Altered infective competence of the human gut microbiome in COVID-19

in Microbiome (2023), 11(1), 46

BACKGROUND: Infections with SARS-CoV-2 have a pronounced impact on the gastrointestinal tract and its resident microbiome. Clear differences between severe cases of infection and healthy individuals have ... [more ▼]

BACKGROUND: Infections with SARS-CoV-2 have a pronounced impact on the gastrointestinal tract and its resident microbiome. Clear differences between severe cases of infection and healthy individuals have been reported, including the loss of commensal taxa. We aimed to understand if microbiome alterations including functional shifts are unique to severe cases or a common effect of COVID-19. We used high-resolution systematic multi-omic analyses to profile the gut microbiome in asymptomatic-to-moderate COVID-19 individuals compared to a control group. RESULTS: We found a striking increase in the overall abundance and expression of both virulence factors and antimicrobial resistance genes in COVID-19. Importantly, these genes are encoded and expressed by commensal taxa from families such as Acidaminococcaceae and Erysipelatoclostridiaceae, which we found to be enriched in COVID-19-positive individuals. We also found an enrichment in the expression of a betaherpesvirus and rotavirus C genes in COVID-19-positive individuals compared to healthy controls. CONCLUSIONS: Our analyses identified an altered and increased infective competence of the gut microbiome in COVID-19 patients. Video Abstract. [less ▲]

PARK7/DJ-1 promotes pyruvate dehydrogenase activity and maintains T(reg) homeostasis during ageing.

in Nature metabolism (2022), 4(5), 589-607

Pyruvate dehydrogenase (PDH) is the gatekeeper enzyme of the tricarboxylic acid (TCA) cycle. Here we show that the deglycase DJ-1 (encoded by PARK7, a key familial Parkinson's disease gene) is a pacemaker ... [more ▼]

Pyruvate dehydrogenase (PDH) is the gatekeeper enzyme of the tricarboxylic acid (TCA) cycle. Here we show that the deglycase DJ-1 (encoded by PARK7, a key familial Parkinson's disease gene) is a pacemaker regulating PDH activity in CD4(+) regulatory T cells (T(reg) cells). DJ-1 binds to PDHE1-β (PDHB), inhibiting phosphorylation of PDHE1-α (PDHA), thus promoting PDH activity and oxidative phosphorylation (OXPHOS). Park7 (Dj-1) deletion impairs T(reg) survival starting in young mice and reduces T(reg) homeostatic proliferation and cellularity only in aged mice. This leads to increased severity in aged mice during the remission of experimental autoimmune encephalomyelitis (EAE). Dj-1 deletion also compromises differentiation of inducible T(reg) cells especially in aged mice, and the impairment occurs via regulation of PDHB. These findings provide unforeseen insight into the complicated regulatory machinery of the PDH complex. As T(reg) homeostasis is dysregulated in many complex diseases, the DJ-1-PDHB axis represents a potential target to maintain or re-establish T(reg) homeostasis. [less ▲]

DJ‐1 depletion prevents immunoaging in T‐cell compartments.

in EMBO Reports (2022), 23(3),

Decline in immune function during aging increases susceptibility to different aging‐related diseases. However, the underlying molecular mechanisms, especially the genetic factors contributing to imbalance ... [more ▼]

Decline in immune function during aging increases susceptibility to different aging‐related diseases. However, the underlying molecular mechanisms, especially the genetic factors contributing to imbalance of naïve/memory T‐cell subpopulations, still remain largely elusive. Here, we show that loss of DJ‐1 encoded by PARK7/DJ‐1, causing early‐onset familial Parkinson’s disease (PD), unexpectedly diminished signs of immunoaging in T‐cell compartments of both human and mice. Compared with two gender‐matched unaffected siblings of similar ages, the index PD patient with DJ‐1 deficiency showed a decline in many critical immunoaging features, including almost doubled non‐senescent T cells. The observation was further consolidated by the results in 45‐week‐old DJ‐1 knockout mice. Our data demonstrated that DJ‐1 regulates several immunoaging features via hematopoietic‐intrinsic and naïve‐CD8‐intrinsic mechanisms. Mechanistically, DJ‐1 depletion reduced oxidative phosphorylation (OXPHOS) and impaired TCR sensitivity in naïve CD8 T cells at a young age, accumulatively leading to a reduced aging process in T‐cell compartments in older mice. Our finding suggests an unrecognized critical role of DJ‐1 in regulating immunoaging, discovering a potent target to interfere with immunoaging‐ and aging‐associated diseases. [less ▲]

