Seafloor Incubation Experiment with Deep-Sea Hydrothermal Vent Fluid Reveals Effect of Pressure and Lag Time on Autotrophic Microbial Communities.

Depressurization and sample processing delays may impact the outcome of shipboard microbial incubations of samples collected from the deep sea. To address this knowledge gap, we developed a remotely operated vehicle (ROV)-powered incubator instrument to carry out and compare results from in situ and shipboard RNA stable isotope probing (RNA-SIP) experiments to identify the key chemolithoautotrophic microbes and metabolisms in diffuse, low-temperature venting fluids from Axial Seamount. All the incubations showed microbial uptake of labeled bicarbonate primarily by thermophilic autotrophic Epsilonbacteraeota that oxidized hydrogen coupled with nitrate reduction. However, the in situ seafloor incubations showed higher abundances of transcripts annotated for aerobic processes, suggesting that oxygen was lost from the hydrothermal fluid samples prior to shipboard analysis. Furthermore, transcripts for thermal stress proteins such as heat shock chaperones and proteases were significantly more abundant in the shipboard incubations, suggesting that depressurization induced thermal stress in the metabolically active microbes in these incubations. Together, the results indicate that while the autotrophic microbial communities in the shipboard and seafloor experiments behaved similarly, there were distinct differences that provide new insight into the activities of natural microbial assemblages under nearly native conditions in the ocean.IMPORTANCE Diverse microbial communities drive biogeochemical cycles in Earth's ocean, yet studying these organisms and processes is often limited by technological capabilities, especially in the deep ocean. In this study, we used a novel marine microbial incubator instrument capable of in situ experimentation to investigate microbial primary producers at deep-sea hydrothermal vents. We carried out identical stable isotope probing experiments coupled to RNA sequencing both on the seafloor and on the ship to examine thermophilic, microbial autotrophs in venting fluids from an active submarine volcano. Our results indicate that microbial communities were significantly impacted by the effects of depressurization and sample processing delays, with shipboard microbial communities being more stressed than seafloor incubations. Differences in metabolism were also apparent and are likely linked to the chemistry of the fluid at the beginning of the experiment. Microbial experimentation in the natural habitat provides new insights into understanding microbial activities in the ocean.

Growth Kinetics, Carbon Isotope Fractionation, and Gene Expression in the Hyperthermophile Methanocaldococcus jannaschii during Hydrogen-Limited Growth and Interspecies Hydrogen Transfer.

Hyperthermophilic methanogens are often H2 limited in hot subseafloor environments, and their survival may be due in part to physiological adaptations to low H2 conditions and interspecies H2 transfer. The hyperthermophilic methanogen Methanocaldococcus jannaschii was grown in monoculture at high (80 to 83 µM) and low (15 to 27 µM) aqueous H2 concentrations and in coculture with the hyperthermophilic H2 producer Thermococcus paralvinellae The purpose was to measure changes in growth and CH4 production kinetics, CH4 fractionation, and gene expression in M. jannaschii with changes in H2 flux. Growth and cell-specific CH4 production rates of M. jannaschii decreased with decreasing H2 availability and decreased further in coculture. However, cell yield (cells produced per mole of CH4 produced) increased 6-fold when M. jannaschii was grown in coculture rather than monoculture. Relative to high H2 concentrations, isotopic fractionation of CO2 to CH4 (εCO2-CH4) was 16‰ larger for cultures grown at low H2 concentrations and 45‰ and 56‰ larger for M. jannaschii growth in coculture on maltose and formate, respectively. Gene expression analyses showed H2-dependent methylene-tetrahydromethanopterin (H4MPT) dehydrogenase expression decreased and coenzyme F420-dependent methylene-H4MPT dehydrogenase expression increased with decreasing H2 availability and in coculture growth. In coculture, gene expression decreased for membrane-bound ATP synthase and hydrogenase. The results suggest that H2 availability significantly affects the CH4 and biomass production and CH4 fractionation by hyperthermophilic methanogens in their native habitats.IMPORTANCE Hyperthermophilic methanogens and H2-producing heterotrophs are collocated in high-temperature subseafloor environments, such as petroleum reservoirs, mid-ocean ridge flanks, and hydrothermal vents. Abiotic flux of H2 can be very low in these environments, and there is a gap in our knowledge about the origin of CH4 in these habitats. In the hyperthermophile Methanocaldococcus jannaschii, growth yields increased as H2 flux, growth rates, and CH4 production rates decreased. The same trend was observed increasingly with interspecies H2 transfer between M. jannaschii and the hyperthermophilic H2 producer Thermococcus paralvinellae With decreasing H2 availability, isotopic fractionation of carbon during methanogenesis increased, resulting in isotopically more negative CH4 with a concomitant decrease in H2-dependent methylene-tetrahydromethanopterin dehydrogenase gene expression and increase in F420-dependent methylene-tetrahydromethanopterin dehydrogenase gene expression. The significance of our research is in understanding the nature of hyperthermophilic interspecies H2 transfer and identifying biogeochemical and molecular markers for assessing the physiological state of methanogens and possible source of CH4 in natural environments.

