Host-linked soil viral ecology along a permafrost thaw gradient

Climate change threatens to release abundant carbon that is sequestered at high latitudes, but the constraints on microbial metabolisms that mediate the release of methane and carbon dioxide are poorly understood1,2,3,4,5,6,7. The role of viruses, which are known to affect microbial dynamics, metabolism and biogeochemistry in the oceans8,9,10, remains largely unexplored in soil. Here, we aimed to investigate how viruses influence microbial ecology and carbon metabolism in peatland soils along a permafrost thaw gradient in Sweden. We recovered 1,907 viral populations (genomes and large genome fragments) from 197 bulk soil and size-fractionated metagenomes, 58% of which were detected in metatranscriptomes and presumed to be active. In silico predictions linked 35% of the viruses to microbial host populations, highlighting likely viral predators of key carbon-cycling microorganisms, including methanogens and methanotrophs. Lineage-specific virus/host ratios varied, suggesting that viral infection dynamics may differentially impact microbial responses to a changing climate. Virus-encoded glycoside hydrolases, including an endomannanase with confirmed functional activity, indicated that viruses influence complex carbon degradation and that viral abundances were significant predictors of methane dynamics. These findings suggest that viruses may impact ecosystem function in climate-critical, terrestrial habitats and identify multiple potential viral contributions to soil carbon cycling

HCV genome-wide genetic analyses in context of disease progression and hepatocellular carcinoma

<div><p>Hepatitis C virus (HCV) is a major cause of hepatitis and hepatocellular carcinoma (HCC) world-wide. Most HCV patients have relatively stable disease, but approximately 25% have progressive disease that often terminates in liver failure or HCC. HCV is highly variable genetically, with seven genotypes and multiple subtypes per genotype. This variation affects HCV’s sensitivity to antiviral therapy and has been implicated to contribute to differences in disease. We sequenced the complete viral coding capacity for 107 HCV genotype 1 isolates to determine whether genetic variation between independent HCV isolates is associated with the rate of disease progression or development of HCC. Consensus sequences were determined by sequencing RT-PCR products from serum or plasma. Positions of amino acid conservation, amino acid diversity patterns, selection pressures, and genome-wide patterns of amino acid covariance were assessed in context of the clinical phenotypes. A few positions were found where the amino acid distributions or degree of positive selection differed between in the HCC and cirrhotic sequences. All other assessments of viral genetic variation and HCC failed to yield significant associations. Sequences from patients with slow disease progression were under a greater degree of positive selection than sequences from rapid progressors, but all other analyses comparing HCV from rapid and slow disease progressors were statistically insignificant. The failure to observe distinct sequence differences associated with disease progression or HCC employing methods that previously revealed strong associations with the outcome of interferon α-based therapy implies that variable ability of HCV to modulate interferon responses is not a dominant cause for differential pathology among HCV patients. This lack of significant associations also implies that host and/or environmental factors are the major causes of differential disease presentation in HCV patients.</p></div

Decay kinetics for <i>gm-csf</i> and <i>ompA-gm-csf</i> transcripts expressed from <i>P_tac</i>.

<p><b>A.</b><i>gm-csf</i> and <i>ompA</i>-<i>gm-csf</i> genes was achieved by the inducer wash-out. <b>B.</b> Rifampicin was used for transcription inhibition. For both A and B: the larger plot shows <i>gm-csf</i> and <i>ompA</i>-<i>gm-csf</i> transcript amounts at time point zero arbitrary set to one. In the upper right corner the decay curves are represented with all values relative to <i>gm-csf</i> transcript level at time point zero (arbitrarily set to one). Solid lines represent the best fit to the data calculated according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066429#pone.0066429.e008" target="_blank">Eqn 4</a> (Material and Methods). Error bars show the deviation between at least three technical recurrences. RQ: relative quantification, AU: arbitrary units.</p

PCR and qRT PCR primers.

1<p>Km^r: kanamycin resistance gene.</p

Kinetics of <i>gm-csf</i> and <i>ompA-gm-csf</i> transcript decay, expressed from <b><i>Pm</i></b>.

