Integrated analysis of SR-like protein kinases Sky1 and Sky2 links signaling networks with transcriptional regulation in Candida albicans

Fungal infections are a major global health burden where Candida albicans is among the most common fungal pathogen in humans and is a common cause of invasive candidiasis. Fungal phenotypes, such as those related to morphology, proliferation and virulence are mainly driven by gene expression, which is primarily regulated by kinase signaling cascades. Serine-arginine (SR) protein kinases are highly conserved among eukaryotes and are involved in major transcriptional processes in human and S. cerevisiae. Candida albicans harbors two SR protein kinases, while Sky2 is important for metabolic adaptation, Sky1 has similar functions as in S. cerevisiae. To investigate the role of these SR kinases for the regulation of transcriptional responses in C. albicans, we performed RNA sequencing of sky1Δ and sky2Δ and integrated a comprehensive phosphoproteome dataset of these mutants. Using a Systems Biology approach, we study transcriptional regulation in the context of kinase signaling networks. Transcriptomic enrichment analysis indicates that pathways involved in the regulation of gene expression are downregulated and mitochondrial processes are upregulated in sky1Δ. In sky2Δ, primarily metabolic processes are affected, especially for arginine, and we observed that arginine-induced hyphae formation is impaired in sky2Δ. In addition, our analysis identifies several transcription factors as potential drivers of the transcriptional response. Among these, a core set is shared between both kinase knockouts, but it appears to regulate different subsets of target genes. To elucidate these diverse regulatory patterns, we created network modules by integrating the data of site-specific protein phosphorylation and gene expression with kinase-substrate predictions and protein-protein interactions. These integrated signaling modules reveal shared parts but also highlight specific patterns characteristic for each kinase. Interestingly, the modules contain many proteins involved in fungal morphogenesis and stress response. Accordingly, experimental phenotyping shows a higher resistance to Hygromycin B for sky1Δ. Thus, our study demonstrates that a combination of computational approaches with integration of experimental data can offer a new systems biological perspective on the complex network of signaling and transcription. With that, the investigation of the interface between signaling and transcriptional regulation in C. albicans provides a deeper insight into how cellular mechanisms can shape the phenotype

EV products obtained from iPSC-derived MSCs show batch-to-batch variations in their ability to modulate allogeneic immune responses in vitro

Mesenchymal stromal cells (MSCs) have demonstrated therapeutic potential in diverse clinical settings, largely due to their ability to produce extracellular vesicles (EVs). These EVs play a pivotal role in modulating immune responses, transforming pro-inflammatory cues into regulatory signals that foster a pro-regenerative milieu. Our previous studies identified the variability in the immunomodulatory effects of EVs sourced from primary human bone marrow MSCs as a consistent challenge. Given the limited proliferation of primary MSCs, protocols were advanced to derive MSCs from GMP-compliant induced pluripotent stem cells (iPSCs), producing iPSC-derived MSCs (iMSCs) that satisfied rigorous MSC criteria and exhibited enhanced expansion potential. Intriguingly, even though obtained iMSCs contained the potential to release immunomodulatory active EVs, the iMSC-EV products displayed batch-to-batch functional inconsistencies, mirroring those from bone marrow counterparts. We also discerned variances in EV-specific protein profiles among independent iMSC-EV preparations. Our results underscore that while iMSCs present an expansive growth advantage, they do not overcome the persistent challenge of functional variability of resulting MSC-EV products. Once more, our findings accentuate the crucial need for batch-to-batch functional testing, ensuring discrimination of effective and ineffective MSC-EV products for considered downstream applications

Lenalidomide and dexamethasone in relapsed/refractory immunoglobulin light chain (AL) amyloidosis: results from a large cohort of patients with long follow-up.