Formate hydrogenlyase and formate secretion ameliorate H2 inhibition in the hyperthermophilic archaeon Thermococcus paralvinellae.

Some hyperthermophilic heterotrophs in the genus Thermococcus produce H2 in the absence of S° and have up to seven hydrogenases, but their combined physiological roles are unclear. Here, we show which hydrogenases in Thermococcus paralvinellae are affected by added H2 during growth without S°. Growth rates and steady-state cell concentrations decreased while formate production rates increased when T. paralvinallae was grown in a chemostat with 65 µM of added H2(aq) . Differential gene expression analysis using RNA-Seq showed consistent expression of six hydrogenase operons with and without added H2 . In contrast, expression of the formate hydrogenlyase 1 (fhl1) operon increased with added H2 . Flux balance analysis showed H2 oxidation and formate production using FHL became an alternate route for electron disposal during H2 inhibition with a concomitant increase in growth rate relative to cells without FHL. T. paralvinellae also grew on formate with an increase in H2 production rate relative to growth on maltose or tryptone. Growth on formate increased fhl1 expression but decreased expression of all other hydrogenases. Therefore, Thermococcus that possess fhl1 have a competitive advantage over other Thermococcus species in hot subsurface environments where organic substrates are present, S° is absent and slow H2 efflux causes growth inhibition.

Validation of a rapid neurodevelopmental assessment tool for 10- to 16-year-old young adolescents in Bangladesh.

OBJECTIVE: To validate a Rapid Neurodevelopmental Assessment (RNDA) tool for use by child health professionals to determine neurodevelopmental impairments (NDIs) in young adolescents aged 10-16 years in Bangladesh. STUDY DESIGN: In a convenience sample of community children (n = 47), inter-rater reliability was determined between four testers, and concurrent validity was determined by simultaneous administration of an intelligence quotient (IQ) test (Wechsler Intelligence Scale for Children, Revised) by a child psychologist. RESULTS: Inter-rater reliability was excellent between the testers on the 47 children administered the RNDA (kappa = 1.00). Significantly lower IQ scores were obtained in those identified with 'any (>1) NDI' (n = 34) compared with those with no NDI (n = 13) on Verbal IQ (P-value < 0.0001), Performance IQ (P-value < 0.0001) and Full-scale IQ (P-value < 0.0001) scores on the Wechsler Intelligence Scale for Children, Revised. CONCLUSION: The RNDA shows promise as a tool for use by child health professionals for identifying NDIs in young adolescents aged 10-16 years. A larger study sample is needed to determine its usefulness for identification of some impairments not found in the study population, i.e. gross motor, fine motor, hearing and seizures.

Correlation of hyperinsulinemia with hyperandrogenemia in primary infertile women with polycystic ovary syndrome.

