CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods

BACKGROUND

The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.

RESULTS

Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.

CONCLUSIONS

Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.

Intratumoral dendritic cell-CD4+ T helper cell niches enable CD8+ T cell differentiation following PD-1 blockade in hepatocellular carcinoma

Despite no apparent defects in T cell priming and recruitment to tumors, a large subset of T cell rich tumors fail to respond to immune checkpoint blockade (ICB). We leveraged a neoadjuvant anti-PD-1 trial in patients with hepatocellular carcinoma (HCC), as well as additional samples collected from patients treated off-label, to explore correlates of response to ICB within T cell-rich tumors. We show that ICB response correlated with the clonal expansion of intratumoral CXCL13+CH25H+IL-21+PD-1+CD4+ T helper cells ("CXCL13+ TH") and Granzyme K+ PD-1+ effector-like CD8+ T cells, whereas terminally exhausted CD39hiTOXhiPD-1hiCD8+ T cells dominated in nonresponders. CD4+ and CD8+ T cell clones that expanded post-treatment were found in pretreatment biopsies. Notably, PD-1+TCF-1+ (Progenitor-exhausted) CD8+ T cells shared clones mainly with effector-like cells in responders or terminally exhausted cells in nonresponders, suggesting that local CD8+ T cell differentiation occurs upon ICB. We found that these Progenitor CD8+ T cells interact with CXCL13+ TH within cellular triads around dendritic cells enriched in maturation and regulatory molecules, or "mregDC". These results suggest that discrete intratumoral niches that include mregDC and CXCL13+ TH control the differentiation of tumor-specific Progenitor exhasuted CD8+ T cells following ICB.

MecCog: A knowledge representation framework for genetic disease mechanism

MOTIVATION

Experimental findings on genetic disease mechanisms are scattered throughout the literature and represented in many ways, including unstructured text, cartoons, pathway diagrams, and network graphs. Integration and structuring of such mechanistic information greatly enhances its utility.

RESULTS

MecCog is a graphical framework for building integrated representations (mechanism schemas) of mechanisms by which a genetic variant causes a disease phenotype. A MecCog mechanism schema displays the propagation of system perturbations across stages of biological organization, using graphical notations to symbolize perturbed entities and activities, hyperlinked evidence tagging, a mechanism ontology, and depiction of knowledge gaps, ambiguities, and uncertainties. The web platform enables a user to construct, store, publish, browse, query, and comment on schemas. MecCog facilitates the identification of potential biomarkers, therapeutic intervention sites, and critical future experiments.

AVAILABILITY

The MecCog framework is freely available at http://www.meccog.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

The role of exome sequencing in newborn screening for inborn errors of metabolism

Public health newborn screening (NBS) programs provide population-scale ascertainment of rare, treatable conditions that require urgent intervention. Tandem mass spectrometry (MS/MS) is currently used to screen newborns for a panel of rare inborn errors of metabolism (IEMs)1-4. The NBSeq project evaluated whole-exome sequencing (WES) as an innovative methodology for NBS. We obtained archived residual dried blood spots and data for nearly all IEM cases from the 4.5 million infants born in California between mid-2005 and 2013 and from some infants who screened positive by MS/MS, but were unaffected upon follow-up testing. WES had an overall sensitivity of 88% and specificity of 98.4%, compared to 99.0% and 99.8%, respectively for MS/MS, although effectiveness varied among individual IEMs. Thus, WES alone was insufficiently sensitive or specific to be a primary screen for most NBS IEMs. However, as a secondary test for infants with abnormal MS/MS screens, WES could reduce false-positive results, facilitate timely case resolution and in some instances even suggest more appropriate or specific diagnosis than that initially obtained. This study represents the largest, to date, sequencing effort of an entire population of IEM-affected cases, allowing unbiased assessment of current capabilities of WES as a tool for population screening.

Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype-weighted knowledge in the CAGI SickKids5 clinical genomes challenge

Precise identification of causative variants from whole-genome sequencing data, including both coding and noncoding variants, is challenging. The Critical Assessment of Genome Interpretation 5 SickKids clinical genome challenge provided an opportunity to assess our ability to extract such information. Participants in the challenge were required to match each of the 24 whole-genome sequences to the correct phenotypic profile and to identify the disease class of each genome. These are all rare disease cases that have resisted genetic diagnosis in a state-of-the-art pipeline. The patients have a range of eye, neurological, and connective-tissue disorders. We used a gene-centric approach to address this problem, assigning each gene a multiphenotype-matching score. Mutations in the top-scoring genes for each phenotype profile were ranked on a 6-point scale of pathogenicity probability, resulting in an approximately equal number of top-ranked coding and noncoding candidate variants overall. We were able to assign the correct disease class for 12 cases and the correct genome to a clinical profile for five cases. The challenge assessor found genes in three of these five cases as likely appropriate. In the postsubmission phase, after careful screening of the genes in the correct genome, we identified additional potential diagnostic variants, a high proportion of which are noncoding.