DJ-1 depletion slows down immunoaging in T-cell compartments

Report (2021)

Decline in immune function during aging increases susceptibility to different aging related diseases. However, the underlying molecular mechanisms, especially the genetic factors contributing to imbalance ... [more ▼]

Decline in immune function during aging increases susceptibility to different aging related diseases. However, the underlying molecular mechanisms, especially the genetic factors contributing to imbalance of naïve/memory T-cell subpopulations, still remain largely elusive. Here we show that loss of DJ-1 encoded by PARK7 /DJ-1, causing early-onset familial Parkinson’s disease (PD), unexpectedly delayed immunoaging in both human and mice. Compared with two gender-matched unaffected sibling carriers of similar ages, the index PD patient with DJ-1 deficiency showed a decline in many critical immunoaging features, including almost doubled frequencies of non-senescent T cells. The observation of a ‘younger’ immune system in the index patient was further consolidated by the results in aged DJ-1 knockout mice. Our data from bone marrow chimera models and adoptive transfer experiments demonstrated that DJ-1 regulates several immunoaging features via hematopoietic-intrinsic and naïve-CD8-intrinsic mechanisms. Our finding suggests an unrecognized critical role of DJ-1 in regulating immunoaging, discovering a potent target to interfere with immunoaging- and aging-associated diseases. [less ▲]

Construction and verification of the transcriptional regulatory response network of Streptococcus mutans upon treatment with the biofilm inhibitor carolacton

in BMC Genomics (2014), 15

Carolacton is a newly identified secondary metabolite causing altered cell morphology and death of Streptococcus mutans biofilm cells. To unravel key regulators mediating these effects, the ... [more ▼]

Carolacton is a newly identified secondary metabolite causing altered cell morphology and death of Streptococcus mutans biofilm cells. To unravel key regulators mediating these effects, the transcriptional regulatory response network of S. mutans biofilms upon carolacton treatment was constructed and analyzed. A systems biological approach integrating timeresolved transcriptomic data, reverse engineering, transcription factor binding sites, and experimental validation was carried out. The co-expression response network constructed from transcriptomic data using the reverse engineering algorithm called the Trend Correlation method consisted of 8284 gene-pairs. The regulatory response network inferred by superimposing transcription factor binding site information into the co-expression network comprised 329 putative transcriptional regulatory interactions and could be classified into 27 sub-networks each co-regulated by a transcription factor. These sub-networks were significantly enriched with genes sharing common functions. The regulatory response network displayed global hierarchy and network motifs as observed in model organisms. The sub-networks modulated by the pyrimidine biosynthesis regulator PyrR, the glutamine synthetase repressor GlnR, the cysteine metabolism regulator CysR, global regulators CcpA and CodY and the two component system response regulators VicR and MbrC among others could putatively be related to the physiological effect of carolacton. The predicted interactions from the regulatory network between MbrC, known to be involved in cell envelope stress response, and the murMN-SMU_718c genes encoding peptidoglycan biosynthetic enzymes were experimentally confirmed using Electro Mobility Shift Assays. Furthermore, gene deletion mutants of five predicted key regulators from the response networks were constructed and their sensitivities towards carolacton were investigated. Deletion of cysR, the node having the highest connectivity among the regulators chosen from the regulatory network, resulted in a mutant which was insensitive to carolacton thus demonstrating not only the essentiality of cysR for the response of S. mutans biofilms to carolacton but also the relevance of the predicted network. The network approach used in this study revealed important regulators and interactions as part of the response mechanisms of S. mutans biofilm cells to carolacton. It also opens a door for further studies into novel drug targets against streptococci. [less ▲]