Polycystic ovary syndrome (PCOS) is the most common endocrinopathy among women of the reproductive age and is the most common form of anovulatory infertility. Hyperinsulinemia and hyperandrogenemia are the characteristic features of PCOS, but the association between hyperinsulinemia and hyperandrogenemia is not well established. To find out any causal association between Hyperinsulinemia and hyperandrogenemia, a retrospective study was done on primary infertile women suffering from PCOS in the department of Biochemistry, Bangabandhu Sheikh Mujib Medical University. A total of 80 subjects were selected, among them 60 were cases and 20 were controls. Depending on their body mass index, the cases were divide into two groups, obese (n=30) and non-obese (n=30). Age and BMI matched controls were taken for both age groups. Observations derived from the study suggested that hyperinsulinemia and hyperandrogenemia are characteristic features of PCOS but significant correlation was not found between hyperinsulinemia and hyperandrogenemia. However, we cannot negate any possible association between the two and thereby we recommend further study to be done with a larger sample size.

Geographical diversity in seasonality of major diarrhoeal pathogens in Bangladesh observed between 2010 and 2012.

The study aimed to determine the geographical diversity in seasonality of major diarrhoeal pathogens among 21 138 patients enrolled between 2010 and 2012 in two urban and two rural sites in Bangladesh under the surveillance system of the International Centre for Diarrhoeal Disease Research, Bangladesh (icddr,b). Distinct patterns in seasonality were found for rotavirus diarrhoea which peaked in winter across the sites (December and January) and dipped during the rainy season (May) in urban Dhaka, August in Mirpur and July in Matlab, equated by time-series analysis using quasi-Poisson regression model. Significant seasonality for shigellosis was observed in Dhaka and rural Mirzapur. Cholera had robust seasonality in Dhaka and Matlab in the hot and rainy seasons. For enterotoxogenic Escherichia coli (ETEC) diarrhoea, clearly defined seasonality was observed in Dhaka (summer). Understanding the seasonality of such pathogens can improve case management with appropriate therapy, allowing policy-makers to identify periods of high disease burden.

Validation of a home-based neurodevelopmental screening tool for under 2-year-old children in Bangladesh.

BACKGROUND: Home-based screening to identify young children at risk for neurodevelopmental impairments (NDIs) is needed to guide the targeting of child neurodevelopmental intervention services in Bangladesh. This study aimed to validate such a tool for children under age 2 years. METHODS: A Developmental Screening Questionnaire was administered to mothers of children aged 0-<2 years in an urban community. Inter-rater reliability among the interviewers, who were high school graduates, was determined. All children who were screen positive and a proportion of screen negatives were subsequently assessed for NDIs by professionals. Sensitivity and specificity were calculated by comparing screening with assessment results. RESULTS: Mean kappa coefficient of agreement among interviewers was 0.95. A total of 197 children were screened, of whom 17% screened positive. Fifty-one children, including 24 screen negatives, were assessed for NDIs. Screen-positivity was significantly different between income groups (P = 0.019), and higher in stunted children (odds ratio = 5.76, 95% confidence interval = 1.72-19.28), indicating good discriminant validity Specificity was excellent (84-100%) for all developmental domains. Sensitivity was 100% for vision and hearing; 70% for speech; and 63%, 53%, 48%, and 45% for gross motor, behaviour, fine motor and cognitive impairments, respectively. CONCLUSION: A tool for screening <2-year-old children at risk for NDIs showed high specificity; and was able to identify all children at risk for vision and hearing impairments, nearly three-fourths with speech impairments, two-thirds with gross motor impairments, and about half with behavioural, cognitive and fine motor impairments. The Developmental Screening Questionnaire tool has potential for use by frontline workers to screen large populations and to link to definitive assessment as well as intervention services.

Machine learning classification by fitting amplicon sequences to existing OTUs.

