addition, FusionCatcher detected PIK3AP::BLNK gene fusion from one of the fusion gene negative patients, suggesting a possibility that the patient truly was fusion gene positive. The differential expression analysis revealed changes in gene expression between the different timepoints. The results showed changes in various pathways related for example to cell death and protein biosynthesis, but also to pathways related to cancer. The results showed that prolonged sample storage alters the gene expression profile thus affecting the results of a gene expression study

Changes in the genetic requirements for microbial interactions with increasing community complexity.

Microbial community structure and function rely on complex interactions whose underlying molecular mechanisms are poorly understood. To investigate these interactions in a simple microbiome, we introduced E. coli into an experimental community based on a cheese rind and identified the differences in E. coli's genetic requirements for growth in interactive and non-interactive contexts using Random Barcode Transposon Sequencing (RB-TnSeq) and RNASeq. Genetic requirements varied among pairwise growth conditions and between pairwise and community conditions. Our analysis points to mechanisms by which growth conditions change as a result of increasing community complexity and suggests that growth within a community relies on a combination of pairwise and higher-order interactions. Our work provides a framework for using the model organism E. coli as a readout to investigate microbial interactions regardless of the genetic tractability of members of the studied ecosystem

Long-term lymphoid progenitors independently sustain naïve T and NK cell production in humans.

Our mathematical model of integration site data in clinical gene therapy supported the existence of long-term lymphoid progenitors capable of surviving independently from hematopoietic stem cells. To date, no experimental setting has been available to validate this prediction. We here report evidence of a population of lymphoid progenitors capable of independently maintaining T and NK cell production for 15 years in humans. The gene therapy patients of this study lack vector-positive myeloid/B cells indicating absence of engineered stem cells but retain gene marking in both T and NK. Decades after treatment, we can still detect and analyse transduced naïve T cells whose production is likely maintained by a population of long-term lymphoid progenitors. By tracking insertional clonal markers overtime, we suggest that these progenitors can support both T and NK cell production. Identification of these long-term lymphoid progenitors could be utilised for the development of next generation gene- and cancer-immunotherapies

Clonal expansion of T memory stem cells determines early anti-leukemic responses and long-term CAR T cell persistence in patients

Low-affinity CD19 chimeric antigen receptor (CAR) T cells display enhanced expansion and persistence, enabling fate tracking through integration site analysis. Here we show that integration sites from early (1 month) and late (>3 yr) timepoints cluster separately, suggesting different clonal contribution to early responses and prolonged anti-leukemic surveillance. CAR T central and effector memory cells in patients with long-term persistence remained highly polyclonal, whereas diversity dropped rapidly in patients with limited CAR T persistence. Analysis of shared integrants between the CAR T cell product and post-infusion demonstrated that, despite their low frequency, T memory stem cell clones in the product contributed substantially to the circulating CAR T cell pools, during both early expansion and long-term persistence. Our data may help identify patients at risk of early loss of CAR T cells and highlight the critical role of T memory stem cells both in mediating early anti-leukemic responses and in long-term surveillance by CAR T cells

On metabolic and phenotypic diversity in yeast

This thesis explores metabolic and phenotypic diversity in the two model yeasts Schizosaccharomyces pombe and Saccharomyces cerevisiae. Colony screens are a classical and powerful technique for investigating these topics, but there is a lack of modern, scalable bioinformatics tools. To address this need, I have developed pyphe which greatly facilitates colony screen data acquisition and statistical analysis. I explore optimal experimental designs, especially regarding the usefulness of timecourse imaging and colony viability analysis. Pyphe is used in a functional genomics screen, aiming to find functions for a set of largely uncharacterised lincRNAs. We identify hundreds of new lincRNA-associated phenotypes across numerous conditions and compare lincRNA phenotype profiles to those of codinggene mutants. Next, I have used pyphe to investigate the respiration/fermentation balance of wild S. pombe isolates. Contrary to the expectation that glucose completely represses respiration in this Crabtree-positive species, I find that strains generally strike a balance and that individual strains differ significantly in their residual respiration activity. This is associated with an unusual miss-sense variant in S. pombe’s sole pyruvate kinase gene. Its impact is dissected in detail, revealing a change in flux through pyruvate kinase and associated changes in gene expression, metabolism, growth and stress resistance. Finally, I explore how extracellular amino acids interact with cellular metabolism, with the aim of answering the important question whether or not clonal yeast cultures segregate into heterogeneous producer/consumer populations that exchange amino acids. I develop a novel proteomics-based method that characterises amino acid labelling patterns in peptides. I find that the supplementation of some, but not all amino acids completely suppresses selfsynthesis. However, I find no evidence for heterogeneous responses of our laboratory S. cerevisiae strain, but the functionality of the method is demonstrated clearly. Overall, this work represents several advancements to our understanding of yeast metabolism and physiology, as well as new experimental and computational methods