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Background The dissemination of MBLs compromises effective use of many β-lactams in the treatment of patients with life-threatening bacterial infections. Predicted global increases in the prevalence of MBL-producing carbapenem-resistant Enterobacterales (CRE) are being realized, yielding infections that are untreatable with existing therapies including newly approved β-lactam/β-lactamase inhibitor combinations. Developing MBL inhibitors (MBLIs) now is essential to address the growing threat that MBL-producing CRE pose to patients. Methods A novel MBLI series was assessed by susceptibility testing and time–kill assays. Target activity and selectivity was evaluated using bacterial NDM, VIM and IMP enzyme assays and human matrix metallopeptidase enzyme assays, respectively, and cytotoxicity was assessed in HepG2 cells. In vivo efficacy of meropenem/MBLI combinations was evaluated in a mouse thigh infection model using an NDM-1-producing Escherichia coli strain. Results Combination of MBLIs with carbapenems reduced MICs for NDM/IMP/VIM-producing Enterobacterales by up to 128-fold compared with the carbapenems alone. Supplementation of meropenem with the promising compound 272 reduced the MIC90 from 128 to 0.25 mg/L in a panel of MBL-producing CRE clinical isolates (n = 115). Compound 272 restored the bactericidal activity of meropenem and was non-cytotoxic, potentiating the antimicrobial action of meropenem through specific inhibition of NDM, IMP and VIM. In vivo efficacy was achieved in a mouse thigh infection model with meropenem/272 dosed subcutaneously. Conclusions We have developed a series of rationally designed MBLIs that restore activity of carbapenems against NDM/IMP/VIM-producing Enterobacterales. This series warrants further development towards a novel combination therapy that combats antibiotic-resistant organisms, which pose a critical threat to human health.

RNA-binding proteins of the ZFP36 family are best known for inhibiting the expression of cytokines through binding to AU-rich elements in the 3' untranslated region and promoting mRNA decay. Here we identified an indispensable role for ZFP36L1 as the regulator of a post-transcriptional hub that determined the identity of marginal-zone B cells by promoting their proper localization and survival. ZFP36L1 controlled a gene-expression program related to signaling, cell adhesion and locomotion; it achieved this in part by limiting expression of the transcription factors KLF2 and IRF8, which are known to enforce the follicular B cell phenotype. These mechanisms emphasize the importance of integrating transcriptional and post-transcriptional processes by RNA-binding proteins for maintaining cellular identity among closely related cell types.

Background Decomposing a protein-protein interaction network (PPI network) into non-overlapping clusters or communities, sometimes called “network modules,” is an important way to explore functional roles of sets of genes. When the method to accomplish this decomposition is solely based on purely graph-theoretic measures of the interconnection structure of the network, this is often called unsupervised clustering or community detection. In this study, we compare unsupervised computational methods for decomposing a PPI network into non-overlapping modules. A method is preferred if it results in a large proportion of nodes being assigned to functionally meaningful modules, as measured by functional enrichment over terms from the Gene Ontology (GO). Results We compare the performance of three popular community detection algorithms with the same algorithms run after the network is pre-processed by removing and reweighting based on the diffusion state distance (DSD) between pairs of nodes in the network. We call this “detangling” the network. In almost all cases, we find that detangling the network based on the DSD distance reweighting provides more meaningful clusters. Conclusions Re-embedding using the DSD distance metric, before applying standard community detection algorithms, can assist in uncovering GO functionally enriched clusters in the yeast PPI network.