Assessment of methods for predicting the effects of PTEN and TPMT protein variants

Thermodynamic stability is a fundamental property shared by all proteins. Changes in stability due to mutation are a widespread molecular mechanism in genetic diseases. Methods for the prediction of mutation-induced stability change have typically been developed and evaluated on incomplete and/or biased data sets. As part of the Critical Assessment of Genome Interpretation, we explored the utility of high-throughput variant stability profiling (VSP) assay data as an alternative for the assessment of computational methods and evaluated state-of-the-art predictors against over 7,000 nonsynonymous variants from two proteins. We found that predictions were modestly correlated with actual experimental values. Predictors fared better when evaluated as classifiers of extreme stability effects. While different methods emerging as top performers depending on the metric, it is nontrivial to draw conclusions on their adoption or improvement. Our analyses revealed that only 16% of all variants in VSP assays could be confidently defined as stability-affecting. Furthermore, it is unclear as to what extent VSP abundance scores were reasonable proxies for the stability-related quantities that participating methods were designed to predict. Overall, our observations underscore the need for clearly defined objectives when developing and using both computational and experimental methods in the context of measuring variant impact.

Assessment of patient clinical descriptions and pathogenic variants from gene panel sequences in the CAGI-5 intellectual disability challenge

The Critical Assessment of Genome Interpretation-5 intellectual disability challenge asked to use computational methods to predict patient clinical phenotypes and the causal variant(s) based on an analysis of their gene panel sequence data. Sequence data for 74 genes associated with intellectual disability (ID) and/or autism spectrum disorders (ASD) from a cohort of 150 patients with a range of neurodevelopmental manifestations (i.e. ID, autism, epilepsy, microcephaly, macrocephaly, hypotonia, ataxia) have been made available for this challenge. For each patient, predictors had to report the causative variants and which of the seven phenotypes were present. Since neurodevelopmental disorders are characterized by strong comorbidity, tested individuals often present more than one pathological condition. Considering the overall clinical manifestation of each patient, the correct phenotype has been predicted by at least one group for 93 individuals (62%). ID and ASD were the best predicted among the seven phenotypic traits. Also, causative or potentially pathogenic variants were predicted correctly by at least one group. However, the prediction of the correct causative variant seems to be insufficient to predict the correct phenotype. In some cases, the correct prediction has been supported by rare or common variants in genes different from the causative one.

Assessment of predicted enzymatic activity of α-N-acetylglucosaminidase variants of unknown significance for CAGI 2016

The NAGLU challenge of the fourth edition of the Critical Assessment of Genome Interpretation experiment (CAGI4) in 2016, invited participants to predict the impact of variants of unknown significance (VUS) on the enzymatic activity of the lysosomal hydrolase α-N-acetylglucosaminidase (NAGLU). Deficiencies in NAGLU activity lead to a rare, monogenic, recessive lysosomal storage disorder, Sanfilippo syndrome type B (MPS type IIIB). This challenge attracted 17 submissions from 10 groups. We observed that top models were able to predict the impact of missense mutations on enzymatic activity with Pearson's correlation coefficients of up to .61. We also observed that top methods were significantly more correlated with each other than they were with observed enzymatic activity values, which we believe speaks to the importance of sequence conservation across the different methods. Improved functional predictions on the VUS will help population-scale analysis of disease epidemiology and rare variant association analysis.

