Integrating Fréchet distance and AI reveals the evolutionary trajectory and origin of SARS-CoV-2.

A genome, composed of a precisely ordered sequence of four nucleotides (ATCG), encompasses a multitude of specific genome features like AAA motif. Mutations occurring within a genome disrupt the sequential order and composition of these features, thereby influencing the evolutionary trajectories and yielding variants. The evolutionary relatedness between a variant and its ancestor can be estimated by assessing evolutionary distances across a spectrum of genome features. This study develops a novel, alignment-free algorithm that considers both the sequential order and composition of genome features, enabling computation of the Fréchet distance (Fr) across multiple genome features to quantify the evolutionary status of a variant. Integrating this algorithm with an artificial recurrent neural network (RNN) reveals the quantitative evolutionary trajectory and origin of SARS-CoV-2, a puzzle unsolved by alignment-based phylogenetics. The RNN generates the evolutionary trajectory from Fr data at two levels: genome sequence mutations and organism variants. At the genome sequence level, SARS-CoV-2 evolutionarily shortens its genome to enhance its infectious capacity. Mutating signature features, such as TTA and GCT, increases its infectious potential and drives its evolution. At the organism level, variants mutating a single biomarker possess low infectious potential. However, mutating multiple markers dramatically increases their infectious capacity, propelling the COVID-19 pandemic. SARS-CoV-2 likely originates from mink coronavirus variants, with its origin trajectory traced as follows: mink, cat, tiger, mouse, hamster, dog, lion, gorilla, leopard, bat, and pangolin. Together, mutating multiple signature features and biomarkers delineates the evolutionary trajectory of mink-origin SARS-CoV-2, leading to the COVID-19 pandemic.

Game Theory-Based Signal Control Considering Both Pedestrians and Vehicles in Connected Environment.

Signal control, as an integral component of traffic management, plays a pivotal role in enhancing the efficiency of traffic and reducing environmental pollution. However, the majority of signal control research based on game theory primarily focuses on vehicular perspectives, often neglecting pedestrians, who are significant participants at intersections. This paper introduces a game theory-based signal control approach designed to minimize and equalize the queued vehicles and pedestrians across the different phases. The Nash bargaining solution is employed to determine the optimal green duration for each phase within a fixed cycle length. Several simulation tests were carried out by SUMO software to assess the effectiveness of this proposed approach. We select the actuated signal control approach as the baseline to demonstrate the superiority and stability of the proposed control strategy. The simulation results reveal that the proposed approach is able to reduce pedestrian and vehicle delay, vehicle queue length, fuel consumption, and CO2 emissions under different demand levels and demand patterns. Furthermore, the proposed approach consistently achieves more equalized queue length for each lane compared to the actuated control strategy, indicating a higher degree of fairness.

Mining the human urine proteome for monitoring renal transplant injury.

The human urinary proteome provides an assessment of kidney injury with specific biomarkers for different kidney injury phenotypes. In an effort to fully map and decipher changes in the urine proteome and peptidome after kidney transplantation, renal allograft biopsy matched urine samples were collected from 396 kidney transplant recipients. Centralized and blinded histology data from paired graft biopsies was used to classify urine samples into diagnostic categories of acute rejection, chronic allograft nephropathy, BK virus nephritis, and stable graft. A total of 245 urine samples were analyzed by liquid chromatography-mass spectrometry using isobaric Tags for Relative and Absolute Quantitation (iTRAQ) reagents. From a group of over 900 proteins identified in transplant injury, a set of 131 peptides were assessed by selected reaction monitoring for their significance in accurately segregating organ injury causation and pathology in an independent cohort of 151 urine samples. Ultimately, a minimal set of 35 proteins were identified for their ability to segregate the 3 major transplant injury clinical groups, comprising the final panel of 11 urinary peptides for acute rejection (93% area under the curve [AUC]), 12 urinary peptides for chronic allograft nephropathy (99% AUC), and 12 urinary peptides for BK virus nephritis (83% AUC). Thus, urinary proteome discovery and targeted validation can identify urine protein panels for rapid and noninvasive differentiation of different causes of kidney transplant injury, without the requirement of an invasive biopsy.