SummaryLenalidomide and dexamethasone (RD) is a standard treatment in relapsed/refractory immunoglobulin light chain (AL) amyloidosis (RRAL). We retrospectively investigated toxicity, efficacy and prognostic markers in 260 patients with RRAL. Patients received a median of two prior treatment lines (68% had been bortezomib‐refractory; 33% had received high‐dose melphalan). The median treatment duration was four cycles. The 3‐month haematological response rate was 31% [very good haematological response (VGHR) in 18%]. The median follow‐up was 56·5 months and the median overall survival (OS) and haematological event‐free survival (haemEFS) were 32 and 9 months. The 2‐year dialysis rate was 15%. VGHR resulted in better OS (62 vs. 26 months, P < 0·001). Cardiac progression predicted worse survival (22 vs. 40 months, P = 0·027), although N‐terminal prohormone of brain natriuretic peptide (NT‐proBNP) increase was frequently observed. Multivariable analysis identified these prognostic factors: NT‐proBNP for OS [hazard ratio (HR) 1·71; P < 0·001]; gain 1q21 for haemEFS (HR 1·68, P = 0·014), with a trend for OS (HR 1·47, P = 0·084); difference between involved and uninvolved free light chains (dFLC) and light chain isotype for OS (HR 2·22, P < 0·001; HR 1·62, P = 0·016) and haemEFS (HR 1·88, P < 0·001; HR 1·59, P = 0·008). Estimated glomerular filtration rate (HR 0·71, P = 0·004) and 24‐h proteinuria (HR 1·10, P = 0·004) were prognostic for renal survival. In conclusion, clonal and organ biomarkers at baseline identify patients with favourable outcome, while VGHR and cardiac progression define prognosis during RD treatment

Transcriptomic buffering of cryptic genetic variation contributes to meningococcal virulence

Ampattu BJ, Hagmann L, Liang C, et al. Transcriptomic buffering of cryptic genetic variation contributes to meningococcal virulence. BMC Genomics. 2017;18(1): 282.Background: Commensal bacteria like Neisseria meningitidis sometimes cause serious disease. However, genomic comparison of hyperinvasive and apathogenic lineages did not reveal unambiguous hints towards indispensable virulence factors. Here, in a systems biological approach we compared gene expression of the invasive strain MC58 and the carriage strain alpha 522 under different ex vivo conditions mimicking commensal and virulence compartments to assess the strain-specific impact of gene regulation on meningococcal virulence. Results: Despite indistinguishable ex vivo phenotypes, both strains differed in the expression of over 500 genes under infection mimicking conditions. These differences comprised in particular metabolic and information processing genes as well as genes known to be involved in host-damage such as the nitrite reductase and numerous LOS biosynthesis genes. A model based analysis of the transcriptomic differences in human blood suggested ensuing metabolic flux differences in energy, glutamine and cysteine metabolic pathways along with differences in the activation of the stringent response in both strains. In support of the computational findings, experimental analyses revealed differences in cysteine and glutamine auxotrophy in both strains as well as a strain and condition dependent essentiality of the (p) ppGpp synthetase gene relA and of a short non-coding AT-rich repeat element in its promoter region. Conclusions: Our data suggest that meningococcal virulence is linked to transcriptional buffering of cryptic genetic variation in metabolic genes including global stress responses. They further highlight the role of regulatory elements for bacterial virulence and the limitations of model strain approaches when studying such genetically diverse species as N. meningitidis

A critical evaluation of network and pathway based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single gene classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single gene classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single gene classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single gene sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single gene classifiers for predicting outcome in breast cancer

Association of Chronic Kidney Disease With Plasma NfL and Other Biomarkers of Neurodegeneration: The H70 Birth Cohort Study in Gothenburg

