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Evidence that psychosis always has a biological cause appears lacking, while evidence that it can be a reaction to life events appears increasingly strong. A broader approach may therefore be required, one allowing for the possibility of psychosis emerging, independently of biological causes, when a person's understandable attempts to solve difficult problems inadvertently create more problems. Efforts by helpers to simplistically explain or suppress psychosis may also then backfire and increase difficulties. Restoring balance may require accepting and integrating psychotic experiences, neither overvaluing them nor dismissing them as being without value. Some methods of working toward this goal are identified and discussed. [ABSTRACT FROM AUTHOR]

Experimental studies and theoretical considerations have shown that only a small subset of Escherichia coli proteins fold in vivo with the help of the GroE chaperone system. These proteins, termed GroE substrates, have been divided into three classes: (a) proteins that can fold independently, but are found to associate with GroEL; (b) proteins that require GroE when the cell is under stress; and (c) 'obligatory' proteins that require GroE assistance even under normal conditions. It remains unclear, however, why some proteins need GroE and others do not. Here, we review experimental and computational studies that addressed this question by comparing the sequences and structural, biophysical and evolutionary properties of GroE substrates with those of nonsubstrates. In general, obligatory substrates are found to have lower folding propensities and be more aggregation prone. GroE substrates are also more conserved than other proteins and tend to utilize more optimal codons, but this latter feature is less apparent for obligatory substrates. There is no evidence, however, for any specific sequence signatures although there is a tendency for sequence periodicity. Our review shows that reliable sequence- or structure-based predictions of GroE dependency remain a challenge. We suggest that the different classes of GroE substrates be studied separately and that proper control test sets (e.g. TIM barrel proteins that need GroE for folding versus TIM barrels that fold independently) be used more extensively in such studies. [ABSTRACT FROM AUTHOR]

Motivation: Prediction of interactions between protein residues (contact map prediction) can facilitate various aspects of 3D structure modeling. However, the accuracy of ab initio contact prediction is still limited. As structural genomics initiatives move ahead, solved structures of homologous proteins can be used as multiple templates to improve contact prediction of the major conformation of an unsolved target protein. Furthermore, multiple templates may provide a wider view of the protein's conformational space. However, successful usage of multiple structural templates is not straightforward, due to their variable relevance to the target protein, and because of data redundancy issues.Results: We present here an algorithm that addresses these two limitations in the use of multiple structure templates. First, the algorithm unites contact maps extracted from templates sharing high sequence similarity with each other in a fashion that acknowledges the possibility of multiple conformations. Next, it weights the resulting united maps in inverse proportion to their evolutionary distance from the target protein. Testing this algorithm against CASP8 targets resulted in high precision contact maps. Remarkably, based solely on structural data of remote homologues, our algorithm identified residue–residue interactions that account for all the known conformations of calmodulin, a multifaceted protein. Therefore, employing multiple templates, which improves prediction of contact maps, can also be used to reveal novel conformations. As multiple templates will soon be available for most proteins, our scheme suggests an effective procedure for their optimal consideration.Availability: A Perl script implementing the WMC algorithm described in this article is freely available for academic use at http://tau.ac.il/~haimash/WMC.Contact: kliger@compugen.co.ilSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Background: Long-range communication is very common in proteins but the physical basis of this phenomenon remains unclear. In order to gain insight into this problem, we decided to explore whether long-range interactions exist in lattice models of proteins. Lattice models of proteins have proven to capture some of the basic properties of real proteins and, thus, can be used for elucidating general principles of protein stability and folding. Results: Using a computational version of double-mutant cycle analysis, we show that long-range interactions emerge in lattice models even though they are not an input feature of them. The coupling energy of both short- and long-range pairwise interactions is found to become more positive (destabilizing) in a linear fashion with increasing 'contact-frequency', an entropic term that corresponds to the fraction of states in the conformational ensemble of the sequence in which the pair of residues is in contact. A mathematical derivation of the linear dependence of the coupling energy on 'contact-frequency' is provided. Conclusion: Our work shows how 'contact-frequency' should be taken into account in attempts to stabilize proteins by introducing (or stabilizing) contacts in the native state and/or through 'negative design' of non-native contacts. [ABSTRACT FROM AUTHOR]

Summary Recently, deep learning models, initially developed in the field of natural language processing (NLP), were applied successfully to analyze protein sequences. A major drawback of these models is their size in terms of the number of parameters needed to be fitted and the amount of computational resources they require. Recently, 'distilled' models using the concept of student and teacher networks have been widely used in NLP. Here, we adapted this concept to the problem of protein sequence analysis, by developing DistilProtBert, a distilled version of the successful ProtBert model. Implementing this approach, we reduced the size of the network and the running time by 50%, and the computational resources needed for pretraining by 98% relative to ProtBert model. Using two published tasks, we showed that the performance of the distilled model approaches that of the full model. We next tested the ability of DistilProtBert to distinguish between real and random protein sequences. The task is highly challenging if the composition is maintained on the level of singlet, doublet and triplet amino acids. Indeed, traditional machine-learning algorithms have difficulties with this task. Here, we show that DistilProtBert preforms very well on singlet, doublet and even triplet-shuffled versions of the human proteome, with AUC of 0.92, 0.91 and 0.87, respectively. Finally, we suggest that by examining the small number of false-positive classifications (i.e. shuffled sequences classified as proteins by DistilProtBert), we may be able to identify de novo potential natural-like proteins based on random shuffling of amino acid sequences. Availability and implementation https://github.com/yarongef/DistilProtBert. [ABSTRACT FROM AUTHOR]