Systematically dissecting the global mechanism of miRNA functions in mouse pluripotent stem cells.

BACKGROUND: MicroRNAs (miRNAs) critically modulate stem cell properties like pluripotency, but the fundamental mechanism remains largely unknown. METHOD: This study systematically analyzes multiple-omics data and builds a systems physical network including genome-wide interactions between miRNAs and their targets to reveal the systems mechanism of miRNA functions in mouse pluripotent stem cells. RESULTS: Globally, miRNAs directly repress the pluripotent core factors during differentiation state. Surprisingly, during the pluripotent state, the top important miRNAs do not directly regulate the pluripotent core factors as previously thought, but they only directly target the pluripotent signal pathways and directly repress developmental processes. Furthermore, at the pluripotent state miRNAs predominately repress DNA methyltransferases, the core enzymes for DNA methylation. The decreasing methylation repressed by miRNAs in turn activates the top miRNAs and pluripotent core factors, creating an active circuit system to modulate pluripotency. CONCLUSION: MiRNAs vary their functions with stem cell states. While miRNAs directly repress pluripotent core factors to facilitate differentiation during the differentiation state, they also help stem cells to maintain pluripotency by activating pluripotent cores through directly repressing DNA methylation systems and primarily inhibiting development in the pluripotent state.

All-trans retinoic acid modulates the balance of ADAMTS13 and VWF in human microvascular endothelial cells.

PURPOSE: To better understand the antithrombotic property of All-trans retinoic acid (ATRA), we investigated whether ATRA may affect the balance between ADAMTS13 and von Willebrand factor (VWF) in human microvascular endothelial cell. METHODS: Compared to tumor necrosis factor-alpha (TNF-α), we observed the effects of ATRA on the expression of ADAMTS13 and VWF. ADAMTS13mRNA in human microvascular endothelial cell (HMEC-1 cell line) were detected by real-time polymerase chain reaction amplification (RT-PCR). The levels of ADAMTS13 and VWF antigen were detected by western blot or enzyme-linked immunosorbent assay (ELISA), and the proteolytic activity of ADAMTS13 was also determined by using GST-VWF73-His peptide as a specific substrate. RESULTS: ATRA significantly upregulated the expression of ADAMTS13mRNA in HMEC-1, while TNF-α inhibited ADAMTS13mRNA expression. ATRA could reverse the inhibition expression of ADAMTS13 by TNF-α. The results were confirmed from the levels of ADAMTS13 protein and its activity, while ATRA had no significant affection on triggering release of VWF. CONCLUSIONS: This study provides the evidence that ATRA modulates the balance of ADAMTS13 and VWF in human microvascular endothelial cell, which might be a very relevant compartment for the antithrombotic property of ATRA.

Transcription factor complex AP-1 mediates inflammation initiated by Chlamydia pneumoniae infection.

Chlamydia pneumoniae is responsible for a high prevalence of respiratory infections worldwide and has been implicated in atherosclerosis. Inflammation is regulated by transcription factor (TF) networks. Yet, the core TF network triggered by chlamydiae remains largely unknown. Primary human coronary artery endothelial cells were mock-infected or infected with C. pneumoniae to generate human transcriptome data throughout the chlamydial developmental cycle. Using systems network analysis, the predominant TF network involved receptor, binding and adhesion and immune response complexes. Cells transfected with interfering RNA against activator protein-1 (AP-1) members FOS, FOSB, JUN and JUNB had significantly decreased expression and protein levels of inflammatory mediators interleukin (IL)6, IL8, CD38 and tumour necrosis factor compared with controls. These mediators have been shown to be associated with C. pneumoniae disease. Expression of AP-1 components was regulated by MAPK3K8, a MAPK pathway component. Additionally, knock-down of JUN and FOS showed significantly decreased expression of Toll-like receptor (TLR)3 during infection, implicating JUN and FOS in TLR3 regulation. TLR3 stimulation led to elevated IL8. These findings suggest that C. pneumoniae initiates signalling via TLR3 and MAPK that activate AP-1, a known immune activator in other bacteria not previously shown for chlamydiae, triggering inflammation linked to C. pneumoniae disease.

A web-server of cell type discrimination system.