The ability to use 16S rRNA gene sequence data to train machine learning classification models offers the opportunity to diagnose patients based on the composition of their microbiome. In some applications, the taxonomic resolution that provides the best models may require the use of de novo operational taxonomic units (OTUs) whose composition changes when new data are added. We previously developed a new reference-based approach, OptiFit, that fits new sequence data to existing de novo OTUs without changing the composition of the original OTUs. While OptiFit produces OTUs that are as high quality as de novo OTUs, it is unclear whether this method for fitting new sequence data into existing OTUs will impact the performance of classification models relative to models trained and tested only using de novo OTUs. We used OptiFit to cluster sequences into existing OTUs and evaluated model performance in classifying a dataset containing samples from patients with and without colonic screen relevant neoplasia (SRN). We compared the performance of this model to standard methods including de novo and database-reference-based clustering. We found that using OptiFit performed as well or better in classifying SRNs. OptiFit can streamline the process of classifying new samples by avoiding the need to retrain models using reclustered sequences. IMPORTANCE There is great potential for using microbiome data to aid in diagnosis. A challenge with de novo operational taxonomic unit (OTU)-based classification models is that 16S rRNA gene sequences are often assigned to OTUs based on similarity to other sequences in the dataset. If data are generated from new patients, the old and new sequences must be reclustered to OTUs and the classification model retrained. Yet there is a desire to have a single, validated model that can be widely deployed. To overcome this obstacle, we applied the OptiFit clustering algorithm to fit new sequence data to existing OTUs allowing for reuse of the model. A random forest model implemented using OptiFit performed as well as the traditional reassign and retrain approach. This result shows that it is possible to train and apply machine learning models based on OTU relative abundance data that do not require retraining or the use of a reference database.

Very early life microbiome and metabolome correlates with primary vaccination variability in children.

IMPORTANCE: We show that simultaneous study of stool and nasopharyngeal microbiome reveals divergent timing and patterns of maturation, suggesting that local mucosal factors may influence microbiome composition in the gut and respiratory system. Antibiotic exposure in early life as occurs commonly, may have an adverse effect on vaccine responsiveness. Abundance of gut and/or nasopharyngeal bacteria with the machinery to produce lipopolysaccharide-a toll-like receptor 4 agonist-may positively affect future vaccine protection, potentially by acting as a natural adjuvant. The increased levels of serum phenylpyruvic acid in infants with lower vaccine-induced antibody levels suggest an increased abundance of hydrogen peroxide, leading to more oxidative stress in low vaccine-responding infants.

A Goldilocks Principle for the Gut Microbiome: Taxonomic Resolution Matters for Microbiome-Based Classification of Colorectal Cancer.

Colorectal cancer is a common and deadly disease in the United States accounting for over 50,000 deaths in 2020. This progressive disease is highly preventable with early detection and treatment, but many people do not comply with the recommended screening guidelines. The gut microbiome has emerged as a promising target for noninvasive detection of colorectal cancer. Most microbiome-based classification efforts utilize taxonomic abundance data from operational taxonomic units (OTUs) or amplicon sequence variants (ASVs) with the goal of increasing taxonomic resolution. However, it is unknown which taxonomic resolution is optimal for microbiome-based classification of colorectal cancer. To address this question, we used a reproducible machine learning framework to quantify classification performance of models based on data annotated to phylum, class, order, family, genus, OTU, and ASV levels. We found that model performance increased with increasing taxonomic resolution, up to the family level where performance was equal (P > 0.05) among family (mean area under the receiver operating characteristic curve [AUROC], 0.689), genus (mean AUROC, 0.690), and OTU (mean AUROC, 0.693) levels before decreasing at the ASV level (P < 0.05; mean AUROC, 0.676). These results demonstrate a trade-off between taxonomic resolution and prediction performance, where coarse taxonomic resolution (e.g., phylum) is not distinct enough, but fine resolution (e.g., ASV) is too individualized to accurately classify samples. Similar to the story of Goldilocks and the three bears (L. B. Cauley, Goldilocks and the Three Bears, 1981), mid-range resolution (i.e., family, genus, and OTU) is "just right" for optimal prediction of colorectal cancer from microbiome data. IMPORTANCE Despite being highly preventable, colorectal cancer remains a leading cause of cancer-related death in the United States. Low-cost, noninvasive detection methods could greatly improve our ability to identify and treat early stages of disease. The microbiome has shown promise as a resource for detection of colorectal cancer. Research on the gut microbiome tends to focus on improving our ability to profile species and strain level taxonomic resolution. However, we found that finer resolution impedes the ability to predict colorectal cancer based on the gut microbiome. These results highlight the need for consideration of the appropriate taxonomic resolution for microbiome analyses and that finer resolution is not always more informative.

mikropml: User-Friendly R Package for Supervised Machine Learning Pipelines.