Motivation Prediction and identification of core promoter elements and transcription factor binding sites is essential for understanding the mechanism of transcription initiation and deciphering the biological activity of a specific locus. Thus, there is a need for an up-to-date tool to detect and curate core promoter elements/motifs in any provided nucleotide sequences. Results Here, we introduce ElemeNT 2023—a new and enhanced version of the Elements Navigation Tool, which provides novel capabilities for assessing evolutionary conservation and for readily evaluating the quality of high-throughput transcription start site (TSS) datasets, leveraging preferential motif positioning. ElemeNT 2023 is accessible both as a fast web-based tool and via command line (no coding skills are required to run the tool). While this tool is focused on core promoter elements, it can also be used for searching any user-defined motif, including sequence-specific DNA binding sites. Furthermore, ElemeNT's CORE database, which contains predicted core promoter elements around annotated TSSs, is now expanded to cover 10 species, ranging from worms to human. In this applications note, we describe the new workflow and demonstrate a case study using ElemeNT 2023 for core promoter composition analysis of diverse species, revealing motif prevalence and highlighting evolutionary insights. We discuss how this tool facilitates the exploration of uncharted transcriptomic data, appraises TSS quality, and aids in designing synthetic promoters for gene expression optimization. Taken together, ElemeNT 2023 empowers researchers with comprehensive tools for meticulous analysis of sequence elements and gene expression strategies. Availability and implementation ElemeNT 2023 is freely available at https://www.juven-gershonlab.org/resources/element-v2023/. The source code and command line version of ElemeNT 2023 are available at https://github.com/OritAdato/ElemeNT. No coding skills are required to run the tool. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is the most common and aggressive primary brain tumor. GBM contains a small subpopulation of glioma stem cells (GSCs) that are implicated in treatment resistance, tumor infiltration, and recurrence, and are thereby considered important therapeutic targets. Recent clinical studies have suggested that the choice of general anesthetic (GA), particularly propofol, during tumor resection, affects subsequent tumor response to treatments and patient prognosis. In this study, we investigated the molecular mechanisms underlying propofol's anti-tumor effects on GSCs and their interaction with microglia cells. Propofol exerted a dose-dependent inhibitory effect on the self-renewal, expression of mesenchymal markers, and migration of GSCs and sensitized them to both temozolomide (TMZ) and radiation. At higher concentrations, propofol induced a large degree of cell death, as demonstrated using microfluid chip technology. Propofol increased the expression of the lncRNA BDNF-AS, which acts as a tumor suppressor in GBM, and silencing of this lncRNA partially abrogated propofol's effects. Propofol also inhibited the pro-tumorigenic GSC-microglia crosstalk via extracellular vesicles (EVs) and delivery of BDNF-AS. In conclusion, propofol exerted anti-tumor effects on GSCs, sensitized these cells to radiation and TMZ, and inhibited their pro-tumorigenic interactions with microglia via transfer of BDNF-AS by EVs. [ABSTRACT FROM AUTHOR]

Provides information on the Center of Knowledge for Bioinformatics Infrastructure, an initiative of the Israeli Ministry of Science to nationally strengthen bioinformatics education and infrastructure. Mission of the initiative; Purpose of developing a central repository for Israel bioinformatics tools; Stages of implementation.

Background: Neonatal intensive care unit (NICU) admission among term neonates is a rare event. The aim of this study was to study the association of the NICU admission of term neonates on the risk of long-term childhood mortality. Methods: A single-center case–control retrospective study between 2005 and 2019, including all in-hospital ≥ 37 weeks' gestation singleton live-born neonates. The center perinatal database was linked with the birth and death certificate registries of the Israeli Ministry of Internal Affairs. The primary aim of the study was to study the association between NICU admission and childhood mortality throughout a 15-year follow-up period. Results: During the study period, 206,509 births were registered; 192,527 (93.22%) term neonates were included in the study; 5292 (2.75%) were admitted to NICU. Throughout the follow-up period, the mortality risk for term neonates admitted to the NICU remained elevated; hazard ratio (HR), 19.72 [14.66, 26.53], (p < 0.001). For all term neonates, the mortality rate was 0.16% (n = 311); 47.9% (n = 149) of those had records of a NICU admission. The mortality rate by time points (ratio1:10,0000 births) related to the age at death during the follow-up period was as follows: 29, up to 7 days; 20, 7–28 days; 37, 28 days to 6 months; 21, 6 months to 1 year; 19, 1–2 years; 9, 2–3 years; 10, 3–4 years; and 27, 4 years and more. Following the exclusion of congenital malformations and chromosomal abnormalities, NICU admission remained the most significant risk factor associated with mortality of the study population, HRs, 364.4 [145.3; 913.3] for mortality in the first 7 days of life; 19.6 [12.1; 32.0] for mortality from 28 days through 6 months of life and remained markedly elevated after age 4 years; HR, 7.1 [3.0; 17.0]. The mortality risk related to the NICU admission event, adjusted for admission diagnoses remained significant; HR = 8.21 [5.43; 12.4]. Conclusions: NICU admission for term neonates is a pondering event for the risk of long-term childhood mortality. This group of term neonates may benefit from focused health care. [ABSTRACT FROM AUTHOR]

Triggering Receptor Expressed On Myeloid Cells 2 (TREM2) is a membrane protein expressed on immune cells, involved in neurodegenerative diseases and cancer. Recently, it was shown that TREM2 is expressed on lipid associated macrophages in adipose tissue, and that TREM2 knockout mice suffer from metabolic symptoms. Here, a computational study using public databases, brings direct evidence for the involvement of TREM2 in human obesity. First, we show a significant correlation between TREM2 expression levels and BMI in adipose tissues in samples from the GTEx database. This association was evident for males but not for females. Second, we identified in the UK Biobank cohort a coding SNP in TREM2 with a significant effect on BMI. Compared to previously identified SNPs associated with BMI, this SNP (rs2234256 SNP, L211P) has the strongest association, reflected in significantly higher BMI values of people carrying the SNP as heterozygous and even more for homozygous. Strikingly, this association was evident only for females. These observations suggest a novel gender-specific role of TREM2 in human obesity, and call for further studies to elucidate the mechanism by which this gene correlates with an obese phenotype. [ABSTRACT FROM AUTHOR]

Recently, in parallel to decrease in semen quality, the consumption of sugar has risen sharply. This provided the rationale to study the association between whole dietary sugar consumption and semen quality. Our aim was to investigate the association between sugar consumption and semen quality. The final cross-sectional study population (n = 280 of initial n = 593, after applying inclusion and exclusion criteria) attending routine semen analysis at sperm bank laboratory was subject to semen quality analysis according to WHO criteria (volume, sperm concentration, total sperm count, percentage total motility, and percentage normal morphology) and filled food frequency questionnaire (FFQ) and lifestyle questionnaire. Associations between consumed sugars and semen quality were analyzed using multivariate regression adjusted to relevant cofounders for 2 food components containing sugar including soft drinks (SoftD) and total added sugar to food products (SugProd). We found negative association between higher consumption of dietary sugar in all 2 dietary sub-categories and sperm concentration. Significant sperm concentration decrements of 18% and 23% were associated with SoftD median consumption of 0.2 drinks/day (IQR; 0.1–0.5 drinks/day). Significant sperm concentration decrements of 15% and 17% were associated with median SugProd consumption of 25 teaspoons of added sugar/day (IQR; 19–31 teaspoons of added sugar/day). In conclusion, our study findings demonstrate that sugar consumption is negatively associated with sperm concentration. [ABSTRACT FROM AUTHOR]