Discriminating cell types is a daily request for stem cell biologists. However, there is not a user-friendly system available to date for public users to discriminate the common cell types, embryonic stem cells (ESCs), induced pluripotent stem cells (iPSCs), and somatic cells (SCs). Here, we develop WCTDS, a web-server of cell type discrimination system, to discriminate the three cell types and their subtypes like fetal versus adult SCs. WCTDS is developed as a top layer application of our recent publication regarding cell type discriminations, which employs DNA-methylation as biomarkers and machine learning models to discriminate cell types. Implemented by Django, Python, R, and Linux shell programming, run under Linux-Apache web server, and communicated through MySQL, WCTDS provides a friendly framework to efficiently receive the user input and to run mathematical models for analyzing data and then to present results to users. This framework is flexible and easy to be expended for other applications. Therefore, WCTDS works as a user-friendly framework to discriminate cell types and subtypes and it can also be expended to detect other cell types like cancer cells.

Conceptual breakthroughs of the long noncoding RNA functional system and its endogenous regulatory role in the cancerous regime.

Long noncoding RNAs (lncRNAs) derived from noncoding regions in the human genome were once regarded as junks with no biological significance, but recent studies have shown that these molecules are highly functional, prompting an explosion of studies on their biology. However, these recent efforts have only begun to recognize the biological significance of a small fraction (< 1%) of the lncRNAs. The basic concept of these lncRNA functions remains controversial. This controversy arises primarily from conventional biased observations based on limited datasets. Fortunately, emerging big data provides a promising path to circumvent conventional bias to understand an unbiased big picture of lncRNA biology and advance the fundamental principles of lncRNA biology. This review focuses on big data studies that break through the critical concepts of the lncRNA functional system and its endogenous regulatory roles in all cancers. lncRNAs have unique functional systems distinct from proteins, such as transcriptional initiation and regulation, and they abundantly interact with mitochondria and consume less energy. lncRNAs, rather than proteins as traditionally thought, function as the most critical endogenous regulators of all cancers. lncRNAs regulate the cancer regulatory regime by governing the endogenous regulatory network of all cancers. This is accomplished by dominating the regulatory network module and serving as a key hub and top inducer. These critical conceptual breakthroughs lay a blueprint for a comprehensive functional picture of the human genome. They also lay a blueprint for combating human diseases that are regulated by lncRNAs.

Safety and efficacy of a probiotic cocktail containing P. acidilactici and L. plantarum for gastrointestinal discomfort in endurance runners: randomized double-blinded crossover clinical trial.

Probiotics are increasingly used to treat conditions associated with gastrointestinal injury and permeability, including exercise-induced gastrointestinal discomfort. This study assessed safety and efficacy of a probiotic in altering the intestinal milieu and mitigating gastrointestinal symptoms (GIS) in endurance runners. In a double blind, crossover study, 16 runners were randomized to 4 weeks of daily supplementation with a probiotic cocktail containing Pediococcus acidilactici bacteria and Lactobacillus plantarum or placebo. Fasting blood and stool samples were collected for measurement of gut permeability markers, immune parameters, and microbiome analyses. Treadmill run tests were performed before and after treatment; participants ran at 65%-70% of VO2max at 27 °C for a maximum of 90 min or until fatigue/GIS developed. A blood sample was collected after the treadmill run test. In healthy individuals, 4 weeks of probiotic supplementation did not alter health parameters, although a marginal reduction in aspartate aminotransferase levels was observed with probiotic treatment only (p = 0.05). GIS, gut permeability-associated parameters (intestinal fatty acid binding protein, lipopolysaccharide binding protein, zonulin, and cytokines), and intestinal microbial content were not altered by the probiotic supplementation. Post-run measurements of GIS and gut-associated parameters did not differ between groups; however, the observed lack of differences is confounded by an absence of measurable functional outcome as GIS was not sufficiently induced during the run. Under the current study conditions, the probiotic was safe to use, and did not affect gut- or immune-associated parameters, or intestinal symptoms in a healthy population. The probiotic might reduce tissue damage, but more studies are warranted.

Ligand Activation of the Aryl Hydrocarbon Receptor Upregulates Epidermal Uridine Diphosphate Glucose Ceramide Glucosyltransferase and Glucosylceramides.