CAGI SickKids challenges: Assessment of phenotype and variant predictions derived from clinical and genomic data of children with undiagnosed diseases

Whole-genome sequencing (WGS) holds great potential as a diagnostic test. However, the majority of patients currently undergoing WGS lack a molecular diagnosis, largely due to the vast number of undiscovered disease genes and our inability to assess the pathogenicity of most genomic variants. The CAGI SickKids challenges attempted to address this knowledge gap by assessing state-of-the-art methods for clinical phenotype prediction from genomes. CAGI4 and CAGI5 participants were provided with WGS data and clinical descriptions of 25 and 24 undiagnosed patients from the SickKids Genome Clinic Project, respectively. Predictors were asked to identify primary and secondary causal variants. In addition, for CAGI5, groups had to match each genome to one of three disorder categories (neurologic, ophthalmologic, and connective), and separately to each patient. The performance of matching genomes to categories was no better than random but two groups performed significantly better than chance in matching genomes to patients. Two of the ten variants proposed by two groups in CAGI4 were deemed to be diagnostic, and several proposed pathogenic variants in CAGI5 are good candidates for phenotype expansion. We discuss implications for improving in silico assessment of genomic variants and identifying new disease genes.

Harnessing formal concepts of biological mechanism to analyze human disease

Mechanism is a widely used concept in biology. In 2017, more than 10% of PubMed abstracts used the term. Therefore, searching for and reasoning about mechanisms is fundamental to much of biomedical research, but until now there has been almost no computational infrastructure for this purpose. Recent work in the philosophy of science has explored the central role that the search for mechanistic accounts of biological phenomena plays in biomedical research, providing a conceptual basis for representing and analyzing biological mechanism. The foundational categories for components of mechanisms—entities and activities—guide the development of general, abstract types of biological mechanism parts. Building on that analysis, we have developed a formal framework for describing and representing biological mechanism, MecCog, and applied it to describing mechanisms underlying human genetic disease. Mechanisms are depicted using a graphical notation. Key features are assignment of mechanism components to stages of biological organization and classes; visual representation of uncertainty, ignorance, and ambiguity; and tight integration with literature sources. The MecCog framework facilitates analysis of many aspects of disease mechanism, including the prioritization of future experiments, probing of gene−drug and gene−environment interactions, identification of possible new drug targets, personalized drug choice, analysis of nonlinear interactions between relevant genetic loci, and classification of diseases based on mechanism.

CAGI4 Crohn's exome challenge: Marker SNP versus exome variant models for assigning risk of Crohn disease

Understanding the basis of complex trait disease is a fundamental problem in human genetics. The CAGI Crohn's Exome challenges are providing insight into the adequacy of current disease models by requiring participants to identify which of a set of individuals has been diagnosed with the disease, given exome data. For the CAGI4 round, we developed a method that used the genotypes from exome sequencing data only to impute the status of genome wide association studies marker SNPs. We then used the imputed genotypes as input to several machine learning methods that had been trained to predict disease status from marker SNP information. We achieved the best performance using Naïve Bayes and with a consensus machine learning method, obtaining an area under the curve of 0.72, larger than other methods used in CAGI4. We also developed a model that incorporated the contribution from rare missense variants in the exome data, but this performed less well. Future progress is expected to come from the use of whole genome data rather than exomes.

Determination of disease phenotypes and pathogenic variants from exome sequence data in the CAGI 4 gene panel challenge

The use of gene panel sequence for diagnostic and prognostic testing is now widespread, but there are so far few objective tests of methods to interpret these data. We describe the design and implementation of a gene panel sequencing data analysis pipeline (VarP) and its assessment in a CAGI4 community experiment. The method was applied to clinical gene panel sequencing data of 106 patients, with the goal of determining which of 14 disease classes each patient has and the corresponding causative variant(s). The disease class was correctly identified for 36 cases, including 10 where the original clinical pipeline did not find causative variants. For a further seven cases, we found strong evidence of an alternative disease to that tested. Many of the potentially causative variants are missense, with no previous association with disease, and these proved the hardest to correctly assign pathogenicity or otherwise. Post analysis showed that three-dimensional structure data could have helped for up to half of these cases. Over-reliance on HGMD annotation led to a number of incorrect disease assignments. We used a largely ad hoc method to assign probabilities of pathogenicity for each variant, and there is much work still to be done in this area.

CAGI4 SickKids clinical genomes challenge: A pipeline for identifying pathogenic variants

Compared with earlier more restricted sequencing technologies, identification of rare disease variants using whole-genome sequence has the possibility of finding all causative variants, but issues of data quality and an overwhelming level of background variants complicate the analysis. The CAGI4 SickKids clinical genome challenge provided an opportunity to assess the landscape of variants found in a difficult set of 25 unsolved rare disease cases. To address the challenge, we developed a three-stage pipeline, first carefully analyzing data quality, then classifying high-quality gene-specific variants into seven categories, and finally examining each candidate variant for compatibility with the often complex phenotypes of these patients for final prioritization. Variants consistent with the phenotypes were found in 24 out of the 25 cases, and in a number of these, there are prioritized variants in multiple genes. Data quality analysis suggests that some of the selected variants are likely incorrect calls, complicating interpretation. The data providers followed up on three suggested variants with Sanger sequencing, and in one case, a prioritized variant was confirmed as likely causative by the referring physician, providing a diagnosis in a previously intractable case.

Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges

Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.

Ensemble variant interpretation methods to predict enzyme activity and assign pathogenicity in the CAGI4 NAGLU (Human N-acetyl-glucosaminidase) and UBE2I (Human SUMO-ligase) challenges

CAGI (Critical Assessment of Genome Interpretation) conducts community experiments to determine the state of the art in relating genotype to phenotype. Here, we report results obtained using newly developed ensemble methods to address two CAGI4 challenges: enzyme activity for population missense variants found in NAGLU (Human N-acetyl-glucosaminidase) and random missense mutations in Human UBE2I (Human SUMO E2 ligase), assayed in a high-throughput competitive yeast complementation procedure. The ensemble methods are effective, ranked second for SUMO-ligase and third for NAGLU, according to the CAGI independent assessors. However, in common with other methods used in CAGI, there are large discrepancies between predicted and experimental activities for a subset of variants. Analysis of the structural context provides some insight into these. Post-challenge analysis shows that the ensemble methods are also effective at assigning pathogenicity for the NAGLU variants. In the clinic, providing an estimate of the reliability of pathogenic assignments is the key. We have also used the NAGLU dataset to show that ensemble methods have considerable potential for this task, and are already reliable enough for use with a subset of mutations.

Lessons from the CAGI-4 Hopkins clinical panel challenge

The CAGI-4 Hopkins clinical panel challenge was an attempt to assess state-of-the-art methods for clinical phenotype prediction from DNA sequence. Participants were provided with exonic sequences of 83 genes for 106 patients from the Johns Hopkins DNA Diagnostic Laboratory. Five groups participated in the challenge, predicting both the probability that each patient had each of the 14 possible classes of disease, as well as one or more causal variants. In cases where the Hopkins laboratory reported a variant, at least one predictor correctly identified the disease class in 36 of the 43 patients (84%). Even in cases where the Hopkins laboratory did not find a variant, at least one predictor correctly identified the class in 39 of the 63 patients (62%). Each prediction group correctly diagnosed at least one patient that was not successfully diagnosed by any other group. We discuss the causal variant predictions by different groups and their implications for further development of methods to assess variants of unknown significance. Our results suggest that clinically relevant variants may be missed when physicians order small panels targeted on a specific phenotype. We also quantify the false-positive rate of DNA-guided analysis in the absence of prior phenotypic indication.

Eimeria species occurrence varies between geographic regions and poultry production systems and may influence parasite genetic diversity

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Multivariate analysis revealed comparable poultry clusters in north and south India.

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Eimeria species occurrence varied between system clusters.

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E. tenella occurrence across systems may underpin region-specific genetic diversity.

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E. necatrix was found to be more common in north than south India.

Coccidiosis is one of the biggest challenges faced by the global poultry industry. Recent studies have highlighted the ubiquitous distribution of all Eimeria species which can cause this disease in chickens, but intriguingly revealed a regional divide in genetic diversity and population structure for at least one species, Eimeria tenella. The drivers associated with such distinct geographic variation are unclear, but may impact on the occurrence and extent of resistance to anticoccidial drugs and future subunit vaccines. India is one of the largest poultry producers in the world and includes a transition between E. tenella populations defined by high and low genetic diversity. The aim of this study was to identify risk factors associated with the prevalence of Eimeria species defined by high and low pathogenicity in northern and southern states of India, and seek to understand factors which vary between the regions as possible drivers for differential genetic variation. Faecal samples and data relating to farm characteristics and management were collected from 107 farms from northern India and 133 farms from southern India. Faecal samples were analysed using microscopy and PCR to identify Eimeria occurrence. Multiple correspondence analysis was applied to transform correlated putative risk factors into a smaller number of synthetic uncorrelated factors. Hierarchical cluster analysis was used to identify poultry farm typologies, revealing three distinct clusters in the studied regions. The association between clusters and presence of Eimeria species was assessed by logistic regression. The study found that large-scale broiler farms in the north were at greatest risk of harbouring any Eimeria species and a larger proportion of such farms were positive for E. necatrix, the most pathogenic species. Comparison revealed a more even distribution for E. tenella across production systems in south India, but with a lower overall occurrence. Such a polarised region- and system-specific distribution may contribute to the different levels of genetic diversity observed previously in India and may influence parasite population structure across much of Asia and Africa. The findings of the study can be used to prioritise target farms to launch and optimise appropriate anticoccidial strategies for long-term control.