A growing number of familial Mediterranean fever (FMF) patients in Israel do not have a single country of origin for all four grandparents. We aimed to predict the Mediterranean fever gene (MEFV) variant most likely to be found for an individual FMF patient, by a machine learning approach. This study was conducted at the Sheba Medical Center, a referral center for FMF in Israel. All Jewish referrals included in this study carried an FMF associated variant in MEFV as shown by genetic testing performed between 2001 and 2017. We introduced the term 'origin score' to capture the dose and different combinations of the grandparents' origin. A machine learning approach was used to analyze the data. In a total of 1781 referrals included in this study, the p.Met694Val variant was the most common, and the variants p.Glu148Gln and p.Val726Ala second and third most common, respectively. Of 26 countries of origin analyzed, those that increased the likelihood of a referral to carry specific variants were identified in North Africa for p.Met694Val, Europe for p.Val726Ala, and west Asia for p.Glu148Gln. Fourteen of the studied countries did not show a highly probable variant. Based on our results, it is possible to describe an association between modern day origins of the three most common MEFV variant types and a geographical region. A strong geographic association could arise from positive selection of a specific MEFV variant conferring resistance to endemic infectious agents. [ABSTRACT FROM AUTHOR]

Objective: Neonatal intensive care unit (NICU) admission among term neonates is associated with significant morbidity and mortality, as well as high healthcare costs. A comprehensive NICU admission risk assessment using an integrated statistical approach for this rare admission event may be used to build a risk calculation algorithm for this group of neonates prior to delivery. Methods: A single-center case–control retrospective study was conducted between August 2005 and December 2019, including in-hospital singleton live born neonates, born at ≥37 weeks' gestation. Analyses included univariate and multivariable models combined with the machine learning gradient-boosting model (GBM). The primary aim of the study was to identify and quantify risk factors and causes of NICU admission of term neonates. Results: During the study period, 206,509 births were registered at the Shaare Zedek Medical Center. After applying the study exclusion criteria, 192,527 term neonates were included in the study; 5292 (2.75%) were admitted to the NICU. The NICU admission risk was significantly higher (ORs [95%CIs]) for offspring of nulliparous women (1.19 [1.07, 1.33]), those with diabetes mellitus or hypertensive complications of pregnancy (2.52 [2.09, 3.03] and 1.28 [1.02, 1.60] respectively), and for those born during the 37th week of gestation (2.99 [2.63, 3.41]; p < 0.001 for all), adjusted for congenital malformations and genetic syndromes. A GBM to predict NICU admission applied to data prior to delivery showed an area under the receiver operating characteristic curve of 0.750 (95%CI 0.743–0.757) and classified 27% as high risk and 73% as low risk. This risk stratification was significantly associated with adverse maternal and neonatal outcomes. Conclusion: The present study identified NICU admission risk factors for term neonates; along with the machine learning ranking of the risk factors, the highly predictive model may serve as a basis for individual risk calculation algorithm prior to delivery. We suggest that in the future, this type of planning of the delivery will serve different health systems, in both high- and low-resource environments, along with the NICU admission or transfer policy. [ABSTRACT FROM AUTHOR]

Models for prediction of allogeneic hematopoietic stem transplantation (HSCT) related mortality partially account for transplant risk. Improving predictive accuracy requires understating of prediction limiting factors, such as the statistical methodology used, number and quality of features collected, or simply the population size. Using an in-silico approach (i.e., iterative computerized simulations), based on machine learning (ML) algorithms, we set out to analyze these factors. A cohort of 25,923 adult acute leukemia patients from the European Society for Blood and Marrow Transplantation (EBMT) registry was analyzed. Predictive objective was non-relapse mortality (NRM) 100 days following HSCT. Thousands of prediction models were developed under varying conditions: increasing sample size, specific subpopulations and an increasing number of variables, which were selected and ranked by separate feature selection algorithms. Depending on the algorithm, predictive performance plateaued on a population size of 6,611–8,814 patients, reaching a maximal area under the receiver operator characteristic curve (AUC) of 0.67. AUCs’ of models developed on specific subpopulation ranged from 0.59 to 0.67 for patients in second complete remission and receiving reduced intensity conditioning, respectively. Only 3–5 variables were necessary to achieve near maximal AUCs. The top 3 ranking variables, shared by all algorithms were disease stage, donor type, and conditioning regimen. Our findings empirically demonstrate that with regards to NRM prediction, few variables “carry the weight” and that traditional HSCT data has been “worn out”. “Breaking through” the predictive boundaries will likely require additional types of inputs. [ABSTRACT FROM AUTHOR]

Trypanosoma brucei, the parasite that causes sleeping sickness, cycles between an insect and a mammalian host. However, the effect of RNA modifications such as pseudouridinylation on its ability to survive in these two different host environments is unclear. Here, two genome-wide approaches were applied for mapping pseudouridinylation sites (s) on small nucleolar RNA (snoRNA), 7SL RNA, vault RNA, and tRNAs from T. brucei. We show using HydraPsiSeq and RiboMeth-seq that the on C/D snoRNA guiding 20-O-methylation increased the efficiency of the guided modification on its target, rRNA. We found differential levels of s on these noncoding RNAs in the two life stages (insect host and mammalian host) of the parasite. Furthermore, tRNA isoform abundance and modifications were characterized in these two life stages demonstrating stage-specific regulation. We conclude that the differential modifications identified here may contribute to modulating the function of noncoding RNAs involved in rRNA processing, rRNA modification, protein synthesis, and protein translocation during cycling of the parasite between its two hosts. [ABSTRACT FROM AUTHOR]

How genome instability is harnessed for fitness gain despite its potential deleterious effects is largely elusive. An ideal system to address this important open question is provided by the protozoan pathogen Leishmania, which exploits frequent variations in chromosome and gene copy number to regulate expression levels. Using ecological genomics and experimental evolution approaches, we provide evidence that Leishmania adaptation relies on epistatic interactions between functionally associated gene copy number variations in pathways driving fitness gain in a given environment. We further uncover posttranscriptional regulation as a key mechanism that compensates for deleterious gene dosage effects and provides phenotypic robustness to genetically heterogenous parasite populations. Finally, we correlate dynamic variations in small nucleolar RNA (snoRNA) gene dosage with changes in ribosomal RNA 2'-O-methylation and pseudouridylation, suggesting translational control as an additional layer of parasite adaptation. Leishmania genome instability is thus harnessed for fitness gain by genome-dependent variations in gene expression and genome-independent compensatory mechanisms. This allows for polyclonal adaptation and maintenance of genetic heterogeneity despite strong selective pressure. The epistatic adaptation described here needs to be considered in Leishmania epidemiology and biomarker discovery and may be relevant to other fast-evolving eukaryotic cells that exploit genome instability for adaptation, such as fungal pathogens or cancer. [ABSTRACT FROM AUTHOR]

