SPINE: SParse eIgengene NEtwork linking gene expression clusters in Dehalococcoides mccartyi to perturbations in experimental conditions

Network-Constrained Eigen-Single-Cell Profile Estimation for Uncovering Crucial Immunogene Regulatory Systems in Human Bone Marrow

We focus on characterizing cell lines from young and aged-healthy and -AML (acute myeloid leukemia) cell lines, and our goal is to identify the key markers associated with the progression of AML. To characterize the age-related phenotypes in AML cell lines, we consider eigenCell analysis that effectively encapsulates the primary expression level patterns across the cell lines. However, earlier investigations utilizing eigenGenes and eigenCells analysis were based on linear combination of all features, leading to the disturbance from noise features. Moreover, the analysis based on a fully dense loading matrix makes it challenging to interpret the results of eigenCells analysis. In order to address these challenges, we develop a novel computational approach termed network-constrained eigenCells profile estimation, which employs a sparse learning strategy. The proposed method estimates eigenCell based on not only the lasso but also network constrained penalization. The use of the network-constrained penalization enables us to simultaneously select neighborhood genes. Furthermore, the hub genes and their regulator/target genes are easily selected as crucial markers for eigenCells estimation. That is, our method can incorporate insights from network biology into the process of sparse loading estimation. Through our methodology, we estimate sparse eigenCells profiles, where only critical markers exhibit expression levels. This allows us to identify the key markers associated with a specific phenotype. Monte Carlo simulations demonstrate the efficacy of our method in reconstructing the sparse structure of eigenCells profiles. We employed our approach to unveil the regulatory system of immunogenes in both young/aged-healthy and -AML cell lines. The markers we have identified for the age-related phenotype in both healthy and AML cell lines have garnered strong support from previous studies. Specifically, our findings, in conjunction with the existing literature, indicate that the activities within this subnetwork of CD79A could be pivotal in elucidating the mechanism driving AML progression, particularly noting the significant role played by the diminished activities in the CD79A subnetwork. We expect that the proposed method will be a useful tool for characterizing disease-related subsets of cell lines, encompassing phenotypes and clones.

Regulation of organohalide respiration

Organohalide respiration (OHR) is an anaerobic metabolism by which bacteria conserve energy with the use of halogenated compounds as terminal electron acceptors. Genes involved in OHR are organized in reductive dehalogenase (rdh) gene clusters and can be found in relatively high copy numbers in the genomes of organohalide-respiring bacteria (OHRB). The minimal rdh gene set is composed by rdhA and rdhB, encoding the catalytic enzyme involved in reductive dehalogenation and its putative membrane anchor, respectively. In this chapter, we present the major findings concerning the regulatory strategies developed by OHRB to control the expression of the rdh gene clusters. The first section focuses on the description of regulation patterns obtained from targeted transcriptional analyses, and from transcriptomic and proteomic studies, while the second section offers a detailed overview of the biochemically characterized OHR regulatory proteins identified so far. Depending on OHRB, transcriptional regulators belonging to three different protein families are found in the direct vicinity of rdh gene clusters, suggesting that they activate the transcription of their cognate gene cluster. In this chapter, strong emphasis was laid on the family of CRP/FNR-type RdhK regulators which belong to members of the genera Dehalobacter and Desulfitobacterium. Whereas only chlorophenols have been identified as effectors for RdhK regulators, the protein sequence diversity suggests a broader organohalide spectrum. Thus, effector identification of new regulators offers a promising alternative to elucidate the substrates of yet uncharacterized reductive dehalogenases. Future work investigating the possible cross-talk between OHR regulators and their possible use as biosensors is discussed.

Integrated DNA methylome and transcriptome analysis reveals the ethylene-induced flowering pathway genes in pineapple

Ethylene has long been used to promote flowering in pineapple production. Ethylene-induced flowering is dose dependent, with a critical threshold level of ethylene response factors needed to trigger flowering. The mechanism of ethylene-induced flowering is still unclear. Here, we integrated isoform sequencing (iso-seq), Illumina short-reads sequencing and whole-genome bisulfite sequencing (WGBS) to explore the early changes of transcriptomic and DNA methylation in pineapple following high-concentration ethylene (HE) and low-concentration ethylene (LE) treatment. Iso-seq produced 122,338 transcripts, including 26,893 alternative splicing isoforms, 8,090 novel transcripts and 12,536 candidate long non-coding RNAs. The WGBS results suggested a decrease in CG methylation and increase in CHH methylation following HE treatment. The LE and HE treatments induced drastic changes in transcriptome and DNA methylome, with LE inducing the initial response to flower induction and HE inducing the subsequent response. The dose-dependent induction of FLOWERING LOCUS T-like genes (FTLs) may have contributed to dose-dependent flowering induction in pineapple by ethylene. Alterations in DNA methylation, lncRNAs and multiple genes may be involved in the regulation of FTLs. Our data provided a landscape of the transcriptome and DNA methylome and revealed a candidate network that regulates flowering time in pineapple, which may promote further studies.

Inferring Gene Networks for Strains of Dehalococcoides Highlights Conserved Relationships between Genes Encoding Core Catabolic and Cell-Wall Structural Proteins

The interpretation of high-throughput gene expression data for non-model microorganisms remains obscured because of the high fraction of hypothetical genes and the limited number of methods for the robust inference of gene networks. Therefore, to elucidate gene-gene and gene-condition linkages in the bioremediation-important genus Dehalococcoides, we applied a Bayesian inference strategy called Reverse Engineering/Forward Simulation (REFS^™) on transcriptomic data collected from two organohalide-respiring communities containing different Dehalococcoides mccartyi strains: the Cornell University mixed community D2 and the commercially available KB-1^® bioaugmentation culture. In total, 49 and 24 microarray datasets were included in the REFS^™ analysis to generate an ensemble of 1,000 networks for the Dehalococcoides population in the Cornell D2 and KB-1^® culture, respectively. Considering only linkages that appeared in the consensus network for each culture (exceeding the determined frequency cutoff of ≥ 60%), the resulting Cornell D2 and KB-1^® consensus networks maintained 1,105 nodes (genes or conditions) with 974 edges and 1,714 nodes with 1,455 edges, respectively. These consensus networks captured multiple strong and biologically informative relationships. One of the main highlighted relationships shared between these two cultures was a direct edge between the transcript encoding for the major reductive dehalogenase (tceA (D2) or vcrA (KB-1^®)) and the transcript for the putative S-layer cell wall protein (DET1407 (D2) or KB1_1396 (KB-1^®)). Additionally, transcripts for two key oxidoreductases (a [Ni Fe] hydrogenase, Hup, and a protein with similarity to a formate dehydrogenase, “Fdh”) were strongly linked, generalizing a strong relationship noted previously for Dehalococcoides mccartyi strain 195 to multiple strains of Dehalococcoides. Notably, the pangenome array utilized when monitoring the KB-1^® culture was capable of resolving signals from multiple strains, and the network inference engine was able to reconstruct gene networks in the distinct strain populations.