Ligand activation of the aryl hydrocarbon receptor (AHR) accelerates keratinocyte differentiation and the formation of the epidermal permeability barrier. Several classes of lipids, including ceramides, are critical to the epidermal permeability barrier. In normal human epidermal keratinocytes, the AHR ligand, 2,3,7,8-tetrachlorodibenzo-p-dioxin, increased RNA levels of ceramide metabolism and transport genes: uridine diphosphate glucose ceramide glucosyltransferase (UGCG), ABCA12, GBA1, and SMPD1. Levels of abundant skin ceramides were also increased by 2,3,7,8-tetrachlorodibenzo-p-dioxin. These included the metabolites synthesized by UGCG, glucosylceramides, and acyl glucosylceramides. Chromatin immunoprecipitation-sequence analysis and luciferase reporter assays identified UGCG as a direct AHR target. The AHR antagonist, GNF351, inhibited the 2,3,7,8-tetrachlorodibenzo-p-dioxin-mediated RNA and transcriptional increases. Tapinarof, an AHR ligand approved for the treatment of psoriasis, increased UGCG RNA, protein, and its lipid metabolites hexosylceramides as well as increased the RNA expression of ABCA12, GBA1, and SMPD1. In Ahr-null mice, Ugcg RNA and hexosylceramides were lower than those in the wild type. These results indicate that the AHR regulates the expression of UGCG, a ceramide-metabolizing enzyme required for ceramide trafficking, keratinocyte differentiation, and epidermal permeability barrier formation.

(Bortezomib plus lenalidomide/thalidomide)- vs. (bortezomib or lenalidomide/thalidomide)-containing regimens as induction therapy in newly diagnosed multiple myeloma: a meta-analysis of randomized controlled trials.

The aim of the study was to perform a meta-analysis of the efficacy and safety of (bortezomib plus lenalidomide/thalidomide)- vs. (bortezomib or lenalidomide/thalidomide)-containing regimens as induction therapy in newly diagnosed multiple myeloma. We searched electronic and printed sources for relevant articles published. Inclusion criteria was as follows: randomized controlled trials (RCT) of (bortezomib plus lenalidomide/thalidomide) vs. (bortezomib or lenalidomide/thalidomide)-containing regimens as induction therapy in newly diagnosed multiple myeloma. Two reviewers independently assessed potentially eligible studies and extracted relevant data. We retrieved five RCT studies including a total of 1,200 patients. Using the random-effects model to pool the five RCT with a statistically significant heterogeneity (P = 0.03; X² = 10.69; df = 4; I² = 63%), the weighted risk ratios of a complete response (CR) for (bortezomib plus lenalidomide/thalidomide)-containing regimens was 1.81 (P = 0.005; 95% CI: 1.20-2.73). When we excluded the study by Cavo et al. (Lancet 376:2075-2085, 2010), the pooled risk ratio for CR was 1.59 (P < 0.0001, 95% CI: 1.29-1.96) with no statistically significant heterogeneity (P = 0.54; X² = 2.14; df = 3; I² = 0%) among four RCT under the fixed effects mode. The pooled odds ratio for the main grade III/IV adverse events (the peripheral neuropathy, thrombotic events, and infections) were 1.76 (P = 0.32; 95% CI: 0.58-5.31), 0.92 (P = 0.76, 95% CI: 0.52-1.61), and 1.05 (P = 0.82, 95% CI: 0.70-1.57), respectively. Our analysis showed (bortezomib plus lenalidomide/thalidomide)-containing regimens as induction treatment in newly diagnosed multiple myeloma improved CR but did not increase the risk of major adverse events (the peripheral neuropathy, thrombotic events, and infections).

Noncoding RNAs endogenously rule the cancerous regulatory realm while proteins govern the normal.

