Cellular differentiation into hyphae and spores in halophilic archaea.

Several groups of bacteria have complex life cycles involving cellular differentiation and multicellular structures. For example, actinobacteria of the genus Streptomyces form multicellular vegetative hyphae, aerial hyphae, and spores. However, similar life cycles have not yet been described for archaea. Here, we show that several haloarchaea of the family Halobacteriaceae display a life cycle resembling that of Streptomyces bacteria. Strain YIM 93972 (isolated from a salt marsh) undergoes cellular differentiation into mycelia and spores. Other closely related strains are also able to form mycelia, and comparative genomic analyses point to gene signatures (apparent gain or loss of certain genes) that are shared by members of this clade within the Halobacteriaceae. Genomic, transcriptomic and proteomic analyses of non-differentiating mutants suggest that a Cdc48-family ATPase might be involved in cellular differentiation in strain YIM 93972. Additionally, a gene encoding a putative oligopeptide transporter from YIM 93972 can restore the ability to form hyphae in a Streptomyces coelicolor mutant that carries a deletion in a homologous gene cluster (bldKA-bldKE), suggesting functional equivalence. We propose strain YIM 93972 as representative of a new species in a new genus within the family Halobacteriaceae, for which the name Actinoarchaeum halophilum gen. nov., sp. nov. is herewith proposed. Our demonstration of a complex life cycle in a group of haloarchaea adds a new dimension to our understanding of the biological diversity and environmental adaptation of archaea.

Spleen-based proteogenomics reveals that Escherichia coli infection induces activation of phagosome maturation pathway in chicken.

Avian pathogenic Escherichia coli (APEC) leads to economic losses in poultry industry and is also a threat to human health. Various strategies were used for searching virulence factors, while little is known about the mechanism by which APEC survives in host or is eliminated by host. Thus, chicken colibacillosis model was constructed by intraperitoneally injecting E. coli O78 in this study, then the protein dynamic expression of spleen was characterized at different post-infection times by quantitative proteome. Comparative analysis showed that E. coli induced significant dysregulation at 72 h post infection in spleen tissue. Transcriptomic method was further used to assess the changes of dysregulated proteins at 72 h post infection at the mRNA level. Total 278 protein groups (5.7%) and 2,443 genes (24.4%) were dysregulated, respectively. The upregulated proteins and genes were consistently enriched in phagosome and lysosome pathways, indicating E. coli infection activates phagosome maturation pathway. The matured phagolysosome might kill the invasive E. coli. This study illuminated the genetic dysregulation in chicken spleen at the protein and mRNA levels after E. coli infecting and identified candidate genes for host response to APEC infection.

Using transcriptomics, proteomics and phosphoproteomics as new approach methodology (NAM) to define biological responses for chemical safety assessment.

Omic-based technologies are of particular interest and importance for hazard identification and health risk characterization of chemicals. Their application in the new approach methodologies (NAMs) anchored on cellular toxicity pathways is based on the premise that any apical health endpoint change must be underpinned by some alterations at the omic levels. In the present study we examined the cellular responses to two chemicals, caffeine and coumarin, by generating and integrating multi-omic data from multi-dose and multi-time point transcriptomic, proteomic and phosphoproteomic experiments. We showed that the methodology presented here was able to capture the complete chain of events from the first chemical-induced changes at the phosphoproteome level, to changes in gene expression, and lastly to changes in protein abundance, each with vastly different points of departure (PODs). In HepG2 cells we found that the metabolism of lipids and general cellular stress response to be the dominant biological processes in response to caffeine and coumarin exposure, respectively. The phosphoproteomic changes were detected early in time, at very low doses and provided a fast, adaptive cellular response to chemical exposure with 7-37-fold lower points of departure comparing to the transcriptomics. Changes in protein abundance were found much less frequently than transcriptomic changes. While challenges remain, our study provides strong and novel evidence supporting the notion that these three omic technologies can be used in an integrated manner to facilitate a more complete understanding of pathway perturbations and POD determinations for risk assessment of chemical exposures.

Mirror proteases of Ac-Trypsin and Ac-LysargiNase precisely improve novel event identifications in Mycolicibacterium smegmatis MC2 155 by proteogenomic analysis.