BACKGROUND AND OBJECTIVES: Studies associate chronic kidney disease (CKD) with neurodegeneration. This study investigated the relation between kidney function, blood, cerebrospinal fluid (CSF), and structural brain MRI markers of neurodegeneration, in a sample including individuals with and without CKD. METHODS: Participants from the Gothenburg H70 Birth Cohort Study, with data on plasma-neurofilament light (P-NfL), estimated glomerular filtration rate (eGFR) and structural brain MRI were included. Participants were invited to also have CSF collected. The primary endpoint of the present study was to determine any association between CKD and P-NfL. Secondary endpoints included cross-sectional associations between CKD, eGFR and cerebrospinal fluid (CSF)- and MRI-derived markers of neurodegeneration and Alzheimer's disease (AD) pathology (MRI: cortical thickness, hippocampal volume, lateral ventricle volume, white matter lesion volume; CSF: β-amyloid (Aβ) 42, Aβ42/40, Aβ42/p-tau, t-tau, p-tau, NfL). Participants with P-NfL and eGFR at baseline were re-examined on eGFR, 5.5 (5.3; 6.1) years (median; IQR) after the first visit, and the predictive value of P-NfL levels on incident CKD was estimated longitudinally, using a Cox proportional hazards model. RESULTS: We included 744 participants, 668 without CKD (Age 71 (70; 71) years, 50% males) and 76 with CKD (age 71 (70;71) years, 39% males). Biomarkers from cerebrospinal fluid (CSF) were analysed in 313 participants. 558 individuals returned for a re-examination of eGFR (75% response rate, age 76 (76; 77), 48% males, 76 new cases of CKD). Participants with CKD had higher P-NfL levels than those with normal kidney function (median; 18.8 versus 14.0 pg/mL, p<0.001), while MRI and CSF markers were similar between the groups. P-NfL was independently associated with CKD after adjustment for confounding variables, including hypertension and diabetes (OR; 3.231, p<0.001), in a logistic regression model. eGFR, and CSF Aβ 42/40: R=0.23, p=0.004 correlated in participants with Aβ42 pathology. P-NfL levels in the highest quartile were associated with incident CKD at follow-up (HR; 2.08 (1.14: 4.50)). DISCUSSION: In a community-based cohort of 70-year olds, P-NfL was associated with both prevalent and incident CKD, while CSF and/or imaging measures did not differ by CKD status. Participants with CKD and dementia presented similar levels of P-NfL

Genome-Wide Association Analyses Point to Candidate Genes for Electric Shock Avoidance in Drosophila melanogaster

Electric shock is a common stimulus for nociception-research and the most widely used reinforcement in aversive associative learning experiments. Yet, nothing is known about the mechanisms it recruits at the periphery. To help fill this gap, we undertook a genome-wide association analysis using 38 inbred Drosophila melanogaster strains, which avoided shock to varying extents. We identified 514 genes whose expression levels and/or sequences covaried with shock avoidance scores. We independently scrutinized 14 of these genes using mutants, validating the effect of 7 of them on shock avoidance. This emphasizes the value of our candidate gene list as a guide for follow-up research. In addition, by integrating our association results with external protein-protein interaction data we obtained a shock avoidance- associated network of 38 genes. Both this network and the original candidate list contained a substantial number of genes that affect mechanosensory bristles, which are hairlike organs distributed across the fly's body. These results may point to a potential role for mechanosensory bristles in shock sensation. Thus, we not only provide a first list of candidate genes for shock avoidance, but also point to an interesting new hypothesis on nociceptive mechanisms

The coactivator role of histone deacetylase 3 in IL-1-signaling involves deacetylation of p65 NF-κB

Histone deacetylase (HDAC) 3, as a cofactor in co-repressor complexes containing silencing mediator for retinoid or thyroid-hormone receptors (SMRT) and nuclear receptor co-repressor (N-CoR), has been shown to repress gene transcription in a variety of contexts. Here, we reveal a novel role for HDAC3 as a positive regulator of IL-1-induced gene expression. Various experimental approaches involving RNAi-mediated knockdown, conditional gene deletion or small molecule inhibitors indicate a positive role of HDAC3 for transcription of the majority of IL-1-induced human or murine genes. This effect was independent from the gene regulatory effects mediated by the broad-spectrum HDAC inhibitor trichostatin A (TSA) and thus suggests IL-1-specific functions for HDAC3. The stimulatory function of HDAC3 for inflammatory gene expression involves a mechanism that uses binding to NF-κB p65 and its deacetylation at various lysines. NF-κB p65-deficient cells stably reconstituted to express acetylation mimicking forms of p65 (p65 K/Q) had largely lost their potential to stimulate IL-1-triggered gene expression, implying that the co-activating property of HDAC3 involves the removal of inhibitory NF-κB p65 acetylations at K122, 123, 314 and 315. These data describe a novel function for HDAC3 as a co-activator in inflammatory signaling pathways and help to explain the anti-inflammatory effects frequently observed for HDAC inhibitors in (pre)clinical us