Decontamination of heavy metal laden sewage sludge with simultaneous solids reduction using thermophilic sulfur and ferrous oxidizing species

A possibility of using simultaneous sewage sludge digestion and metal leaching (SSDML) process at the thermophilic temperature to remove heavy metals and suspended solids from sewage sludge is explored in this study. Though thermophilic sludge digestion efficiently produces a stable sludge, its inability to remove heavy metals requires it to be used in tandem with another process like bioleaching for metal reduction. Previously, different temperature optima were known for the heterotrophs (thermophilic) responsible for the sludge digestion and the autotrophs involved in bioleaching (mesophilic), because of which the metal concentration was brought down separately in a different reactor. In our study, SSDML process was carried out at 50 °C (thermophilic) by using ferrous sulfate (batch-1) and sulfur (batch-2) as the energy source in two reactors. The concentration of volatile suspended solids reduced by >40% in both batches, while that of heavy metals zinc, copper, chromium, cadmium and nickel decreased by >50% in both batch-1 and batch-2. Lead got leached out only in batch-1. Using 16S rRNA gene-based PCR-denaturing gradient gel electrophoresis analysis, Alicyclobacillus tolerans was found to be the microorganism responsible for lowering the pH in both the reactors at thermophilic temperature. The indicator organism count was also below the maximum permissible limit making sludge suitable for agricultural use. Our results indicate that SSDML at thermophilic temperature can be effectively used for reduction of heavy metals and suspended solids from sewage sludge.

Genetic diversity within ITS-1 region of Eimeria species infecting chickens of north India

Coccidiosis, caused by apicomplexan parasites of the genus Eimeria, inflicts severe economic losses to the poultry industry around the globe. In the present study, ITS-1 based species specific nested PCR revealed prevalence of E. acervulina, E. brunetti, E. maxima, E. mitis, E. praecox, E. necatrix and E. tenella in 79.2%, 12.5%, 64.6%, 89.6%, 60.4%, 64.6% and 97.9% poultry farms of north India, respectively. The ITS-1 sequences of different Eimeria spp. from north India were generated and analyzed to establish their phylogenetic relationship. The sequence identity with available sequences ranged from 80 to 100% in E. tenella, 95 to 100% in E. acervulina, 64 to 97% in E. necatrix, 96 to 99% in E. brunetti and 97 to 98% in E. mitis. Only long ITS-1 sequences of E. maxima could be generated in the present study and it had 80-100% identity with published sequences. Two out of the four ITS-1 sequences of E. maxima had mismatches in the published nested primer sequences from Australia, while one sequence of E. necatrix had a mismatch near 3' end of both forward and reverse published nested primer sequences, warranting for the need of designing new set of degenerate primers for these two species of Eimeria. In the phylogenetic tree, all isolates of E. acervulina, E. brunetti, E. mitis, E. tenella and E. necatrix clustered in separate clades with high bootstrap value. E. maxima sequences of north Indian isolates grouped in a long form of E. maxima clade. Complete ITS-1 sequences of E. necatrix and E. mitis are reported for the first time from India. Further studies are required with more number of isolates to verify whether these differences are unique to geographical locations.

Nijmegen breakage syndrome detected by newborn screening for T cell receptor excision circles (TRECs)

Purpose

Severe combined immunodeficiency (SCID) encompasses a group of disorders characterized by reduced or absent T-cell number and function and identified by newborn screening utilizing T-cell receptor excision circles (TRECs). This screening has also identified infants with T lymphopenia who lack mutations in typical SCID genes. We report an infant with low TRECs and non-SCID T lymphopenia, who proved upon whole exome sequencing to have Nijmegen breakage syndrome (NBS).

Methods

Exome sequencing of DNA from the infant and his parents was performed. Genomic analysis revealed deleterious variants in the NBN gene. Confirmatory testing included Sanger sequencing and immunoblotting and radiosensitivity testing of patient lymphocytes.