Accurate identification of novel peptides remains challenging because of the lack of evaluation criteria in large-scale proteogenomic studies. Mirror proteases of trypsin and lysargiNase can generate complementary b/y ion series, providing the opportunity to efficiently assess authentic novel peptides in experiments other than filter potential targets by different false discovery rates (FDRs) ranking. In this study, a pair of in-house developed acetylated mirror proteases, Ac-Trypsin and Ac-LysargiNase, were used in Mycolicibacterium smegmatis MC2 155 for proteogenomic analysis. The mirror proteases accurately identified 368 novel peptides, exhibiting 75-80% b and y ion coverages against 65-68% y or b ion coverages of Ac-Trypsin (38.9% b and 68.3% y) or Ac-LysargiNase (65.5% b and 39.6% y) as annotated peptides from M. smegmatis MC2 155. The complementary b and y ion series largely increased the reliability of overlapped sequences derived from novel peptides. Among these novel peptides, 311 peptides were annotated in other public M. smegmatis strains, and 57 novel peptides with more continuous b and y pairs were obtained for further analysis after spectral quality assessment. This enabled mirror proteases to successfully correct six annotated proteins' N-termini and detect 17 new coding open reading frames (ORFs). We believe that mirror proteases will be an effective strategy for novel peptide detection in both prokaryotic and eukaryotic proteogenomics.

Quantitative phosphoproteomics reveal cellular responses from caffeine, coumarin and quercetin in treated HepG2 cells.

Protein phosphorylation is the most common type of post-translational modification where serine, threonine or tyrosine are reversibly bound to the phosphate group of ATP in a reaction catalyzed by protein kinases. Phosphorylation plays an important role in regulation of cell homeostasis, including but not limited to signal perception and transduction, gene expression and function of proteins. Protein phosphorylation happens on a fast time scale and represents an energy-efficient way for the cell to adapt to exposure to chemical stressors. To understand the cascade of cellular signaling induced by exposure to chemicals, we have exposed HepG2 cells to three chemicals with different modes of action, namely, caffeine, coumarin, and quercetin in a concentration and time response manner. Significantly upregulated and downregulated phosphosites were screened to analyze the activation/deactivation of signaling pathways by protein kinases. In total, 69, 44 and 12 signaling pathways were found enriched in caffeine, coumarin and quercetin treated cells, respectively, of which 9 pathways were co-enriched with 11 jointly responded kinases. Among identified co-responded kinases, CDK1, MAPK1 and MAPK3 play important roles in cell cycle and insulin signaling pathways. Quantitative phosphoproteomics can sensitively distinguish the effects of different chemicals on cells, allowing the assessment of chemical safety through changes in substrates and metabolic pathways at the cellular level, which is important for the development of non-animal approaches for chemical safety assessment.

Ac-LysargiNase efficiently helps genome reannotation of Mycolicibacterium smegmatis MC2 155.

Accurate genome annotation, the foundation of life science research in the genome era, is hampered by limited known gene models, nonstandard start codons, and the limited homology of annotated genes in other organisms. LysargiNase mirrors trypsin at the cleavage sites, providing the opportunity to identify peptides other than tryptic peptides. In this study, we used an in-house developed acetylated LysargiNase (Ac-LysargiNase) with higher activity and stability in non-pathogenic Mycolicibacterium smegmatis MC2 155 to supplement the widely used trypsin in proteomic studies. We identified 27,582 peptides from 3844 annotated proteins and 332 novel genome search-specific peptides (GSSPs). Among these GSSPs, 88 peptides were annotated in another M.smegmatis genome database, and 41 were verified as novel peptides by predicted theoretical spectra and their corresponding 15N-labeling spectra. Further analysis revealed that 17 verified GSSPs corrected the N-terminus of the 13 annotated genes. The other 24 verified GSSPs helped identify 17 novel open reading frames (ORFs) missed in previously annotated M. smegmatis genomes. Among these novel ORFs, four relatively small proteins with amino acid residues less than 100 and three were precisely identified with C-terminal peptides. Ac-LysargiNase helps with genome reannotation by identifying new genes and events in proteogenomic studies. SIGNIFICANCE: Correct genomic annotation is vital in the field of life sciences. The nonstandard start codons seriously affect the confirmation of the translation initiation sites (TISs) of an open reading frame (ORF), and unknown structural genes are easily missed in automated gene prediction. Although proteogenomics presents new avenues for validating gene expression and gene structure refinement based on conventional tryptic peptides, determining the TISs and potential encoding genes is complicated. Thus, validation of TISs and encoding ORFs is crucial and urgent. Therefore, we recommend Ac-LysargiNase, a mirror enzyme of trypsin that can identify additional novel peptides for N-terminal correction and ORF identification.

Deep N-terminomics of Mycobacterium tuberculosis H37Rv extensively correct annotated encoding genes.

