Harri Lähdesmäki

The Dynamics of the Human Infant Gut Microbiome in Development and in Progression toward Type 1 Diabetes

Colonization of the fetal and infant gut microbiome results in dynamic changes in diversity, which can impact disease susceptibility. To examine the relationship between human gut microbiome dynamics throughout infancy and type 1 diabetes (T1D), we examined a cohort of 33 infants genetically predisposed to T1D. Modeling trajectories of microbial abundances through infancy revealed a subset of microbial relationships shared across most subjects. Although strain composition of a given species was highly variable between individuals, it was stable within individuals throughout infancy. Metabolic composition and metabolic pathway abundance remained constant across time. A marked drop in alpha-diversity was observed in T1D progressors in the time window between seroconversion and T1D diagnosis, accompanied by spikes in inflammation-favoring organisms, gene functions, and serum and stool metabolites. This work identifies trends in the development of the human infant gut microbiome along with specific alterations that precede T1D onset and distinguish T1D progressors from nonprogressors.

On Learning Gene Regulatory Networks Under the Boolean Network Model

Boolean networks are a popular model class for capturing the interactions of genes and global dynamical behavior of genetic regulatory networks. Recently, a significant amount of attention has been focused on the inference or identification of the model structure from gene expression data. We consider the Consistency as well as Best-Fit Extension problems in the context of inferring the networks from data. The latter approach is especially useful in situations when gene expression measurements are noisy and may lead to inconsistent observations. We propose simple efficient algorithms that can be used to answer the Consistency Problem and find one or all consistent Boolean networks relative to the given examples. The same method is extended to learning gene regulatory networks under the Best-Fit Extension paradigm. We also introduce a simple and fast way of finding all Boolean networks having limited error size in the Best-Fit Extension Problem setting. We apply the inference methods to a real gene expression data set and present the results for a selected set of genes.

Evaluation of methods for modeling transcription factor sequence specificity

Genomic analyses often involve scanning for potential transcription factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to model and learn a proteins DNA-binding specificity, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For nine TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro–derived motifs performed similarly to motifs derived from the in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices trained by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases (<10% of the TFs examined here). In addition, the best-performing motifs typically have relatively low information content, consistent with widespread degeneracy in eukaryotic TF sequence preferences.

Modulation of TET2 expression and 5-methylcytosine oxidation by the CXXC domain protein IDAX

TET (ten-eleven-translocation) proteins are Fe(ii)- and α-ketoglutarate-dependent dioxygenases that modify the methylation status of DNA by successively oxidizing 5-methylcytosine to 5-hydroxymethylcytosine, 5-formylcytosine and 5-carboxycytosine, potential intermediates in the active erasure of DNA-methylation marks. Here we show that IDAX (also known as CXXC4), a reported inhibitor of Wnt signalling that has been implicated in malignant renal cell carcinoma and colonic villous adenoma, regulates TET2 protein expression. IDAX was originally encoded within an ancestral TET2 gene that underwent a chromosomal gene inversion during evolution, thus separating the TET2 CXXC domain from the catalytic domain. The IDAX CXXC domain binds DNA sequences containing unmethylated CpG dinucleotides, localizes to promoters and CpG islands in genomic DNA and interacts directly with the catalytic domain of TET2. Unexpectedly, IDAX expression results in caspase activation and TET2 protein downregulation, in a manner that depends on DNA binding through the IDAX CXXC domain, suggesting that IDAX recruits TET2 to DNA before degradation. IDAX depletion prevents TET2 downregulation in differentiating mouse embryonic stem cells, and short hairpin RNA against IDAX increases TET2 protein expression in the human monocytic cell line U937. Notably, we find that the expression and activity of TET3 is also regulated through its CXXC domain. Taken together, these results establish the separate and linked CXXC domains of TET2 and TET3, respectively, as previously unknown regulators of caspase activation and TET enzymatic activity.

Differential gene and protein expression in primary breast malignancies and their lymph node metastases as revealed by combined cDNA microarray and tissue microarray analysis.

Metastatic disease is a major adverse prognostic factor in breast carcinoma. Lymph node metastases often represent the first step in the metastatic process.

Androgen-regulated miR-32 targets BTG2 and is overexpressed in castration-resistant prostate cancer

The androgen receptor (AR) signaling pathway is involved in the emergence of castration-resistant prostate cancer (CRPC). Here, we identified several androgen-regulated microRNAs (miRNAs) that may contribute to the development of CRPC. Seven miRNAs, miR-21, miR-32, miR-99a, miR-99b, miR-148a, miR-221 and miR-590-5p, were found to be differentially expressed in CRPC compared with benign prostate hyperplasia (BPH) according to microarray analyses. Significant growth advantage for LNCaP cells transfected with pre-miR-32 and pre-miR-148a was found. miR-32 was demonstrated to reduce apoptosis, whereas miR-148a enhanced proliferation. Androgen regulation of miR-32 and miR-148a was confirmed by androgen stimulation of the LNCaP cells followed by expression analyses. The AR-binding sites in proximity of these miRNAs were demonstrated with chromatin immunoprecipitation (ChIP). To identify target genes for the miRNAs, mRNA microarray analyses were performed with LNCaP cells transfected with pre-miR-32 and pre-miR-148a. Expression of BTG2 and PIK3IP1 was reduced in the cells transfected with pre-miR-32 and pre-miR-148a, respectively. Also, the protein expression was reduced according to western blot analysis. BTG2 and PIK3IP1 were confirmed to be targets by 3′UTR-luciferase assays. Finally, immunostainings showed a statistically significant (P<0.0001) reduction of BTG2 protein in CRPCs compared with untreated prostate cancer (PC). The lack of BTG2 staining was also associated (P<0.01) with a short progression-free time in patients who underwent prostatectomy. In conclusion, androgen-regulated miR-32 is overexpressed in CRPC, leading to reduced expression of BTG2. Thus, miR-32 is a potential marker for aggressive disease and is a putative drug target in PC.