<p>The larger plot shows decay kinetics with both <i>gm-csf</i> and <i>ompA-gm-csf</i> transcript levels at time point zero arbitrarily set to one. The smaller plot shown in the upper right corner represents all transcript data relative to the value of <i>gm-csf</i> at time point zero (arbitrarily set to one). Solid lines represent the best fit to the data calculated according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066429#pone.0066429.e008" target="_blank">Eqn 4</a> (Material and Methods). Error bars show the deviation between two biological recurrences. RQ: relative quantification, AU: arbitrary units.</p

Accumulation and decay of <i>IL1RN_S</i> transcripts.

<p>The respective genes were expressed from either the T7 or the <i>Pm</i> promoter. <b>A.</b> For both T7 and <i>Pm</i> promoter generated transcripts the amounts are given relative to the value at time point 60 minutes (arbitrarily set to one). The amounts of <i>IL1RN_S</i> transcript generated through the T7 system was increased about 10-fold compared to those of the <i>Pm</i> system (after 60 minutes). Solid lines represent the best fit to the data, calculated according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066429#pone.0066429.e005" target="_blank">Eqn 2</a> (Material and Methods). In case of the T7 system, only time points 15, 20, 40 and 60 minutes were included for the generation of the transcript accumulation curve. <b>B.</b> Parallel determination of decay for <i>Pm</i> and T7 generated transcripts. For both systems all transcript amounts are presented relative to <i>IL1RN_S</i> transcript level at time zero (arbitrarily set to one). Error bars show the deviation between three biological recurrences. Solid lines represent the best fit to the data calculated according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066429#pone.0066429.e008" target="_blank">Eqn 4</a> (Material and Methods). RQ: relative quantification, AU: arbitrary units.</p

Decay kinetics of two <i>bla</i> transcripts with different 5′ UTR sequences.

<p>The respective genes were expressed from <i>Pm</i> and contained the DNA sequence of either the wild type 5′-UTR or the LV-2 variant. The larger plot describes decay kinetics where the level of the two <i>bla</i> transcripts is arbitrary set to one at time point zero. In the upper right corner the transcript amounts are represented with all values relative to the wild type 5′-UTR; arbitrary set to one at time point zero. Solid lines represent the best fit to the data calculated according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0066429#pone.0066429.e008" target="_blank">Eqn 4</a> (Material and Methods). Error bars show the deviation between three biological recurrences. RQ: relative quantification, AU: arbitrary units.</p

The <i>bla</i> gene expression in DH5α (pBSP1bla) and DH5α (pBSP1bla-C19).

<p><b>A.</b> Determination of accumulated transcript levels and enzymatic activity of β-lactamase, with all values relative to the pBSP1bla level (arbitrarily set to one). <b>B.</b> Total β-lactamase production of DH5α (pBSP1bla) (denoted as wt) and DH5α (pBSP1bla-C19) (denoted as C19) was visualized by Western blotting and total cell samples were loaded in dilution series of decreasing total protein amount (µg). The size of the mature β-lactamase protein is approximately 29 kDa.</p

Interspecies cross-feeding orchestrates carbon degradation in the rumen ecosystem.

Because of their agricultural value, there is a great body of research dedicated to understanding the microorganisms responsible for rumen carbon degradation. However, we lack a holistic view of the microbial food web responsible for carbon processing in this ecosystem. Here, we sampled rumen-fistulated moose, allowing access to rumen microbial communities actively degrading woody plant biomass in real time. We resolved 1,193 viral contigs and 77 unique, near-complete microbial metagenome-assembled genomes, many of which lacked previous metabolic insights. Plant-derived metabolites were measured with NMR and carbohydrate microarrays to quantify the carbon nutrient landscape. Network analyses directly linked measured metabolites to expressed proteins from these unique metagenome-assembled genomes, revealing a genome-resolved three-tiered carbohydrate-fuelled trophic system. This provided a glimpse into microbial specialization into functional guilds defined by specific metabolites. To validate our proteomic inferences, the catalytic activity of a polysaccharide utilization locus from a highly connected metabolic hub genome was confirmed using heterologous gene expression. Viral detected proteins and linkages to microbial hosts demonstrated that phage are active controllers of rumen ecosystem function. Our findings elucidate the microbial and viral members, as well as their metabolic interdependencies, that support in situ carbon degradation in the rumen ecosystem