Cancers evolve from normal tissues and share an endogenous regulatory realm distinctive from that of normal human tissues. Unearthing such an endogenous realm faces challenges due to heterogeneous biology data. This study computes petabyte level data and reveals the endogenous regulatory networks of normal and cancers and then unearths the most important endogenous regulators for normal and cancerous realm. In normal, proteins dominate the entire realm and trans-regulate their targets across chromosomes and ribosomal proteins serve as the most important drivers. However, in cancerous realm, noncoding RNAs dominate the whole realm and pseudogenes work as the most important regulators that cis-regulate their neighbors, in which they primarily regulate their targets within 1 million base pairs but they rarely regulate their cognates with complementary sequences as thought. Therefore, two distinctive mechanisms rule the normal and cancerous realm separately, in which noncoding RNAs endogenously regulate cancers, instead of proteins as currently conceptualized. This establishes a fundamental avenue to understand the basis of cancerous and normal physiology.

Distinctive functional regime of endogenous lncRNAs in dark regions of human genome.

>98% of the human genome is composed of noncoding regions and >93% of these noncoding regions are actively transcribed, suggesting their criticality in the human genome. Yet <1% of these regions have been functionally characterized, leaving most of the human genomes in the dark. Here, this study processes petabyte level data and systematically decodes endogenous lncRNAs located in unannotated regions of the human genome and deciphers a distinctive functional regime of lncRNAs hidden in massive RNAseq data. LncRNAs divergently distribute across chromosomes, independent of protein-coding regions. Their transcriptions rarely initiate on promoters through polymerase II, but rather partially on enhancers. Yet conventional enhancer markers (e.g. H3K4me1) only account for a small proportion of lncRNA transcriptions, suggesting alternatively unknown mechanisms initiating the majority of lncRNAs. Furthermore, lncRNA-self regulation also notably contributes to lncRNA activation. LncRNAs regulate broad bioprocesses, including transcription and RNA processing, cell cycle, respiration, response to stress, chromatin organization, post-translational modification, and development. Therefore, lncRNAs functionally govern their own regime distinctive from protein coding genes. This finding establishes a clear framework to comprehend human genome-wide lncRNA-lncRNA and lncRNA-protein coding gene regulations.

Noncoding RNAs and Deep Learning Neural Network Discriminate Multi-Cancer Types.

Detecting cancers at early stages can dramatically reduce mortality rates. Therefore, practical cancer screening at the population level is needed. To develop a comprehensive detection system to classify multiple cancer types, we integrated an artificial intelligence deep learning neural network and noncoding RNA biomarkers selected from massive data. Our system can accurately detect cancer vs. healthy objects with 96.3% of AUC of ROC (Area Under Curve of a Receiver Operating Characteristic curve), and it surprisingly reaches 78.77% of AUC when validated by real-world raw data from a completely independent data set. Even validating with raw exosome data from blood, our system can reach 72% of AUC. Moreover, our system significantly outperforms conventional machine learning models, such as random forest. Intriguingly, with no more than six biomarkers, our approach can easily discriminate any individual cancer type vs. normal with 99% to 100% AUC. Furthermore, a comprehensive marker panel can simultaneously multi-classify common cancers with a stable 82.15% accuracy rate for heterogeneous cancerous tissues and conditions. This detection system provides a promising practical framework for automatic cancer screening at population level. Key points: (1) We developed a practical cancer screening system, which is simple, accurate, affordable, and easy to operate. (2) Our system binarily classify cancers vs. normal with >96% AUC. (3) In total, 26 individual cancer types can be easily detected by our system with 99 to 100% AUC. (4) The system can detect multiple cancer types simultaneously with >82% accuracy.

Correlation Analysis and Prognostic Impacts of Biological Characteristics in Elderly Patients with Acute Myeloid Leukemia.

Background: The significant heterogeneity of elderly AML patients' biological features has caused stratification difficulties and adverse prognosis. This paper did a correlation study between their genetic mutations, clinical features, and prognosis to further stratify them. Methods: 90 newly diagnosed elderly acute myeloid leukemia (AML) patients (aged ≥60 years) who detected genetic mutations by next-generation sequencing (NGS) were enrolled between April 2015 and March 2021 in our medical center. Results: A total of 29 genetic mutations were identified in 82 patients among 90 cases with a frequency of 91.1%. DNMT3A, BCOR, U2AF1, and BCORL1 mutations were unevenly distributed among different FAB classifications (p < 0.05). DNMT3A, IDH2, NPM1, FLT3-ITD, ASXL1, IDH1, SRSF2, BCOR, NRAS, RUNX1, U2AF1, MPO, and WT1 mutations were distributed differently when an immunophenotype was expressed or not expressed (p<0.05). NPM1 and FLT3-ITD had higher mutation frequencies in patients with normal chromosome karyotypes than abnormal chromosome karyotypes (p<0.001, p=0.005). DNMT3A and NRAS mutations predicted lower CR rates. DNMT3A, TP53, and U2AF1 mutations were related to unfavorable OS. TET2 mutation with CD123+, CD11b+ or CD34- predicted lower CR rate. IDH2+/CD34- predicted lower CR rate. ASXL1+/CD38+ and SRSF2+/CD123- predicted shorter OS. Conclusion: The study showed specific correlations between elderly AML patients' genetic mutations and clinical features, some of which may impact prognosis.