Non-covalent residue–residue contacts drive the folding of proteins and stabilize them. They may be local—i.e. involve residues that are close in sequence, or non-local. It has been suggested that, in most proteins, local contacts drive protein folding by providing crucial constraints of the conformational space, thus allowing proteins to fold. We compared residues that are involved in local contacts to residues that are involved in non-local contacts and found that, in most proteins, residues in non-local contacts are significantly more conserved evolutionarily than residues in local contacts. Moreover, non-local contacts are more structurally conserved: a contact between positions that are distant in sequence is more likely to exist in many structural homologues compared with a contact between positions that are close in sequence. These results provide new insights into the mechanisms of protein folding and may allow for better prediction of critical intra-chain contacts.Supplementary information: Supplementary data are available at Bioinformatics online.Contact: yanay@ofranlab.org [ABSTRACT FROM PUBLISHER]

Two different strategies for stabilizing proteins are (i) positive design in which the native state is stabilized and (ii) negative design in which competing non-native conformations are destabilized. Here, the circumstances under which one strategy might be favored over the other are explored in the case of lattice models of proteins and then generalized and discussed with regard to real proteins. The balance between positive and negative design of proteins is found to be determined by their average ''contact-frequency'', a property that corresponds to the fraction of states in the conformational ensemble of the sequence in which a pair of residues is in contact. Lattice model proteins with a high average contact-frequency are found to use negative design more than model proteins with a low average contact-frequency. A mathematical derivation of this result indicates that it is general and likely to hold also for real proteins. Comparison of the results of correlated mutation analysis for real proteins with typical contact-frequencies to those of proteins likely to have high contactfrequencies (such as disordered proteins and proteins that are dependent on chaperonins for their folding) indicates that the latter tend to have stronger interactions between residues that are not in contact in their native conformation. Hence, our work indicates that negative design is employed when insufficient stabilization is achieved via positive design owing to high contact-frequencies. [ABSTRACT FROM AUTHOR]

Small nucleolar RNAs constitute a family of newly discovered non-coding small RNAs, most of which function in guiding RNA modifications. Two prevalent types of modifications are 2′-O-methylation and pseudouridylation. The modification is directed by the formation of a canonical small nucleolar RNA-target duplex. Initially, RNA-guided modification was shown to take place on rRNA, but recent studies suggest that small nuclear RNA, mRNA, tRNA, and the trypanosome spliced leader RNA also undergo guided modifications. Trypanosomes contain more modifications and potentially more small nucleolar RNAs than yeast, and the increased number of modifications may help to preserve ribosome function under adverse environmental conditions during the cycling between the insect and mammalian host. The genome organisation in clusters carrying the two types of small nucleolar RNAs, C/D and H/ACA-like RNAs, resembles that in plants. However, the trypanosomatid H/ACA RNAs are similar to those found in Archaea and are composed of a single hairpin that may represent the primordial H/ACA RNA. In this review we summarise this new field of trypanosome small nucleolar RNAs, emphasising the open questions regarding the number of small nucleolar RNAs, the repertoire, genome organisation, and the unique function of guided modifications in these protozoan parasites. [Copyright &y& Elsevier]

The parasite Trypanosoma brucei cycles between an insect and a mammalian host and is the causative agent of sleeping sickness. Here, we performed high‐throughput mapping of pseudouridines (Ψs) on mRNA from two life stages of the parasite. The analysis revealed ~273 Ψs, including developmentally regulated Ψs that are guided by homologs of pseudouridine synthases (PUS1, 3, 5, and 7). Mutating the U that undergoes pseudouridylation in the 3′ UTR of valyl‐tRNA synthetase destabilized the mRNA level. To investigate the mechanism by which Ψ affects the stability of this mRNA, proteins that bind to the 3′ UTR were identified, including the RNA binding protein RBSR1. The binding of RBSR1 protein to the 3′ UTR was stronger when lacking Ψ compared to transcripts carrying the modification, suggesting that Ψ can inhibit the binding of proteins to their target and thus affect the stability of mRNAs. Consequently, Ψ modification on mRNA adds an additional level of regulation to the dominant post‐transcriptional control in these parasites. [ABSTRACT FROM AUTHOR]

Introduction and hypothesis: Obstetric anal sphincter injury (OASI) is a complication with substantial maternal morbidity. The aim of this study was to develop a machine learning model that would allow a personalized prediction algorithm for OASI, based on maternal and fetal variables collected at admission to labor. Materials and methods: We performed a retrospective cohort study at a tertiary university hospital. Included were term deliveries (live, singleton, vertex). A comparison was made between women diagnosed with OASI and those without such injury. For formation of a machine learning-based model, a gradient boosting machine learning algorithm was implemented. Evaluation of the performance model was achieved using the area under the receiver-operating characteristic curve (AUC). Results: Our cohort comprised 98,463 deliveries, of which 323 (0.3%) were diagnosed with OASI. Applying a machine learning model to data recorded during admission to labor allowed for individualized risk assessment with an AUC of 0.756 (95% CI 0.732–0.780). According to this model, a lower number of previous births, fewer pregnancies, decreased maternal weight and advanced gestational week elevated the risk for OASI. With regard to parity, women with one previous delivery had approximately 1/3 of the risk for OASI compared to nulliparous women (OR = 0.3 (0.23–0.39), p < 0.001), and women with two previous deliveries had 1/3 of the risk compared to women with one previous delivery (OR = 0.35 (0.21–0.60), p < 0.001). Conclusion: Our machine learning-based model stratified births to high or low risk for OASI, making it an applicable tool for personalized decision-making upon admission to labor. [ABSTRACT FROM AUTHOR]