Machine learning (ML) for classification and prediction based on a set of features is used to make decisions in healthcare, economics, criminal justice and more. However, implementing an ML pipeline including preprocessing, model selection, and evaluation can be time-consuming, confusing, and difficult. Here, we present mikropml (prononced "meek-ROPE em el"), an easy-to-use R package that implements ML pipelines using regression, support vector machines, decision trees, random forest, or gradient-boosted trees. The package is available on GitHub, CRAN, and conda.

Vaccine Hyporesponse Induced by Individual Antibiotic Treatment in Mice and Non-Human Primates Is Diminished upon Recovery of the Gut Microbiome.

Emerging evidence demonstrates a connection between microbiome composition and suboptimal response to vaccines (vaccine hyporesponse). Harnessing the interaction between microbes and the immune system could provide novel therapeutic strategies for improving vaccine response. Currently we do not fully understand the mechanisms and dynamics by which the microbiome influences vaccine response. Using both mouse and non-human primate models, we report that short-term oral treatment with a single antibiotic (vancomycin) results in the disruption of the gut microbiome and this correlates with a decrease in systemic levels of antigen-specific IgG upon subsequent parenteral vaccination. We further show that recovery of microbial diversity before vaccination prevents antibiotic-induced vaccine hyporesponse, and that the antigen specific IgG response correlates with the recovery of microbiome diversity. RNA sequencing analysis of small intestine, spleen, whole blood, and secondary lymphoid organs from antibiotic treated mice revealed a dramatic impact on the immune system, and a muted inflammatory signature is correlated with loss of bacteria from Lachnospiraceae, Ruminococcaceae, and Clostridiaceae. These results suggest that microbially modulated immune pathways may be leveraged to promote vaccine response and will inform future vaccine design and development strategies.

A Framework for Effective Application of Machine Learning to Microbiome-Based Classification Problems.

Machine learning (ML) modeling of the human microbiome has the potential to identify microbial biomarkers and aid in the diagnosis of many diseases such as inflammatory bowel disease, diabetes, and colorectal cancer. Progress has been made toward developing ML models that predict health outcomes using bacterial abundances, but inconsistent adoption of training and evaluation methods call the validity of these models into question. Furthermore, there appears to be a preference by many researchers to favor increased model complexity over interpretability. To overcome these challenges, we trained seven models that used fecal 16S rRNA sequence data to predict the presence of colonic screen relevant neoplasias (SRNs) (n = 490 patients, 261 controls and 229 cases). We developed a reusable open-source pipeline to train, validate, and interpret ML models. To show the effect of model selection, we assessed the predictive performance, interpretability, and training time of L2-regularized logistic regression, L1- and L2-regularized support vector machines (SVM) with linear and radial basis function kernels, a decision tree, random forest, and gradient boosted trees (XGBoost). The random forest model performed best at detecting SRNs with an area under the receiver operating characteristic curve (AUROC) of 0.695 (interquartile range [IQR], 0.651 to 0.739) but was slow to train (83.2 h) and not inherently interpretable. Despite its simplicity, L2-regularized logistic regression followed random forest in predictive performance with an AUROC of 0.680 (IQR, 0.625 to 0.735), trained faster (12 min), and was inherently interpretable. Our analysis highlights the importance of choosing an ML approach based on the goal of the study, as the choice will inform expectations of performance and interpretability.IMPORTANCE Diagnosing diseases using machine learning (ML) is rapidly being adopted in microbiome studies. However, the estimated performance associated with these models is likely overoptimistic. Moreover, there is a trend toward using black box models without a discussion of the difficulty of interpreting such models when trying to identify microbial biomarkers of disease. This work represents a step toward developing more-reproducible ML practices in applying ML to microbiome research. We implement a rigorous pipeline and emphasize the importance of selecting ML models that reflect the goal of the study. These concepts are not particular to the study of human health but can also be applied to environmental microbiology studies.

Women Are Underrepresented and Receive Differential Outcomes at ASM Journals: a Six-Year Retrospective Analysis.