The role of certain Post classes in Boolean network models of genetic networks

A topic of great interest and debate concerns the source of order and remarkable robustness observed in genetic regulatory networks. The study of the generic properties of Boolean networks has proven to be useful for gaining insight into such phenomena. The main focus, as regards ordered behavior in networks, has been on canalizing functions, internal homogeneity or bias, and network connectivity. Here we examine the role that certain classes of Boolean functions that are closed under composition play in the emergence of order in Boolean networks. The closure property implies that any gene at any number of steps in the future is guaranteed to be governed by a function from the same class. By means of Derrida curves on random Boolean networks and percolation simulations on square lattices, we demonstrate that networks constructed from functions belonging to these classes have a tendency toward ordered behavior. Thus they are not overly sensitive to initial conditions, and damage does not readily spread throughout the network. In addition, the considered classes are significantly larger than the class of canalizing functions as the connectivity increases. The functions in these classes exhibit the same kind of preference toward biased functions as do canalizing functions, meaning that functions from this class are likely to be biased. Finally, functions from this class have a natural way of ensuring robustness against noise and perturbations, thus representing plausible evolutionarily selected candidates for regulatory rules in genetic networks.

Robust detection of periodic time series measured from biological systems

BackgroundPeriodic phenomena are widespread in biology. The problem of finding periodicity in biological time series can be viewed as a multiple hypothesis testing of the spectral content of a given time series. The exact noise characteristics are unknown in many bioinformatics applications. Furthermore, the observed time series can exhibit other non-idealities, such as outliers, short length and distortion from the original wave form. Hence, the computational methods should preferably be robust against such anomalies in the data.ResultsWe propose a general-purpose robust testing procedure for finding periodic sequences in multiple time series data. The proposed method is based on a robust spectral estimator which is incorporated into the hypothesis testing framework using a so-called g-statistic together with correction for multiple testing. This results in a robust testing procedure which is insensitive to heavy contamination of outliers, missing-values, short time series, nonlinear distortions, and is completely insensitive to any monotone nonlinear distortions. The performance of the methods is evaluated by performing extensive simulations. In addition, we compare the proposed method with another recent statistical signal detection estimator that uses Fishers test, based on the Gaussian noise assumption. The results demonstrate that the proposed robust method provides remarkably better robustness properties. Moreover, the performance of the proposed method is preferable also in the standard Gaussian case. We validate the performance of the proposed method on real data on which the method performs very favorably.ConclusionAs the time series measured from biological systems are usually short and prone to contain different kinds of non-idealities, we are very optimistic about the multitude of possible applications for our proposed robust statistical periodicity detection method.AvailabilityThe presented methods have been implemented in Matlab and in R. Codes are available on request. Supplementary material is available at: http://www.cs.tut.fi/sgn/csb/robustperiodic/.

Relationships between probabilistic Boolean networks and dynamic Bayesian networks as models of gene regulatory networks

A significant amount of attention has recently been focused on modeling of gene regulatory networks. Two frequently used large-scale modeling frameworks are Bayesian networks (BNs) and Boolean networks, the latter one being a special case of its recent stochastic extension, probabilistic Boolean networks (PBNs). PBN is a promising model class that generalizes the standard rule-based interactions of Boolean networks into the stochastic setting. Dynamic Bayesian networks (DBNs) is a general and versatile model class that is able to represent complex temporal stochastic processes and has also been proposed as a model for gene regulatory systems. In this paper, we concentrate on these two model classes and demonstrate that PBNs and a certain subclass of DBNs can represent the same joint probability distribution over their common variables. The major benefit of introducing the relationships between the models is that it opens up the possibility of applying the standard tools of DBNs to PBNs and vice versa. Hence, the standard learning tools of DBNs can be applied in the context of PBNs, and the inference methods give a natural way of handling the missing values in PBNs which are often present in gene expression measurements. Conversely, the tools for controlling the stationary behavior of the networks, tools for projecting networks onto sub-networks, and efficient learning schemes can be used for DBNs. In other words, the introduced relationships between the models extend the collection of analysis tools for both model classes.

The transcription factor NFAT promotes exhaustion of activated CD8⁺ T cells.

During persistent antigen stimulation, CD8(+) T cells show a gradual decrease in effector function, referred to as exhaustion, which impairs responses in the setting of tumors and infections. Here we demonstrate that the transcription factor NFAT controls the program of T cell exhaustion. When expressed in cells, an engineered form of NFAT1 unable to interact with AP-1 transcription factors diminished T cell receptor (TCR) signaling, increased the expression of inhibitory cell surface receptors, and interfered with the ability of CD8(+) T cells to protect against Listeria infection and attenuate tumor growth in vivo. We defined the genomic regions occupied by endogenous and engineered NFAT1 in primary CD8(+) T cells and showed that genes directly induced by the engineered NFAT1 overlapped with genes expressed in exhausted CD8(+) T cells in vivo. Our data show that NFAT promotes T cell anergy and exhaustion by binding at sites that do not require cooperation with AP-1.