Identification of molecular sub-networks associated with cell survival in a chronically SIVmac-infected human CD4+ T cell line.

in Virology Journal (2014), 11

BACKGROUND: The deciphering of cellular networks to determine susceptibility to infection by HIV or the related simian immunodeficiency virus (SIV) is a major challenge in infection biology. RESULTS: Here ... [more ▼]

BACKGROUND: The deciphering of cellular networks to determine susceptibility to infection by HIV or the related simian immunodeficiency virus (SIV) is a major challenge in infection biology. RESULTS: Here, we have compared gene expression profiles of a human CD4+ T cell line at 24 h after infection with a cell line of the same origin permanently releasing SIVmac. A new knowledge-based-network approach (Inter-Chain-Finder, ICF) has been used to identify sub-networks associated with cell survival of a chronically SIV-infected T cell line. Notably, the method can identify not only differentially expressed key hub genes but also non-differentially expressed, critical, 'hidden' regulators. Six out of the 13 predicted major hidden key regulators were among the landscape of proteins known to interact with HIV. Several sub-networks were dysregulated upon chronic infection with SIV. Most prominently, factors reported to be engaged in early stages of acute viral infection were affected, e.g. entry, integration and provirus transcription and other cellular responses such as apoptosis and proliferation were modulated. For experimental validation of the gene expression analyses and computational predictions, individual pathways/sub-networks and significantly altered key regulators were investigated further. We showed that the expression of caveolin-1 (Cav-1), the top hub in the affected protein-protein interaction network, was significantly upregulated in chronically SIV-infected CD4+ T cells. Cav-1 is the main determinant of caveolae and a central component of several signal transduction pathways. Furthermore, CD4 downregulation and modulation of the expression of alternate and co-receptors as well as pathways associated with viral integration into the genome were also observed in these cells. Putatively, these modifications interfere with re-infection and the early replication cycle and inhibit cell death provoked by syncytia formation and bystander apoptosis. CONCLUSIONS: Thus, by using the novel approach for network analysis, ICF, we predict that in the T cell line chronically infected with SIV, cellular processes that are known to be crucial for early phases of HIV /SIV replication are altered and cellular responses that result in cell death are modulated. These modifications presumably contribute to cell survival despite chronic infection. [less ▲]

PLAU inferred from a correlation network is critical for suppressor function of regulatory T cells.

in Molecular Systems Biology (2012), 8

Human FOXP3(+)CD25(+)CD4(+) regulatory T cells (Tregs) are essential to the maintenance of immune homeostasis. Several genes are known to be important for murine Tregs, but for human Tregs the genes and ... [more ▼]

Human FOXP3(+)CD25(+)CD4(+) regulatory T cells (Tregs) are essential to the maintenance of immune homeostasis. Several genes are known to be important for murine Tregs, but for human Tregs the genes and underlying molecular networks controlling the suppressor function still largely remain unclear. Here, we describe a strategy to identify the key genes directly from an undirected correlation network which we reconstruct from a very high time-resolution (HTR) transcriptome during the activation of human Tregs/CD4(+) T-effector cells. We show that a predicted top-ranked new key gene PLAU (the plasminogen activator urokinase) is important for the suppressor function of both human and murine Tregs. Further analysis unveils that PLAU is particularly important for memory Tregs and that PLAU mediates Treg suppressor function via STAT5 and ERK signaling pathways. Our study demonstrates the potential for identifying novel key genes for complex dynamic biological processes using a network strategy based on HTR data, and reveals a critical role for PLAU in Treg suppressor function. [less ▲]

Metabolic Networks, Chapter 10

in Junker, Björn H.; Schreiber, Falk (Eds.) Analysis of Biological Networks (2007)

Dynamic cumulative activity of transcription factors as a mechanism of quantitative gene regulation.