Introduction: Epidural analgesia (EA) is an established option for efficient intrapartum analgesia. Meta-analyses have shown that EA differentially affects the first stage of labor but prolongs the second. The question of EA timing remains open. We aimed to investigate whether EA prolongs delivery in total and whether the EA administration timing vis-à-vis cervical dilation at catheter insertion is associated with a modulation of its effects on the duration of the first and second stages, as well as the rate of instrumental vaginal delivery in primiparas and multiparas.Material and Methods: A retrospective electronic medical records-based study of 18 870 singleton term deliveries occurring in our institution from 2003 to 2015. Cervical dilation was determined within a half-hour of EA administration. We examined whether cervical dilation at EA administration correlated with the duration of the first and/or second stage, with the rate of prolonged second stage, and with the rate of interventional delivery. The study group was stratified to 10 subgroups defined by 1-cm intervals of cervical dilation at EA administration. Logistic regression modeling was applied to analyze the association between EA timing and rate of instrumental delivery while controlling for possible confounders.Results: In primiparas, receiving EA correlated with longer medians of active first stage (+51 minutes; P < .001) and second stage (+55 minutes; P < .001). In multiparas, median increases in active first stage (+43 minutes; P < .001) and second stage (+8 minutes; P < .001) were noted. The timing of EA, vis-à-vis cervical dilation (1-10 cm) was not associated with a substantial modulation of these effects. Logistic regression showed that cervical dilation at EA was not associated with a higher instrumental vaginal delivery rate.Conclusions: Epidural analgesia prolonged the first and second stages of labor vs no epidural. Having EA was associated with a higher instrumental delivery rate but not with higher rates of maternal or neonatal complications, in primi- and multiparas. Importantly, the timing of EA, vis-à-vis cervical dilation, was not associated with substantial changes in the duration of labor stages or the instrumental delivery rate. Thus, EA may be offered early in the first stage of labor. [ABSTRACT FROM AUTHOR]

Background: While endeavors to reduce cesarean delivery (CD) rates are given priority worldwide, it is important to evaluate if these efforts place parturients and neonates at risk. CD performed in the second stage of labor carries higher risks of maternal and fetal complications and is a more challenging surgical procedure than that performed in the first stage or before labor. In a population with a low CD rate, we sought to evaluate the rate of maternal and fetal complications associated with unplanned CD (UCD) performed in the second vs. the first stage of labor, in primiparas and multiparas, as well as the risk factors leading to and the complications associated with UCD in the second stage of labor in this low-CD rate setting.Methods: This was a retrospective, electronic medical record-based study of 7,635 term and preterm singletons born via UCD in the period 2003-2015. Maternal and neonatal background and outcome parameters were compared between groups. Logistic regression modeling was applied to adjust for clinically and statistically significant risk factors.Results: UCD was more likely to be performed in the second stage of labor in mothers delivering larger fetuses (head circumference and body weight ≥90 centile) and those with persistent occiput posterior (POP) presentation. UCD in the second stage was strongly associated with serious maternal complications (excessive hemorrhage and fever) compared to UCD performed in the first stage, in both primiparas and multiparas.Conclusions: UCD performed in the second stage of labor, while less frequent than first-stage UCD, is more likely with larger neonates and POP presentation, and is associated with a higher rate of maternal complications in primiparas and multiparas. Complication rates in our low-CD-rate population did not exceed those reported in the literature from high-CD-rate areas. [ABSTRACT FROM AUTHOR]

The regulation of transcription initiation is critical for developmental and cellular processes. RNA polymerase II (Pol II) is recruited by the basal transcription machinery to the core promoter where Pol II initiates transcription. The core promoter encompasses the region from -40 to +40 bp relative to the +1 transcription start site (TSS). Core promoters may contain one or more core promoter motifs that confer specific properties to the core promoter, such as the TATA box, initiator (Inr) and motifs that are located downstream of the TSS, namely, motif 10 element (MTE), the downstream core promoter element (DPE) and the Bridge, a bipartite core promoter element. We had previously shown that Caudal, an enhancer-binding homeodomain transcription factor and a key regulator of the Hox gene network, is a DPE-specific activator. Interestingly, pair-rule proteins have been implicated in enhancer-promoter communication at the engrailed locus. Fushi tarazu (Ftz) is an enhancer-binding homeodomain transcription factor encoded by the ftz pair-rule gene. Ftz works in concert with its co-factor, Ftz-F1, to activate transcription. Here, we examined whether Ftz and Ftz-F1 activate transcription with a preference for a specific core promoter motif. Our analysis revealed that similarly to Caudal, Ftz and Ftz-F1 activate the promoter containing a TATA box mutation to significantly higher levels than the promoter containing a DPE mutation, thus demonstrating a preference for the DPE motif. We further discovered that Ftz target genes are enriched for a combination of functional downstream core promoter elements that are conserved among Drosophila species. Thus, the unique combination (Inr, Bridge and DPE) of functional downstream core promoter elements within Ftz target genes highlights the complexity of transcriptional regulation via the core promoter in the transcription of different developmental gene regulatory networks. [ABSTRACT FROM AUTHOR]

Objective: To study association of semen quality with a priori whole dietary pattern indexes, which reflect real-world dietary practices and the numerous combinations by which foods are consumed: Healthy Eating Index (HEI), Dietary Approaches to Stop Hypertension (DASH), alternate Mediterranean Diet score (aMED), and Alternative Healthy Eating Index (AHEI).Design: A cross-sectional single-center study.Setting: Hospital fertility center and university.Patient(s): A total of 280 men attending fertility center from 2012 to 2015.Intervention(s): Food frequency questionnaire (FFQ) and semen and sperm analysis.Main Outcome Measure(s): Food consumption with the use of FFQ and HEI, AHEI, aMED, DASH nutritional individual scoring indexes. Semen parameters, including semen volume, sperm concentration, motility, total count, and morphology.Result(s): Comparing the highest and lowest quartiles of the nutritional indexes, men in the highest quartiles of HEI, AHEI, aMed, and DASH indexes had significantly higher adjusted means of sperm concentration (by 10%, 45%, and 24% for HEI, AHEI, and DASH, respectively), normal sperm morphology (by 21% and 8% for AHEI and DASH, respectively), total sperm count (by 29% for AHEI), and sperm motility (by 6% and 11% for aMed and HEI, respectively).Conclusion(s): Adherence to any of the four dietary indexes is associated with better overall sperm quality, with AHEI best associated. Following our novel findings, we recommend using AHEI as a clinical and practical tool for public whole nutritional recommendation for semen quality. [ABSTRACT FROM AUTHOR]

The GroE chaperonin system, which comprises GroEL and GroES, assists protein folding in vivo and in vitro. It is conserved in all prokaryotes except in most, but not all, members of the class of mollicutes. In Escherichia coli, about 60 proteins were found to be obligatory clients of the GroE system. Here, we describe the properties of the homologs of these GroE clients in mollicutes and the evolution of chaperonins in this class of bacteria. Comparing the properties of these homologs in mollicutes with and without chaperonins enabled us to search for features correlated with the presence of GroE. Interestingly, no sequence-based features of proteins such as average length, amino acid composition and predicted folding/disorder propensity were found to be affected by the absence of GroE. Other properties such as genome size and number of proteins were also found to not differ between mollicute species with and without GroE. Our data suggest that two clades of mollicutes re-acquired the GroE system, thereby supporting the view that gaining the system occurred polyphyletically and not monophyletically, as previously debated. Our data also suggest that there might have been three isolated cases of lateral gene transfer from specific bacterial sources. Taken together, our data indicate that loss of GroE does not involve crossing a high evolutionary barrier and can be compensated for by a small number of changes within the few dozen client proteins. [ABSTRACT FROM AUTHOR]

