Tag: antibiotic

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Breakthrough advances in 2018 so far: flu, germs, and cancer

2018 medicine breakthrough review!

So far this year has seen some pretty important research breakthrough advances in several key areas of health and medicine.  I want to briefly describe some of what we’ve seen in just the first few months of 2018.

Flu

A pharmaceutical company in Japan has released phase 3 trial results showing that its drug, Xofluza, can effectively kill the virus in just 24 hours in infected humans.  And it can do this with just one single dose, compared to a 10-dose, three day regimen of Tamiflu. The drug works by inhibiting an endonuclease needed for replication of the virus.

Germs

It is common knowledge that antibiotics are over-prescribed and over-used.  This fact has led to the rise of MRSA and other resistant bacteria which threaten human health.  Although it is thought that bacteria could be a source of novel antibiotics since they are in constant chemical warfare with each other, most bacteria aren’t culture-friendly in the lab and so researchers haven’t been looking at them for leads.  Until now.

Malacidin drugs kill multi-drug resistant S. Aureus in tests on rats.

By adopting whole genome sequencing approaches to soil bacterial diversity, researchers were able to screen for gene clusters associated with calcium-binding motifs known for antibiotic activity.   The result was the discovery of a novel class of lipo-peptides, called malacidins A and B.  They showed potent activity against MRSA in skin infection models in rats.

The researchers estimate that 99% of bacterial natural-product antibiotic compounds remain unexplored at present.

Cancer

2017 and 2018 have seen some major advances with cancer treatment.   It seems that the field is moving away from the focus on small-molecule drugs towards harnessing the patient’s own immune system to attack cancer.  The CAR-T therapies for pediatric leukemia appear extremely promising.  These kinds of therapies are now in trials for a wide range of blood and solid tumors.

A great summary of the advances being made is available here from the Fred Hutchinson Cancer Research Center.   Here is how Dr. Gilliland, President of Fred Hutch, begins his review of the advances:

I’ve gone on record to say that by 2025, cancer researchers will have developed curative therapeutic approaches for most if not all cancers.

I took some flak for putting that stake in the ground. But we in the cancer research field are making incredible strides toward better and safer, potentially curative treatments for cancer, and I’m excited for what’s next. I believe that we must set a high bar, execute and implement — that there should be no excuses for not advancing the field at that pace.

This is a stunning statement on its own;  but made even more so because it is usually the scientists in the day-to-day trenches of research who are themselves the most pessimistic about the possibility of rapid advances.

Additionally, an important paper came out recently proposing a novel paradigm for understanding and modeling cancer incidence with age.  For a long time the dominant model has been the “two-hit” hypothesis which predicts that clinically-observable cancers arise when a cell acquires sufficient mutations in tumor-suppressor genes to become a tumor.

This paper challenges that notion and shows that a model of thymic function decline (the thymus produces T-cells) over time better describes the incidence of cancers with age.   This model better fits the data and leads to the conclusion that cancers are continually arising in our bodies, but it is our properly functioning immune system that roots them out and prevents clinical disease from emerging.  This model also helps explain why novel cancer immunotherapies are so potent and why focus has shifted to supporting and activating T-cells.

Declining T cell production leads to increasing disease incidence with age.

 

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Understanding bacterial stress response networks: high fitness vs. high expression genes

Are differentially expressed (DE) genes also phenotypically important?

A new paper in Cell Reports utilizes RNA-seq and Tn-seq (the “tn” in tn-seq stands for transposon) to map the transcriptional and fitness changes in bacterial gene networks in response to stressors, like nutrient depletion and antibiotics.

The transcriptional response measures changes in gene expression as measured by RNA-seq.  The fitness or phenotypic response describes the importance of each gene to the response.  This is measured by a different assay, Tn-seq, which takes advantage of transposon insertion to selectively inactivate genes in the bacterial genome.  Those genes that are depleted in the stressor condition are determined to be “high fitness” (owing to the fact that the bacteria without those genes died under stress).

First, before even considering DE genes, they found that there is no correlation between a gene’s transcriptional abundance (not fold change) and it’s fitness.   While most high-fitness genes were also high abundance, many more high-abundance genes were not high-fitness.  Thus, there is no useful relationship between a gene’s abundance and fitness.

Superficially, however, one might expect that genes that show large changes in abundance (i.e., large DE) in response to stressors would also be critical for the phenotypic expression of the bacteria’s stress response.   That is, those genes with high differential expression would confer high fitness on the cell.

Testing the DE / high fitness relationship

As it turns out, little is actually known about this, and in this paper, Opijnen, et. al., set out to test the idea to determine if in fact high DE genes are also high fitness.

The researchers looked at comparing differential expression in response to a reduced nutrient environment (a type of minimal media) and an antibiotic stress versus high-fitness genes.  They found no correlation:

Expression vs. fitness changes
Expression vs. fitness changes for various strains of S. pneumoniae during nutrient depletion (left) and antibiotic stress (right).

You can see from the figure that high fitness genes (those on the far left of the x-axis), are not correlated to high DE genes.  There are no genes in the upper-left quadrant of either plot, showing that there is no correlation between fitness and high DE in response to either nutrient or antibiotic stress.

Gene networks co-localize high DE and high fitness genes

Even though the authors found no correlation between DE and fitness changes for individual genes, they took the next step and constructed a metabolic gene network for the S. pneumoniae bacteria.  Mapping the DE and fitness changes onto this network revealed a key finding: the high DE genes co-localize in pathways with the high fitness genes.  That is, a biochemical pathway might have some members that are high DE, and others that are high fitness.  An example of this is the shikimate pathway shown below:

Changes in gene expression and fitness in the Trp biosynthesis pathway. The dashed line is the branch point into Trp synthesis.

The first half of the pathway consists of six genes with significant fitness changes (red boxes) in a row.  The next seven genes, from the Trp branchpoint (blue dashed line) are not high-fitness, but do show high DE expression, with four reaching statistical significance.   It is not really understood why this happens, but the authors theorize that having the bottom half of the pathway under transcriptional control allows the bacterial to control flux into Trp synthesis and other AA sub-pathways while always maintaining a stable supply of the starting point intermediates (the product of SP1374) through reversible, end product-regulated biosynthesis.

Transcriptomic data should not be used as a surrogate for functional importance

The authors point out that the reliance on trancriptonal abundance changes as markers for functional importance in bacteria, particularly in drug discovery efforts, may be misguided and need to be revisited in light of this and other studies.   They also point on that the response to an “orderly” stressor (like nutrient depletion) for which the bacterium is evolved, is likely to be much more clearly defined on a network basis.  While the response to a disorderly stressor (a novel antibiotic, for example) may provoke a disorderly transcriptional and fitnress response that can’t easily be interpreted from network analysis.  This has important implications for the design of next-generation antibiotics.