in Genome Biology (2007), 8(9), 181

BACKGROUND: The regulation of genes in multicellular organisms is generally achieved through the combinatorial activity of different transcription factors. However, the quantitative mechanisms of how a ... [more ▼]

BACKGROUND: The regulation of genes in multicellular organisms is generally achieved through the combinatorial activity of different transcription factors. However, the quantitative mechanisms of how a combination of transcription factors controls the expression of their target genes remain unknown. RESULTS: By using the information on the yeast transcription network and high-resolution time-series data, the combinatorial expression profiles of regulators that best correlate with the expression of their target genes are identified. We demonstrate that a number of factors, particularly time-shifts among the different regulators as well as conversion efficiencies of transcription factor mRNAs into functional binding regulators, play a key role in the quantification of target gene expression. By quantifying and integrating these factors, we have found a highly significant correlation between the combinatorial time-series expression profile of regulators and their target gene expression in 67.1% of the 161 known yeast three-regulator motifs and in 32.9% of 544 two-regulator motifs. For network motifs involved in the cell cycle, these percentages are much higher. Furthermore, the results have been verified with a high consistency in a second independent set of time-series data. Additional support comes from the finding that a high percentage of motifs again show a significant correlation in time-series data from stress-response studies. CONCLUSION: Our data strongly support the concept that dynamic cumulative regulation is a major principle of quantitative transcriptional control. The proposed concept might also apply to other organisms and could be relevant for a wide range of biotechnological applications in which quantitative gene regulation plays a role. [less ▲]

In search of functional association from time-series microarray data based on the change trend and level of gene expression.

in BMC Bioinformatics (2006), 7

BACKGROUND: The increasing availability of time-series expression data opens up new possibilities to study functional linkages of genes. Present methods used to infer functional linkages between genes ... [more ▼]

BACKGROUND: The increasing availability of time-series expression data opens up new possibilities to study functional linkages of genes. Present methods used to infer functional linkages between genes from expression data are mainly based on a point-to-point comparison. Change trends between consecutive time points in time-series data have been so far not well explored. RESULTS: In this work we present a new method based on extracting main features of the change trend and level of gene expression between consecutive time points. The method, termed as trend correlation (TC), includes two major steps: 1, calculating a maximal local alignment of change trend score by dynamic programming and a change trend correlation coefficient between the maximal matched change levels of each gene pair; 2, inferring relationships of gene pairs based on two statistical extraction procedures. The new method considers time shifts and inverted relationships in a similar way as the local clustering (LC) method but the latter is merely based on a point-to-point comparison. The TC method is demonstrated with data from yeast cell cycle and compared with the LC method and the widely used Pearson correlation coefficient (PCC) based clustering method. The biological significance of the gene pairs is examined with several large-scale yeast databases. Although the TC method predicts an overall lower number of gene pairs than the other two methods at a same p-value threshold, the additional number of gene pairs inferred by the TC method is considerable: e.g. 20.5% compared with the LC method and 49.6% with the PCC method for a p-value threshold of 2.7E-3. Moreover, the percentage of the inferred gene pairs consistent with databases by our method is generally higher than the LC method and similar to the PCC method. A significant number of the gene pairs only inferred by the TC method are process-identity or function-similarity pairs or have well-documented biological interactions, including 443 known protein interactions and some known cell cycle related regulatory interactions. It should be emphasized that the overlapping of gene pairs detected by the three methods is normally not very high, indicating a necessity of combining the different methods in search of functional association of genes from time-series data. For a p-value threshold of 1E-5 the percentage of process-identity and function-similarity gene pairs among the shared part of the three methods reaches 60.2% and 55.6% respectively, building a good basis for further experimental and functional study. Furthermore, the combined use of methods is important to infer more complete regulatory circuits and network as exemplified in this study. CONCLUSION: The TC method can significantly augment the current major methods to infer functional linkages and biological network and is well suitable for exploring temporal relationships of gene expression in time-series data. [less ▲]