Results

Two novel nonsense mutations in NBN were identified in genomic DNA from the family. Immunoblotting showed absence of nibrin protein. A colony survival assay demonstrated radiosensitivity comparable to patients with ataxia telangiectasia.

Conclusions

Although TREC screening was developed to identify newborns with SCID, it has also identified T lymphopenic disorders that may not otherwise be diagnosed until later in life. Timely identification of an infant with T lymphopenia allowed for prompt pursuit of underlying etiology, making possible a diagnosis of NBS, genetic counseling, and early intervention to minimize complications.

Impact of abrupt temperature increase on the performance of an anaerobic hybrid bioreactor and its intrinsic microbial community

This study aimed to analyse the effect of sudden temperature increases (in the range of 45-65 °C) on the performance and the microbial community structure of a hybrid anaerobic reactor. The reactor recovered with time after every temperature shock up to the operating temperature of 55 °C. At 55 °C, a 10 °C shock resulting in an operating temperature of 65 °C, deteriorated the reactor's performance. At this condition, both, the diversity and the relative abundance of methanogenic groups, especially of Methanosaetaceae, were significantly affected as observed by DGGE fingerprinting and quantitative PCR. In contrast, at lower temperatures (i.e., 45 and 55 °C), thermal shocks seemed to have less effect due to the presence and maintenance of thermophilic strains, which prevented system deterioration. At 65 °C, the absence of any acetoclastic methanogen is assumed to be the cause of system failure.

Genetic characterization and phylogenetic relationships based on 18S rRNA and ITS1 region of small form of canine Babesia spp. from India

Canine babesiosis is a vector borne disease caused by intra-erythrocytic apicomplexan parasites Babesia canis (large form) and Babesia gibsoni (small form), throughout the globe. Apart from few sporadic reports on the occurrence of B. gibsoni infection in dogs, no attempt has been made to characterize Babesia spp. of dogs in India. Fifteen canine blood samples, positive for small form of Babesia, collected from northern to eastern parts of India, were used for amplification of 18S rRNA gene (∼1665bp) of Babesia sp. and partial ITS1 region (∼254bp) of B. gibsoni Asian genotype. Cloning and sequencing of the amplified products of each sample was performed separately. Based on sequences and phylogenetic analysis of 18S rRNA and ITS1 sequences, 13 were considered to be B. gibsoni. These thirteen isolates shared high sequence identity with each other and with B. gibsoni Asian genotype. The other two isolates could not be assigned to any particular species because of the difference(s) in 18S rRNA sequence with B. gibsoni and closer identity with Babesiaoccultans and Babesiaorientalis. In the phylogenetic tree, all the isolates of B. gibsoni Asian genotype formed a separate major clade named as Babesia spp. sensu stricto clade with high bootstrap support. The two unnamed Babesia sp. (Malbazar and Ludhiana isolates) clustered close together with B. orientalis, Babesia sp. (Kashi 1 isolate) and B. occultans of bovines. It can be inferred from this study that 18S rRNA gene and ITS1 region are highly conserved among 13 B. gibsoni isolates from India. It is the maiden attempt of genetic characterization by sequencing of 18S rRNA gene and ITS1 region of B. gibsoni from India and is also the first record on the occurrence of an unknown Babesia sp. of dogs from south and south-east Asia.

Drying, burning and emission characteristics of beehive charcoal briquettes: an alternative household fuel of Eastern Himalayan Region

Beehive charcoal briquettes were produced from powdered charcoal in which soil was added as binder. It was found to be an eco-friendly, clean and economic alternative source of household fuel for the people of Eastern Himalayan Region. Experiments were conducted to determine natural drying behaviour, normalised burn rate, temperature profile and emission of CO, CO2, UBHC (unburnt hydrocarbons) and NO(x) of beehive briquettes prepared from 60:40; 50:50 and 40:60 ratios of charcoal and soil. It was observed that under natural drying conditions (temperature, humidity) briquettes took 433 hr to reach equilibrium moisture content of 5.56-10.29%. Page's model was found suitable to describe the drying characteristics of all three combinations. Normalised burn rate varied between 0.377-0.706% of initial mass min⁻¹. Total burning time of briquette ranged between 133-143 min. The peak temperature attained by briquettes ranged from 437 °C to 572 °C. All the briquette combinations were found suitable for cooking and space heating. Emission of CO, CO2, UBHC, NO and NO2 ranged between 68.4-107.2, 922-1359, 20.9-50.8, 0.19-0.29 and 0.34-0.64 g kg⁻¹, respectively which were less than firewood.