Background Decomposing a protein-protein interaction network (PPI network) into non-overlapping clusters or communities, sometimes called “network modules,” is an important way to explore functional roles of sets of genes. When the method to accomplish this decomposition is solely based on purely graph-theoretic measures of the interconnection structure of the network, this is often called unsupervised clustering or community detection. In this study, we compare unsupervised computational methods for decomposing a PPI network into non-overlapping modules. A method is preferred if it results in a large proportion of nodes being assigned to functionally meaningful modules, as measured by functional enrichment over terms from the Gene Ontology (GO). Results We compare the performance of three popular community detection algorithms with the same algorithms run after the network is pre-processed by removing and reweighting based on the diffusion state distance (DSD) between pairs of nodes in the network. We call this “detangling” the network. In almost all cases, we find that detangling the network based on the DSD distance reweighting provides more meaningful clusters. Conclusions Re-embedding using the DSD distance metric, before applying standard community detection algorithms, can assist in uncovering GO functionally enriched clusters in the yeast PPI network.

Summary Sequence-specific RNA binding proteins (RBP) are important regulators of the immune response. RBP modulate gene expression by regulating splicing, polyadenylation, localization, translation and decay of target mRNAs. Increasing evidence suggests that RBP play critical roles in the development, activation and function of lymphocyte populations in the immune system. This review will discuss the post-transcriptional regulation of gene expression by RBP during lymphocyte development, with particular focus on the Tristetraprolin family of RBP.

Introduction The importance of the antitumor activity of some antimicrobial peptides (AMPs) is being increasingly recognized. The antimicrobial peptide, tachyplesin, has been shown to exhibit anticancer properties and a linear, cysteine deleted analogue (CDT), was found to retain its antibacterial function. Objectives The objective was to test CDT and related analogues against normal mammalian, bacterial, and cancer cells to determine their effectiveness and then utilize specific assays to determine a possible mechanism of action. Methods We used sequence reversal and D-amino acids to synthesize four CDT analogues by solid phase peptide synthesis. A number of assays were used including liposome dye-leakage, antibacterial activity against both Gram-positive and Gram-negative bacterial strains, hemolytic assays, methyl thiazolyl tetrazolium (MTT), and apoptosis to examine their effectiveness as both AMPs and anti-cancer peptides (ACPs). We then tested the analogues for their ability to inhibit proliferation of the human lung cancer cell line, A549. Results We found that D-CDT exhibited the best bactericidal properties of those tested and was not damaging to red blood cells. Both D-CDT and reverse D-CDT showed a dose-dependent reduction of cell viability. However, D-CDT was most effective with an IC50 of 9.814 μM, a value 9-fold lower than that calculated for reverse D-CDT (90.16 μM). Apoptosis does not appear to be a mechanism by which D-CDT exerts its anticancer properties since > 100 μM was required to increase activation of caspase 3. Moreover, the ERK1/2 pathway is also unlikely since only a modest (20%) decrease of activity was observed with > 100 μM D-CDT. However, D-CDT was found to operate via a hyaluronan (HA)-dependent mechanism as pretreatment of the cells with hyaluronidase decreased the cytotoxic effects of D-CDT on A549 cells and increased its IC50 29-fold to 283.9 μM. Conclusion D-CDT is both an effective AMP and ACP, and likely exerts its anticancer effects through both membranolytic as well as an HA-mediated mechanism.

The novel bacterial topoisomerase inhibitor class is an investigational type of antibacterial inhibitor of DNA gyrase and topoisomerase IV that does not have cross-resistance with the quinolones. Here, we report the evaluation of the in vitro properties of a new series of this type of small molecule. Exemplar compounds selectively and potently inhibited the catalytic activities of Escherichia coli DNA gyrase and topoisomerase IV but did not block the DNA breakage-reunion step. Compounds showed broad-spectrum inhibitory activity against a wide range of Gram-positive and Gram-negative pathogens, including biodefence microorganisms and Mycobacterium tuberculosis . No cross-resistance with fluoroquinolone-resistant Staphylococcus aureus and E. coli isolates was observed. Measured MIC 90 values were 4 and 8 μg/ml against a panel of contemporary multidrug-resistant isolates of Acinetobacter baumannii and E. coli , respectively. In addition, representative compounds exhibited greater antibacterial potency than the quinolones against obligate anaerobic species. Spontaneous mutation rates were low, with frequencies of resistance typically −8 against E. coli and A. baumannii at concentrations equivalent to 4-fold the MIC. Compound-resistant E. coli mutants that were isolated following serial passage were characterized by whole-genome sequencing and carried a single Arg38Leu amino acid substitution in the GyrA subunit of DNA gyrase. Preliminary in vitro safety data indicate that the series shows a promising therapeutic index and potential for low human ether-a-go-go-related gene (hERG) inhibition (50% inhibitory concentration [IC 50 ], >100 μM). In summary, the compounds' distinct mechanism of action relative to the fluoroquinolones, whole-cell potency, low potential for resistance development, and favorable in vitro safety profile warrant their continued investigation as potential broad-spectrum antibacterial agents.