[Clinical Study on Dynamic Detection of Minimal Residual Disease in Acute Myeloid Leukemia by Multiparameter Flow Cytometry].

OBJECTIVE: To investigate the prognostic significance of dynamic detection of minimal residual disease (MRD) in patients with acute myeloid leukemia (AML) by 8-color flow cytometry. METHODS: MRD of 282 AML patients who achieved remission after initial therapy was detected by 8-color flow cytometry. MRD threshold for predicting recurrence was determined by receiver operating characteristic (ROC) curve, and time from MRD-positive to clinical recurrence was analyzed. The differences in overall survival (OS) time and relapse-free survival (RFS) time of patients with different MRD-changes were compared, and the related factors of recurrence in patients with MRD-negative were analyzed by univariate and logistic regression analysis. RESULTS: ROC curve determined that the MFC-MRD threshold for predicting the recurrence of AML was 0.105%, and the recurrence rate of MRD-positive patients was significantly higher than that of MRD-negative patients [52.45% (75/143 cases) vs 35.97% (50/139 cases), P=0.005]. The patients in MRD persistent positive group and negative to positive group recurred earlier than those in positive to negative group and negative-positive fluctuation group (P<0.005). Survival analysis showed that OS and RFS time of patients with MRD persistent positive were significantly shorter than those of patients with MRD persistent negative, positive to negative, and negative-positive fluctuation (P<0.005). There was no significant difference in OS and RFS between MRD negative to positive group and MRD persistent positive group (P>0.005), either between MRD persistent negative group and MRD positive to negative group (P>0.005). Among 139 MRD-negative patients, 50 recurred. Univariate and logistic regression analysis showed that the risk of recurrence increased with the increase of white blood cells level (95%CI: 1.000-1.013, P=0.045). The risk of recurrence in patients without hematopoietic stem cell transplantation (HSCT) was 9.694 times higher than that in patients who received HSCT (95%CI: 1.720-54.651, P=0.010), and in the high-risk group was 5.848 times higher than that in the low-risk group (95%CI: 1.418-24.121, P=0.015). CONCLUSION: The prognosis of AML patients with different MRD changes is significantly different. No matter MRD-positive or MRD-negative at the initial remission, dynamic detection of MRD after treatment is more helpful to accurately guide treatment.

Palbociclib promotes the antitumor activity of Venetoclax plus Azacitidine against acute myeloid leukemia.

Around 70 % of patients diagnosed with acute myeloid leukemia (AML) survive less than 5 years due to drug resistance and disease relapse. Consequently, improved clinical treatments are urgently needed. Some but not all AML patients benefit from the combination of the BCL-2 inhibitor Venetoclax with the hypomethylation agent Azacitidine. Here we investigated the utility of employing the cyclin dependent kinase (CDK6) inhibitor Palbociclib to improve the efficacy of Venetoclax/Azacitidine combination therapy. Our analysis of publicly available RNA sequencing datasets showed CDK6 was highly expressed in the major acute forms of leukemia including AML. Consistently, using qPCR and flow cytometry we found that CDK6 was overexpressed in bone marrow mononuclear cells from AML patients compared to healthy controls. Subsequent in vitro testing of Palbociclib, Venetoclax and Azacitidine, alone and in combination against CDK6-overexpressing AML cells lines THP-1 and KG-1 and primary AML cells showed that the Palbociclib/Venetoclax/Azacitidine combination improved treatment efficacy compared to Venetoclax/Azacitidine treatment alone. Additional investigations in a subcutaneous KG-1 mouse model showed similarly the three-drug combination produced the most significant reductions in tumor load together with the least amount of spleen infiltration. We established Palbociclib functioned in combination with Venetoclax/Azacitidine by increasing the rates of apoptosis in AML cells. Further investigations revealed that Palbociclib does not affect BCL-2 activity but downregulated the anti-apoptotic proteins MCL-1 and BCL-XL, making AML cells more sensitive to Venetoclax/Azacitidine treatment. Our results propose that the Palbociclib/Venetoclax/Azacitidine regimen warrants further preclinical research for clinical application in AML patients.