The GroE chaperonin system in Escherichia coli comprises GroEL and GroES and facilitates ATP-dependent protein folding in vivo and in vitro Proteins with very similar sequences and structures can differ in their dependence on GroEL for efficient folding. One potential but unverified source for GroEL dependence is frustration, wherein not all interactions in the native state are optimized energetically, thereby potentiating slow folding and misfolding. Here, we chose enhanced green fluorescent protein as a model system and subjected it to random mutagenesis, followed by screening for variants whose in vivo folding displays increased or decreased GroEL dependence. We confirmed the altered GroEL dependence of these variants with in vitro folding assays. Strikingly, mutations at positions predicted to be highly frustrated were found to correlate with decreased GroEL dependence. Conversely, mutations at positions with low frustration were found to correlate with increased GroEL dependence. Further support for this finding was obtained by showing that folding of an enhanced green fluorescent protein variant designed computationally to have reduced frustration is indeed less GroEL-dependent. Our results indicate that changes in local frustration also affect partitioning in vivo between spontaneous and chaperonin-mediated folding. Hence, the design of minimally frustrated sequences can reduce chaperonin dependence and improve protein expression levels. [ABSTRACT FROM AUTHOR]

Background Risk scores for prediction of mortality 30-days following a ST-segment elevation myocardial infarction (STEMI) have been developed using a conventional statistical approach. Objective To evaluate an array of machine learning (ML) algorithms for prediction of mortality at 30-days in STEMI patients and to compare these to the conventional validated risk scores. Methods This was a retrospective, supervised learning, data mining study. Out of a cohort of 13,422 patients from the Acute Coronary Syndrome Israeli Survey (ACSIS) registry, 2782 patients fulfilled inclusion criteria and 54 variables were considered. Prediction models for overall mortality 30 days after STEMI were developed using 6 ML algorithms. Models were compared to each other and to the Global Registry of Acute Coronary Events (GRACE) and Thrombolysis In Myocardial Infarction (TIMI) scores. Results Depending on the algorithm, using all available variables, prediction models' performance measured in an area under the receiver operating characteristic curve (AUC) ranged from 0.64 to 0.91. The best models performed similarly to the Global Registry of Acute Coronary Events (GRACE) score (0.87 SD 0.06) and outperformed the Thrombolysis In Myocardial Infarction (TIMI) score (0.82 SD 0.06, p < 0.05). Performance of most algorithms plateaued when introduced with 15 variables. Among the top predictors were creatinine, Killip class on admission, blood pressure, glucose level, and age. Conclusions We present a data mining approach for prediction of mortality post-ST-segment elevation myocardial infarction. The algorithms selected showed competence in prediction across an increasing number of variables. ML may be used for outcome prediction in complex cardiology settings. [ABSTRACT FROM AUTHOR]

The parasite Trypanosoma brucei is the causative agent of African sleeping sickness and is known for its unique RNA processing mechanisms that are common to all the kinetoplastidea including Leishmania and Trypanosoma cruzi. Trypanosomes possess two canonical RNA poly (A) polymerases (PAPs) termed PAP1 and PAP2. PAP1 is encoded by one of the only two genes harboring cis -spliced introns in this organism, and its function is currently unknown. In trypanosomes, all mRNAs, and non-coding RNAs such as small nucleolar RNAs (snoRNAs) and long non-coding RNAs (lncRNAs), undergo trans- splicing and polyadenylation. Here, we show that the function of PAP1, which is located in the nucleus, is to polyadenylate non-coding RNAs, which undergo trans- splicing and polyadenylation. Major substrates of PAP1 are the snoRNAs and lncRNAs. Under the silencing of either PAP1 or PAP2, the level of snoRNAs is reduced. The dual polyadenylation of snoRNA intermediates is carried out by both PAP2 and PAP1 and requires the factors essential for the polyadenylation of mRNAs. The dual polyadenylation of the precursor snoRNAs by PAPs may function to recruit the machinery essential for snoRNA processing. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1038/s41598-022-27272-x. Correction to: I Scientific Reports i https://doi.org/10.1038/s41598-022-27272-x, published online 10 January 2023 The original version of this Article contained an error in the spelling of the author Ariel Feiglin which was incorrectly given as Ariel Faiglin. [Extracted from the article]

Bacterial repellence in suture materials is a desirable property that can potentially improve the healing process by preventing infection. We describe a method for generating nanostructures at the surface of commercial sutures of different composition, and their potential for preventing biofilm formation. We show how bacteria attachment is altered in the presence of nanosized topographies and identify optimum designs for preventing it without compromising biocompatibility and applicability in terms of nanostructure robustness or tissue friction. These studies open new possibilities for flexible and cost-effective realization of topography-based antibacterial coatings for absorbable biomedical textiles. [ABSTRACT FROM AUTHOR]

Background: Neonatal jaundice occurs in approximately 60% of term newborns. Although risk factors for neonatal jaundice have been studied, all the suggested strategies are based on various newborn tests for bilirubin levels. We aim to stratify neonates into risk groups for clinically significant neonatal jaundice using a combined data analysis approach, without serum bilirubin evaluation.Study Design: Term (gestational week 37-42) neonates born in a single medical center, 2005-2018 were identified. Anonymized data were analyzed using machine learning. Thresholds for stratification into risk groups were established. Associations were evaluated statistically using neonates with and without clinically significant neonatal jaundice from the study population.Results: A total of 147,667 consecutive term live neonates were included. The machine learning diagnostic ability to evaluate the risk for neonatal jaundice was 0.748; 95% CI 0.743-0.754 (AUC). The most important factors were (in order of importance) maternal blood type, maternal age, gestational age at delivery, estimated birth weight, parity, CBC at admission, and maternal blood pressure at admission. Neonates were then stratified by risk: 61% (n = 90,140) were classed as low-risk, 39% (n = 57,527) as higher-risk. Prevalence of jaundice was 4.14% in the full cohort, and 1.47% and 8.29% in the low- and high-risk cohorts, respectively; OR 6.06 (CI: 5.7-6.45) for neonatal jaundice in high-risk group.Conclusion: A population tailored "first step" screening policy using machine learning model presents potential of neonatal jaundice risk stratification for term neonates. Future development and validation of this computational model are warranted. [ABSTRACT FROM AUTHOR]