Despite 50% of biology Ph.D. graduates being women, the number of women that advance in academia decreases at each level (e.g., from graduate to postdoctorate to tenure track). Recently, scientific societies and publishers have begun examining internal submissions data to evaluate representation and evaluation of women in their peer review processes; however, representation and attitudes differ by scientific field, and to date, no studies have investigated academic publishing in the field of microbiology. Using manuscripts submitted between January 2012 and August 2018 to the 15 journals published by the American Society for Microbiology (ASM), we describe the representation of women at ASM journals and the outcomes of their manuscripts. Senior women authors at ASM journals were underrepresented compared to global and society estimates of microbiology researchers. Additionally, manuscripts submitted by corresponding authors that were women received more negative outcomes than those submitted by men. These negative outcomes were somewhat mediated by whether or not the corresponding author was based in the United States and by the type of institution for United States-based authors. Nonetheless, the pattern for women corresponding authors to receive more negative outcomes on their submitted manuscripts held. We conclude with suggestions to improve the representation of women and decrease structural penalties against women.IMPORTANCE Barriers in science and academia have prevented women from becoming researchers and experts that are viewed as equivalent to their colleagues who are men. We evaluated the participation and success of women researchers at ASM journals to better understand their success in the field of microbiology. We found that women are underrepresented as expert scientists at ASM journals. This is, in part, due to a combination of both low submissions from senior women authors and more negative outcomes on submitted manuscripts for women compared to men.

Fecal Short-Chain Fatty Acids Are Not Predictive of Colonic Tumor Status and Cannot Be Predicted Based on Bacterial Community Structure.

Colonic bacterial populations are thought to have a role in the development of colorectal cancer with some protecting against inflammation and others exacerbating inflammation. Short-chain fatty acids (SCFAs) have been shown to have anti-inflammatory properties and are produced in large quantities by colonic bacteria that produce SCFAs by fermenting fiber. We assessed whether there was an association between fecal SCFA concentrations and the presence of colonic adenomas or carcinomas in a cohort of individuals using 16S rRNA gene and metagenomic shotgun sequence data. We measured the fecal concentrations of acetate, propionate, and butyrate within the cohort and found that there were no significant associations between SCFA concentration and tumor status. When we incorporated these concentrations into random forest classification models trained to differentiate between people with healthy colons and those with adenomas or carcinomas, we found that they did not significantly improve the ability of 16S rRNA gene or metagenomic gene sequence-based models to classify individuals. Finally, we generated random forest regression models trained to predict the concentration of each SCFA based on 16S rRNA gene or metagenomic gene sequence data from the same samples. These models performed poorly and were able to explain at most 14% of the observed variation in the SCFA concentrations. These results support the broader epidemiological data that questions the value of fiber consumption for reducing the risks of colorectal cancer. Although other bacterial metabolites may serve as biomarkers to detect adenomas or carcinomas, fecal SCFA concentrations have limited predictive power.IMPORTANCE Considering that colorectal cancer is the third leading cancer-related cause of death within the United States, it is important to detect colorectal tumors early and to prevent the formation of tumors. Short-chain fatty acids (SCFAs) are often used as a surrogate for measuring gut health and for being anticarcinogenic because of their anti-inflammatory properties. We evaluated the fecal SCFA concentrations of a cohort of individuals with different colonic tumor burdens who were previously analyzed to identify microbiome-based biomarkers of tumors. We were unable to find an association between SCFA concentration and tumor burden or use SCFAs to improve our microbiome-based models of classifying people based on their tumor status. Furthermore, we were unable to find an association between the fecal community structure and SCFA concentrations. Our results indicate that the association between fecal SCFAs, the gut microbiome, and tumor burden is weak.

Stress in mothers of preterm infants in Bangladesh: associations with family, child and maternal factors and children's neuro-development.