Decomposing a protein-protein interaction network (PPI network) into non-overlapping clusters or communities, sometimes called "network modules," is an important way to explore functional roles of sets of genes. When the method to accomplish this decomposition is solely based on purely graph-theoretic measures of the interconnection structure of the network, this is often called unsupervised clustering or community detection. In this study, we compare unsupervised computational methods for decomposing a PPI network into non-overlapping modules. A method is preferred if it results in a large proportion of nodes being assigned to functionally meaningful modules, as measured by functional enrichment over terms from the Gene Ontology (GO).We compare the performance of three popular community detection algorithms with the same algorithms run after the network is pre-processed by removing and reweighting based on the diffusion state distance (DSD) between pairs of nodes in the network. We call this "detangling" the network. In almost all cases, we find that detangling the network based on the DSD distance reweighting provides more meaningful clusters.Re-embedding using the DSD distance metric, before applying standard community detection algorithms, can assist in uncovering GO functionally enriched clusters in the yeast PPI network.

Background Cell-free RNA in amniotic fluid supernatant reflects developmental changes in gene expression in the living fetus, which includes genes that are specific to the central nervous system. Although it has been previously shown that central nervous system–specific transcripts are present in amniotic fluid supernatant, it is not known whether changes in the amniotic fluid supernatant transcriptome reflect the specific pathophysiologic condition of fetal central nervous system disorders. In myelomeningocele, there is open communication between the central nervous system and amniotic fluid. Objectives The purpose of this study was to identify molecular pathophysiologic changes and novel disease mechanisms that are specific to myelomeningocele by the analysis of amniotic fluid supernatant cell-free RNA in fetuses with open myelomeningocele. Study Design Amniotic fluid supernatant was collected from 10 pregnant women at the time of the open myelomeningocele repair in the second trimester (24.5±1.0 weeks); 10 archived amniotic fluid supernatant from sex and gestational age–matched euploid fetuses without myelomeningocele were used as controls (20.9±0.9 weeks). Differentially regulated gene expression patterns were analyzed with the use of human genome expression arrays. Results Fetuses with myelomeningocele had 284 differentially regulated genes (176 up- and 108 down-regulated) in amniotic fluid supernatant. Known genes that were associated with myelomeningocele ( PRICKLE2 , GLI3 , RAB2 3, HES1 , FOLR1 ) and novel dysregulated genes were identified in association with neurodevelopment and neuronal regeneration (up-regulated, GAP4 3 and ZEB1 ) or axonal growth and guidance (down-regulated, ACAP1 ). Pathway analysis demonstrated a significant contribution of inflammation to disease and a broad influence of Wnt signaling pathways ( Wnt1 , Wnt5A , ITPR1 ). Conclusion Transcriptomic analyses of living fetuses with myelomeningocele with the use of amniotic fluid supernatant cell-free RNA demonstrated differential regulation of specific genes and molecular pathways relevant to this central nervous system disorder, which resulted in a new understanding of pathophysiologic changes. The data also suggested the importance of pathways that involve secondary disease, such as inflammation, in myelomeningocele. These newly identified pathways may lead to hypotheses that can test novel therapeutic targets as adjuncts to fetal surgical repair.