Highlights: [•] The Trypanosoma brucei telomerase RNA is a C/D snoRNA that is localized near the nucleolus. [•] TER silencing causes shortening of the chromosomes but no growth arrest. [•] pre-TER is trans-spliced linking trans-splicing to the processing of non-coding RNAs. [Copyright &y& Elsevier]

Single-nucleotide mutations (SNPs) in protein-coding regions of the human genome are a major factor in determining human variation in health and disease. Here, we analyze the amino acid changes and functional effects due to non-synonymous SNPs. Three databases were used: (i) Variation - mutations found in the general human population; (ii) Cosmic - mutations found in cancer cells; and (iii) Pathogenic - a curated subset of mutations in Variation that are associated with diseases. The distributions of amino acid changes in these datasets were analyzed. It is shown that mutations in the Pathogenic dataset, in particular, tend to introduce order-promoting residues. The effects of the mutations in these datasets were also studied using the program Polyphen-2, which predicts the functional impact of non-synonymous mutations. In order to evaluate the significance of these predicted effects, we compared them to those due to the same amino acid replacements introduced at other positions in the same proteins as a control. A mutation can be deleterious because the amino acid change is drastic (for example a change from hydrophobic residue to hydrophilic residue) or because of its location in the protein. We found that, on both counts, mutations in the Variation dataset tend to be less deleterious than randomly expected whereas mutations in the Pathogenic dataset tend to be more deleterious than their control mutations. The mutations in the Cosmic dataset are found to be more deleterious than those in its control set but less than those in Pathogenic. [ABSTRACT FROM AUTHOR]

Motivation: Biological processes are dynamic, whereas the networks that depict them are typically static. Quantitative modeling using differential equations or logic-based functions can offer quantitative predictions of the behavior of biological systems, but they require detailed experimental characterization of interaction kinetics, which is typically unavailable. To determine to what extent complex biological processes can be modeled and analyzed using only the static structure of the network (i.e. the direction and sign of the edges), we attempt to predict the phenotypic effect of perturbations in biological networks from the static network structure.Results: We analyzed three networks from different sources: The EGFR/MAPK and PI3K/AKT network from a detailed experimental study, the TNF regulatory network from the STRING database and a large network of all NCI-curated pathways from the Protein Interaction Database. Altogether, we predicted the effect of 39 perturbations (e.g. by one or two drugs) on 433 target proteins/genes. In up to 82% of the cases, an algorithm that used only the static structure of the network correctly predicted whether any given protein/gene is upregulated or downregulated as a result of perturbations of other proteins/genes.Conclusion: While quantitative modeling requires detailed experimental data and heavy computations, which limit its scalability for large networks, a wiring-based approach can use available data from pathway and interaction databases and may be scalable. These results lay the foundations for a large-scale approach of predicting phenotypes based on the schematic structure of networks.Contact: yanay@ofranlab.orgSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The yeast protein-protein interaction network has been shown to have distinct topological features such as a scale free degree distribution and a high level of clustering. Here we analyze an additional feature which is called Neighbor Overlap. This feature reflects the number of shared neighbors between a pair of proteins. We show that Neighbor Overlap is enriched in the yeast protein-protein interaction network compared with control networks carefully designed to match the characteristics of the yeast network in terms of degree distribution and clustering coefficient. Our analysis also reveals that pairs of proteins with high Neighbor Overlap have higher sequence similarity, more similar GO annotations and stronger genetic interactions than pairs with low ones. Finally, we demonstrate that pairs of proteins with redundant functions tend to have high Neighbor Overlap. We suggest that a combination of three mechanisms is the basis for this feature: The abundance of protein complexes, selection for backup of function, and the need to allow functional variation. [ABSTRACT FROM AUTHOR]

Background: Recent studies have provided extensive evidence for multitudes of non-coding RNA (ncRNA) transcripts in a wide range of eukaryotic genomes. ncRNAs are emerging as key players in multiple layers of cellular regulation. With the availability of many whole genome sequences, comparative analysis has become a powerful tool to identify ncRNA molecules. In this study, we performed a systematic genome-wide in silico screen to search for novel small ncRNAs in the genome of Trypanosoma brucei using techniques of comparative genomics. Results: In this study, we identified by comparative genomics, and validated by experimental analysis several novel ncRNAs that are conserved across multiple trypanosomatid genomes. When tested on known ncRNAs, our procedure was capable of finding almost half of the known repertoire through homology over six genomes, and about two-thirds of the known sequences were found in at least four genomes. After filtering, 72 conserved unannotated sequences in at least four genomes were found, 29 of which, ranging in size from 30 to 392 nts, were conserved in all six genomes. Fifty of the 72 candidates in the final set were chosen for experimental validation. Eighteen of the 50 (36%) were shown to be expressed, and for 11 of them a distinct expression product was detected, suggesting that they are short ncRNAs. Using functional experimental assays, five of the candidates were shown to be novel H/ACA and C/D snoRNAs; these included three sequences that appear as singletons in the genome, unlike previously identified snoRNA molecules that are found in clusters. The other candidates appear to be novel ncRNA molecules, and their function is, as yet, unknown. Conclusions: Using comparative genomic techniques, we predicted 72 sequences as ncRNA candidates in T. brucei. The expression of 50 candidates was tested in laboratory experiments. This resulted in the discovery of 11 novel short ncRNAs in procyclic stage T. brucei, which have homologues in the other trypansomatids. A few of these molecules are snoRNAs, but most of them are novel ncRNA molecules. Based on this study, our analysis suggests that the total number of ncRNAs in trypanosomatids is in the range of several hundred. [ABSTRACT FROM AUTHOR]

Motivation: Intriguingly, sequence analysis of genomes reveals that a large number of genes are unique to each organism. The origin of these genes, termed ORFans, is not known. Here, we explore the origin of ORFan genes by defining a simple measure called ‘composition bias’, based on the deviation of the amino acid composition of a given sequence from the average composition of all proteins of a given genome. [ABSTRACT FROM PUBLISHER]