BACKGROUND: The aim of this paper was to ascertain stress experienced by mothers of prospectively followed up preterm infants, and associations with family, child and maternal factors and children's neuro-development. METHODS: Within a follow-up study of preterm infants<33 weeks gestational age at a Child Development Center in Dhaka Shishu Hospital, mothers were interviewed with the Self-Report Questionnaire (SRQ) at each visit. Association between SRQ scores and child, family and maternal variables at first and final visit and children's neuro-developmental outcomes was determined. RESULTS: Low income mothers were more compliant (54%) compared with the defaulters (31%) (P=0.0001) among the 159 mothers enrolled. Of the 88 mothers who were followed up until a mean age of 22 months of their child, 29.3% were at high risk for psychiatric morbidity at first visit compared with 23.9% on their last visit. Use of abortifacients (P=0.026) and higher maternal age (P=0.040) were significantly associated with maternal stress at first visit; while at last follow-up, total number of visits had the most significant association (P=0.041). Twenty-five per cent and 19% of mothers were at risk for psychiatric morbidity in children developing normally and those with neuro-developmental impairments respectively. CONCLUSIONS: Mothers at risk for psychiatric morbidity can be helped through follow-up support within public hospitals close to their homes, which is most availed by low income families. Neuro-developmental monitoring of high-risk infants closer to homes may be more feasible in resource poor countries than reliance on hospital visits, which increase stress. Biological markers of stress and coping strategies need further research.

Prediction of outcome in newly diagnosed myeloma: a meta-analysis of the molecular profiles of 1905 trial patients.

Robust establishment of survival in multiple myeloma (MM) and its relationship to recurrent genetic aberrations is required as outcomes are variable despite apparent similar staging. We assayed copy number alterations (CNA) and translocations in 1036 patients from the NCRI Myeloma XI trial and linked these to overall survival (OS) and progression-free survival. Through a meta-anlysis of these data with data from MRC Myeloma IX trial, totalling 1905 newly diagnosed MM patients (NDMM), we confirm the association of t(4;14), t(14;16), t(14;20), del(17p) and gain(1q21) with poor prognosis with hazard ratios (HRs) for OS of 1.60 (P=4.77 × 10-7), 1.74 (P=0.0005), 1.90 (P=0.0089), 2.10 (P=8.86 × 10-14) and 1.68 (P=2.18 × 10-14), respectively. Patients with 'double-hit' defined by co-occurrence of at least two adverse lesions have an especially poor prognosis with HRs for OS of 2.67 (P=8.13 × 10-27) for all patients and 3.19 (P=1.23 × 10-18) for intensively treated patients. Using comprehensive CNA and translocation profiling in Myeloma XI we also demonstrate a strong association between t(4;14) and BIRC2/BIRC3 deletion (P=8.7 × 10-15), including homozygous deletion. Finally, we define distinct sub-groups of hyperdiploid MM, with either gain(1q21) and CCND2 overexpression (P<0.0001) or gain(11q25) and CCND1 overexpression (P<0.0001). Profiling multiple genetic lesions can identify MM patients likely to relapse early allowing stratification of treatment.

A new approach for the identification and cloning of genes: the pBACwich system using Cre/lox site-specific recombination.

With current plant transformation methods ( Agrobacterium, biolistics and protoplast fusion), insertion of DNA into the genome occurs randomly and in many instances at multiple sites. Associated position effects, copy number differences and multigene interactions can make gene expression experiments difficult to interpret and plant phenotypes less predictable. An alternative approach to random integration of large DNA fragments into plants is to utilize one of several site-specific recombination (SSR) systems, such as Cre/ lox. Cre has been shown in numerous instances to mediate lox site-specific recombination in animal and plant cells. By incorporating the Cre/ lox SSR system into a bacterial artificial chromosome (BAC) vector, a more precise evaluation of large DNA inserts for genetic complementation should be possible. Site-specific insertion of DNA into predefined sites in the genome may eliminate unwanted 'position effects' caused by the random integration of exogenously introduced DNA. In an effort to make the Cre/ lox system an effective tool for site-directed integration of large DNAs, we constructed and tested a new vector potentially capable of integrating large DNA inserts into plant and fungal genomes. In this study, we present the construction of a new BAC vector, pBACwich, for the system and the use of this vector to demonstrate SSR of large DNA inserts (up to 230 kb) into plant and fungal genomes.