Noncoding RNAs Serve as the Deadliest Universal Regulators of all Cancers.

Numerous cancer drivers have been identified, but they are specific to a given cancer type and condition; universal cancer drivers and universal cancer mechanisms still remain largely unclear. Here, we identified the deadliest universal drivers for all cancers via developing algorithms to analyze massive RNAseqs and clinical data from The Cancer Genome Atlas (TCGA). In general, noncoding RNAs primarily serve as the most important inducers and suppressors for all types of cancers. In particular, pseudogenes are primary inducers, and specifically the antisense RNA RP11-335K5.2 serves as the most universal cancerous driver, independently of the cancer type and condition. Therefore, noncoding RNAs, instead of proteins as conventionally thought, primarily drive cancer, which establishes a novel field for future cancer research and therapy.

The Impact of a Dried Fruit and Vegetable Supplement and Fiber Rich Shake on Gut and Health Parameters in Female Healthcare Workers: A Placebo-Controlled, Double-Blind, Randomized Clinical Trial.

Aim: Phytochemicals from fruits and vegetables are known to reduce inflammation and improve overall health. The objective of this study was to determine the effect of a fruit and vegetable concentrate (FVC) and high fiber component on the gut microbiome in an overweight/obese, female population. Methods: The study was a randomized, double blind, placebo-controlled trial with 57 asymptomatic, pre-menopausal, overweight/obese females between 25-50 years of age working in healthcare. Blood and fecal samples were collected before and after two, four and five months of daily supplementation. Metabolic parameters were measured, and the gut microbiome analyzed. Results: No effect was observed with FVC supplementation for blood lipids, glucose and immune parameters. There was an improvement in glucose clearance. The FVC supplement did not result in taxonomic alterations at phyla level, or changes in α or ß diversity, but reduced Bacteroides abundance and increased fecal butyrate. An additional high fiber component improved levels of health associated bacteria. Conclusion: The results suggest that a dried fruit and vegetable supplement, with a high fiber meal replacement can alter the intestinal microbiota and improve glucose clearance, suggesting that this combination of supplements can improve glucose metabolism and possibly reduce the risk of insulin resistance.

A systemic network for Chlamydia pneumoniae entry into human cells.

Bacterial entry is a multistep process triggering a complex network, yet the molecular complexity of this network remains largely unsolved. By employing a systems biology approach, we reveal a systemic bacterial-entry network initiated by Chlamydia pneumoniae, a widespread opportunistic pathogen. The network consists of nine functional modules (i.e., groups of proteins) associated with various cellular functions, including receptor systems, cell adhesion, transcription, and endocytosis. The peak levels of gene expression for these modules change rapidly during C. pneumoniae entry, with cell adhesion occurring at 5 min postinfection, receptor and actin activity at 25 min, and endocytosis at 2 h. A total of six membrane proteins (chemokine C-X-C motif receptor 7 [CXCR7], integrin beta 2 [ITGB2], platelet-derived growth factor beta polypeptide [PDGFB], vascular endothelial growth factor [VEGF], vascular cell adhesion molecule 1 [VCAM1], and GTP binding protein overexpressed in skeletal muscle [GEM]) play a key role during C. pneumoniae entry, but none alone is essential to prevent entry. The combination knockdown of three genes (coding for CXCR7, ITGB2, and PDGFB) significantly inhibits C. pneumoniae entry, but the entire network is resistant to the six-gene depletion, indicating a resilient network. Our results reveal a complex network for C. pneumoniae entry involving at least six key proteins.