Background: Recent studies have provided extensive evidence for multitudes of non-coding RNA (ncRNA) transcripts in a wide range of eukaryotic genomes. ncRNAs are emerging as key players in multiple layers of cellular regulation. With the availability of many whole genome sequences, comparative analysis has become a powerful tool to identify ncRNA molecules. In this study, we performed a systematic genome-wide in silico screen to search for novel small ncRNAs in the genome of Trypanosoma brucei using techniques of comparative genomics. Results: In this study, we identified by comparative genomics, and validated by experimental analysis several novel ncRNAs that are conserved across multiple trypanosomatid genomes. When tested on known ncRNAs, our procedure was capable of finding almost half of the known repertoire through homology over six genomes, and about two-thirds of the known sequences were found in at least four genomes. After filtering, 72 conserved unannotated sequences in at least four genomes were found, 29 of which, ranging in size from 30 to 392 nts, were conserved in all six genomes. Fifty of the 72 candidates in the final set were chosen for experimental validation. Eighteen of the 50 (36%) were shown to be expressed, and for 11 of them a distinct expression product was detected, suggesting that they are short ncRNAs. Using functional experimental assays, five of the candidates were shown to be novel H/ACA and C/D snoRNAs; these included three sequences that appear as singletons in the genome, unlike previously identified snoRNA molecules that are found in clusters. The other candidates appear to be novel ncRNA molecules, and their function is, as yet, unknown. Conclusions: Using comparative genomic techniques, we predicted 72 sequences as ncRNA candidates in T. brucei. The expression of 50 candidates was tested in laboratory experiments. This resulted in the discovery of 11 novel short ncRNAs in procyclic stage T. brucei, which have homologues in the other trypansomatids. A few of these molecules are snoRNAs, but most of them are novel ncRNA molecules. Based on this study, our analysis suggests that the total number of ncRNAs in trypanosomatids is in the range of several hundred. [ABSTRACT FROM AUTHOR]

Summary: e23D, a database of A-to-I RNA editing sites from human, mouse and fly mapped to evolutionary related protein 3D structures, is presented. Genomic coordinates of A-to-I RNA editing sites are converted to protein coordinates and mapped onto 3D structures from PDB or theoretical models from ModBase. e23D allows visualization of the protein structure, modeling of recoding events and orientation of the editing with respect to nearby genomic functional sites from databases of disease causing mutations and genomic polymorphism. [ABSTRACT FROM AUTHOR]

Background: Scanning large genomes with a sliding window in search of locally stable RNA structures is a well motivated problem in bioinformatics. Given a predefined window size L and an RNA sequence S of size N (L < N), the consecutive windows folding problem is to compute the minimal free energy (MFE) for the folding of each of the L-sized substrings of S. The consecutive windows folding problem can be naively solved in O(NL³) by applying any of the classical cubic-time RNA folding algorithms to each of the N-L windows of size L. Recently an O(NL²) solution for this problem has been described. Results: Here, we describe and implement an O(NLψ(L)) engine for the consecutive windows folding problem, where ψ(L) is shown to converge to O(1) under the assumption of a standard probabilistic polymer folding model, yielding an O(L) speedup which is experimentally confirmed. Using this tool, we note an intriguing directionality (5'-3' vs. 3'-5') folding bias, i.e. that the minimal free energy (MFE) of folding is higher in the native direction of the DNA than in the reverse direction of various genomic regions in several organisms including regions of the genomes that do not encode proteins or ncRNA. This bias largely emerges from the genomic dinucleotide bias which affects the MFE, however we see some variations in the folding bias in the different genomic regions when normalized to the dinucleotide bias. We also present results from calculating the MFE landscape of a mouse chromosome 1, characterizing the MFE of the long ncRNA molecules that reside in this chromosome. Conclusion: The efficient consecutive windows folding engine described in this paper allows for genome wide scans for ncRNA molecules as well as large-scale statistics. This is implemented here as a software tool, called RNAslider, and applied to the scanning of long chromosomes, leading to the observation of features that are visible only on a large scale. [ABSTRACT FROM AUTHOR]

Abstract: Lineage trees describe the microevolution of cells within an organism. They have been useful in the study of B cell affinity maturation, which is based on somatic hypermutation of immunoglobulin genes in germinal centers and selection of the resulting mutants. Our aim was to create and implement an algorithm that can generate lineage trees from immunoglobulin variable region gene sequences. The IgTree© program implements the algorithm we developed, and generates lineage trees. Original sequences found in experiments are assigned to either leaves or internal nodes of the tree. Each tree node represents a single mutation separating the sequences. The mutations that separate the sequences from each other can be point mutations, deletions or insertions. The program can deal with gaps and find potential reversion mutations. The program also enumerates mutation frequencies and sequence motifs around each mutation, on a per-tree basis. The algorithm has proven useful in several studies of immunoglobulin variable region gene mutations. [Copyright &y& Elsevier]

RNA interference of Sm proteins in Trypanosoma brucei demonstrated that the stability of the small nuclear RNAs (U1, U2, U4, U5) and the spliced leader RNA, but not U6 RNA, were affected upon Sm depletion (Mandelboim, M., Barth, S., Biten, M., Liang, X. H., and Michaeli, S. (2003) J. BioL Chem. 278, 51469–51478), suggesting that Lsm proteins that bind and stabilize U6 RNA in other eukaryotes should exist in trypanosomes. In this study, we identified seven Lsm proteins (Lsm2p to Lsm8p) and examined the function of Lsm3p and LsmSp by RNA interference silencing. Both Lsm proteins were found to be essential for U6 stability and mRNA decay. Silencing was lethal, and c/s-and trans-splicing were inhibited. Importantly, silencing also affected the level of U4.U6 and the U4.U6/U5 tri-small nuclear ribonucleoprotein complexes. The presence of Lsm proteins in trypanosomes that diverged early in the eukaryotic lineage suggests that these proteins are highly conserved in both structure and function among eukaryote. Interestingly, however, Lsm1p that is specific to the mRNA decay complex was not identified in the genome database of any Idnetoplastidae, and the Lsm8p that in other eukaryotes exclusively functions in U6 stability was found to function in trypanosomes also in mRNA decay. These data therefore suggest that in trypanosomes only a single Lsm complex may exist. [ABSTRACT FROM AUTHOR]

We present a system for predicting protein-protein modifications, and demonstrate its usefulness in the field of signal transduction research. Signal transduction is one of the most important areas of investigation in biological research. One of the major mechanisms frequently employed by cells to regulate signal transduction processes involves protein phosphorylation by various kinases. As many as 1000 protein kinases and 500 protein phosphatases in the human genome are thought to be involved in phosphorylation processes which regulate all aspects of cell function. The complexity of such interactions stems from the enormous number of factors and interactions, which makes the identification of putative substrates for any given enzyme by straightforward experimentation increasingly difficult. We present here a data mining algorithm, based on the similarity between the modifier proteins and between the modified proteins, and on experimental constraints. The application presented here (PESI) focuses on substrate phosphorylation by various enzymes. This algorithm reduces the number of substrate candidates for experimental study by about two orders of magnitude. Moreover, this algorithm has already yielded predictions for previously unknown substrates of the enzymes PKC δ and PKC η, which we have confirmed experimentally. [ABSTRACT FROM AUTHOR]