Comparative Studies on Performance Characteristics of CI Engine Fuelled with Neem Methyl Ester and Mahua Methyl Ester and Its Respective Blends with Diesel Fuel

In the present investigation, neem and mahua methyl ester were prepared by transesterification using potassium hydroxide as a catalyst and tested in 4-stroke single cylinder water cooled diesel engine. Tests were carried out at constant speed of 1500 rev/min at different brake mean effective pressures. A series of tests were conducted which worked at different brake mean effective pressures, OkPa, 1kPa, 2kPa, 3kPa, 4kPa, 5kPa, 6kPa and 6.5kPa. The performance and exhaust emission characteristics of the diesel engine were analyzed and compared with diesel fuel. Results showed that BTE of NME was comparable with diesel and it was noted that the BTE of N0100 is 63.11% higher than that of diesel at part load whereas it reduces 11.2% with diesel fuel at full load. In case of full load, NME showed decreasing trend with diesel fuel. BTE of diesel was 15.37% and 36.89% at part load and full load respectively. The observation indicated that BTE for MME 100 was slightly higher than diesel at part loads. The specific fuel consumption (SFC) was more for almost all blends at all loads, compared to diesel. At part load, the EGT of MME and its blends were showing similar trend to diesel fuel and at full load, the exhaust gas temperature of MME and blends were higher than diesel. Based on this study, NME could be a substitute for diesel fuel in diesel engine.

Detection of beet yellows virus by RT-PCR and immunocapture RT-PCR in Tetragonia expansa and Beta vulgaris

Two sensitive methods, RT-PCR with phenol-extracted RNA or Triton X-100-released RNA and immunocapture RT-PCR (IR-RT-PCR) were used for the detection of Beet yellows virus (BYV) in young and old leaves of Tetragonia expansa and sugar beet (Beta vulgaris) and in sugar beet roots. Four oligonucleotide primer pairs proved suitable for the detection of BYV. The release of BYV RNA with Triton X-100 was shown to be a very effective and easy as compared to isolation of total RNA by phenol extraction with the same or higher sensitivity of subsequent PCR. Using the Triton X-100 release of RNA and IC-RT-PCR the sensitivity of detection was so high that pg amounts of BYV RNA occurring in dilutions up to 10(-6) of saps from young Tetragonia and sugar beet leaves could be detected.

Perturbative study of classical Ablowitz-Ladik type soliton dynamics in relation to energy transport in alpha-helical proteins

Classical Ablowitz-Ladik type soliton dynamics from three closely related classical nonlinear equations is studied using a perturbative method. Model nonintegrable equations are derived by assuming nearest neighbor hopping of an exciton(vibron) in the presence of a full exciton(vibron)-phonon interaction in soft molecular chains in general and spines of alpha-helices in particular. In all cases, both trapped and moving solitons are found implying activation energy barrier for propagating solitons. Analysis further shows that staggered and nearly staggered trapped solitons will have a negative effective mass. In some models the exciton(vibron)-phonon coupling affects the hopping. For these models, when the conservation of probability is taken into account, only propagating solitons with a broad profile are found to be acceptable solutions. Of course, for the soliton to be a physically meaningful entity, total nonlinear coupling strength should exceed a critical value. On the basis of the result, a plausible modification in the mechanism for biological energy transport involving conformational change in alpha-helix is proposed. Future directions of the work are also mentioned.

Dependence of thermodynamic efficiency of proton pumps on frequency of oscillatory concentration of ATP

In order to evaluate the utilization of variable ATP concentration produced by an oscillatory reaction (as in anaerobic glycolysis), we analyze the thermodynamic efficiency of power output of a cyclic, ATP-driven proton pump found in the plasma membrane of plant cells. The model used includes the coupling of potassium and calcium ion transport. Oscillations in the concentration of ATP can lead to either increases or decreases in efficiency compared to that at constant ATP concentration, with corresponding decreases and increases in dissipation in the irreversible processes of the proton pump, depending on the frequency of the oscillations. Variations of imposed frequencies induce, in the periodic response, variations of phase shifts between the components of the total membrane current, which consist of the pump's proton current and the currents of potassium and calcium ions. Increases in efficiency are attained when the phase shifts are such that maxima (or minima) in the proton pump current and membrane potential occur simultaneously.