Mycobacterium tuberculosis (MTB) is a severe causing agent of tuberculosis (TB). Although H37Rv, the type strain of M. tuberculosis was sequenced in 1998, annotation errors of encoding genes have been frequently reported in hundreds of papers. This phenomenon is particularly severe at the 5' end of the genes. Here, we applied a TMPP [(N-Succinimidyloxycarbonylmethyl) tris (2,4,6-trimethoxyphenyl) phosphonium bromide] labeling combined with StageTip separating strategy on M. tuberculosis H37Rv to characterize the N-terminal start sites of its annotated encoding genes. Totally, 1047 proteins were identified with 2058 TMPP labeled N-terminal peptides from all the 2625 mass spectrometer (MS) sequenced proteins. Comparative genomics analysis allowed the re-annotation of 43 proteins' N-termini in H37Rv and 762 proteins in Mycobacteriaceae. All revised N-termini start sites were distributed in 5'-UTR of annotated genes due to over-annotation of previous N-terminal initiation codon, especially the ATG. In addition, we identified and verified a novel gene Rv1078A in +3 frame different from the annotated gene Rv1078 in +2 frame. Altogether, our findings contribute to the better understanding of N-terminal of H37Rv and other species from Mycobacteriaceae that can assist future studies on biological study.

Quantitative proteomics reveals that dormancy-related proteins mediate the attenuation in mycobacterium strains.

Although members of the Mycobacterium tuberculosis complex (MTBC) exhibit high similarity, they are characterized by differences with respect to virulence, immune response, and transmissibility. To understand the virulence of these bacteria and identify potential novel therapeutic targets, we systemically investigated the total cell protein contents of virulent H37Rv, attenuated H37Ra, and avirulent M. bovis BCG vaccine strains at the log and stationary phases, based on tandem mass tag (TMT) quantitative proteomics. Data analysis revealed that we obtained deep-coverage protein identification and high quantification. Although 272 genetic variations were reported in H37Ra and H37Rv, they showed very little expression difference in log and stationary phase. Quantitative comparison revealed H37Ra and H37Rv had significantly dysregulation in log phase (227) compared with stationary phase (61). While BCG and H37Rv, and BCG and H37Ra showed notable differences in stationary phase (1171 and 1124) with respect to log phase (381 and 414). In the log phase, similar patterns of protein abundance were observed between H37Ra and BCG, whereas a more similar expression pattern was observed between H37Rv and H37Ra in the stationary phase. Bioinformatic analysis revealed that the upregulated proteins detected for H37Rv and H37Ra in log phase were virulence-related factors. In both log and stationary phases, the dysregulated proteins detected for BCG, which have also been identified as M. tuberculosis response proteins under dormancy conditions. We accordingly describe the proteomic profiles of H37Rv, H37Ra, and BCG, which we believe will potentially provide a better understanding of H37Rv pathogenesis, H37Ra attenuation, and BCG immuno protection.

Learning curves for drug response prediction in cancer cell lines.

BACKGROUND: Motivated by the size and availability of cell line drug sensitivity data, researchers have been developing machine learning (ML) models for predicting drug response to advance cancer treatment. As drug sensitivity studies continue generating drug response data, a common question is whether the generalization performance of existing prediction models can be further improved with more training data. METHODS: We utilize empirical learning curves for evaluating and comparing the data scaling properties of two neural networks (NNs) and two gradient boosting decision tree (GBDT) models trained on four cell line drug screening datasets. The learning curves are accurately fitted to a power law model, providing a framework for assessing the data scaling behavior of these models. RESULTS: The curves demonstrate that no single model dominates in terms of prediction performance across all datasets and training sizes, thus suggesting that the actual shape of these curves depends on the unique pair of an ML model and a dataset. The multi-input NN (mNN), in which gene expressions of cancer cells and molecular drug descriptors are input into separate subnetworks, outperforms a single-input NN (sNN), where the cell and drug features are concatenated for the input layer. In contrast, a GBDT with hyperparameter tuning exhibits superior performance as compared with both NNs at the lower range of training set sizes for two of the tested datasets, whereas the mNN consistently performs better at the higher range of training sizes. Moreover, the trajectory of the curves suggests that increasing the sample size is expected to further improve prediction scores of both NNs. These observations demonstrate the benefit of using learning curves to evaluate prediction models, providing a broader perspective on the overall data scaling characteristics. CONCLUSIONS: A fitted power law learning curve provides a forward-looking metric for analyzing prediction performance and can serve as a co-design tool to guide experimental biologists and computational scientists in the design of future experiments in prospective research studies.